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+{"cells":[{"cell_type":"markdown","metadata":{"id":"m7veV0NV5CV4"},"source":["## Obesity Dataset:"]},{"cell_type":"markdown","metadata":{"id":"K7m-Jmrf5CV7"},"source":["- The data consist of the estimation of **obesity levels** in people from the countries of Mexico, Peru and Colombia, with ages between 14 and 61 and diverse eating habits and physical condition , data was collected using a web platform with a survey where anonymous users answered each question, then the information was processed obtaining **17 attributes and 2111 records**."]},{"cell_type":"markdown","metadata":{"id":"AMTaGYem5CV8"},"source":["- **The attributes related with eating habits are:**\n","1. Frequent consumption of high caloric food (FAVC).\n","2. Frequency of consumption of vegetables (FCVC).\n","3. Number of main meals (NCP).\n","4. Consumption of food between meals (CAEC)\n","5. Consumption of water daily (CH20)\n","6. Consumption of alcohol (CALC).\n","\n","- **The attributes related with the physical condition are:**\n","1. Calories consumption monitoring (SCC)\n","2. Physical activity frequency (FAF)\n","3. Time using technology devices (TUE)\n","4. Transportation used (MTRANS)"]},{"cell_type":"markdown","metadata":{"id":"sA9-aoIf5CV9"},"source":["|  Column  |             Meaning       |\n","|----------|---------------------------|\n","|  Gender  |      Gender (1=Female or 0=Male)       |\n","|  Age  |         ages between 14 and 61        |\n","|  Height  |             Height       |\n","|  Weight  |             Weight       |\n","|  family_history_with_overweight  |             family member suffered or suffers from overweight (1=Yes, 0=No)    |\n","|  FAVC  |              Frequent consumption of high caloric food (0=Yes, 1=No)       |\n","|  FCVC  |            Frequency of consumption of vegetables (1, 2, or 3)      |\n","|  NCP  |             Number of main meals (1, 2, 3, or 4)      |\n","|  CAEC  |          Consumption of food between meals (1=No, 2=Sometimes, 3=Frequently, 4=Always)      |\n","|  SMOKE  |         Smoker or not (0=Yes, 1=No)      |\n","|  CH2O  |        Consumption of water daily       |\n","|  SCC  |         Calories consumption monitoring (0=Yes, 1=No)      |\n","|  FAF  |         Physical activity frequency (0, 1, 2, or 3)       |\n","|  TUE  |        Time using technology devices (0, 1, or 2)      |\n","|  CALC  |       Consumption of alcohol (1=No, 2=Sometimes, 3=Frequently or 4=Always)      |\n","|  MTRANS  |          Transportation used (automobile, motorbike, bike, public transportation, or walking)     |\n","|  NObeyesdad  |             Obesity level deducted (1=Insufficient Weight, 2=Normal Weight, 3=Overweight Level I, 4=Overweight Level II, 5=Obesity Type I, 6=Obesity Type_II, 7=Obesity Type III)    |"]},{"cell_type":"markdown","metadata":{"id":"waL6UCc55CV9"},"source":["**Data Label**\n","\n","- NObesity values are:\n","\n","• Underweight Less than 18.5                                        \n","• Normal 18.5 to 24.9                                      \n","• Overweight 25.0 to 29.9                                      \n","• Obesity I 30.0 to 34.9                                          \n","• Obesity II 35.0 to 39.9                                        \n","• Obesity III Higher than 40"]},{"cell_type":"markdown","metadata":{"id":"47hG3mRT5CV-"},"source":["## Import Libraries:"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":27404,"status":"ok","timestamp":1710711735671,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"p8U6PbAc6lb0","outputId":"92bc754e-068f-454a-a988-cac34915e77a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting catboost\n","  Downloading catboost-1.2.3-cp310-cp310-manylinux2014_x86_64.whl (98.5 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.5/98.5 MB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.1)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n","Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.25.2)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.5.3)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.11.4)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.15.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost) (1.16.0)\n","Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2023.4)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.2.0)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.49.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.5)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (24.0)\n","Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (9.4.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.1.2)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (8.2.3)\n","Installing collected packages: catboost\n","Successfully installed catboost-1.2.3\n"]}],"source":["!pip install catboost"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17},"executionInfo":{"elapsed":1852,"status":"ok","timestamp":1710712702379,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"5UWxS_3m5CV-","outputId":"4e276597-2889-4405-fa6f-8cbcabba59f2"},"outputs":[{"output_type":"display_data","data":{"text/html":["        <script type=\"text/javascript\">\n","        window.PlotlyConfig = {MathJaxConfig: 'local'};\n","        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n","        if (typeof require !== 'undefined') {\n","        require.undef(\"plotly\");\n","        requirejs.config({\n","            paths: {\n","                'plotly': ['https://cdn.plot.ly/plotly-2.24.1.min']\n","            }\n","        });\n","        require(['plotly'], function(Plotly) {\n","            window._Plotly = Plotly;\n","        });\n","        }\n","        </script>\n","        "]},"metadata":{}}],"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import LabelEncoder\n","from sklearn.preprocessing import OrdinalEncoder\n","from sklearn.preprocessing import StandardScaler\n","from imblearn.over_sampling import SMOTE\n","\n","import plotly.express as px\n","from plotly.offline import init_notebook_mode, iplot\n","init_notebook_mode(connected=True)\n","import plotly.graph_objects as go\n","\n","from sklearn.svm import SVC\n","from sklearn.tree import plot_tree\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.neighbors import KNeighborsClassifier\n","from xgboost import XGBClassifier\n","from lightgbm import LGBMClassifier\n","from catboost import CatBoostClassifier\n","from sklearn.ensemble import GradientBoostingClassifier\n","from sklearn.ensemble import VotingClassifier\n","from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n","from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n","from sklearn.model_selection import cross_val_score\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer\n","\n","import warnings\n","warnings.filterwarnings('ignore')"]},{"cell_type":"markdown","metadata":{"id":"kPUUABk45CWA"},"source":["## Read Data:"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":410,"status":"ok","timestamp":1710711773868,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"l9y9HKPS5CWA"},"outputs":[],"source":["train = pd.read_csv('train.csv')\n","test = pd.read_csv('test.csv')"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226},"executionInfo":{"elapsed":355,"status":"ok","timestamp":1710711774571,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"GMRpitGJ5CWA","outputId":"43a9c285-ee8f-4455-805f-75bcfd09ba90"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   id  Gender        Age    Height      Weight family_history_with_overweight  \\\n","0   0    Male  24.443011  1.699998   81.669950                            yes   \n","1   1  Female  18.000000  1.560000   57.000000                            yes   \n","2   2  Female  18.000000  1.711460   50.165754                            yes   \n","3   3  Female  20.952737  1.710730  131.274851                            yes   \n","4   4    Male  31.641081  1.914186   93.798055                            yes   \n","\n","  FAVC      FCVC       NCP        CAEC SMOKE      CH2O SCC       FAF  \\\n","0  yes  2.000000  2.983297   Sometimes    no  2.763573  no  0.000000   \n","1  yes  2.000000  3.000000  Frequently    no  2.000000  no  1.000000   \n","2  yes  1.880534  1.411685   Sometimes    no  1.910378  no  0.866045   \n","3  yes  3.000000  3.000000   Sometimes    no  1.674061  no  1.467863   \n","4  yes  2.679664  1.971472   Sometimes    no  1.979848  no  1.967973   \n","\n","        TUE       CALC                 MTRANS           NObeyesdad  \n","0  0.976473  Sometimes  Public_Transportation  Overweight_Level_II  \n","1  1.000000         no             Automobile        Normal_Weight  \n","2  1.673584         no  Public_Transportation  Insufficient_Weight  \n","3  0.780199  Sometimes  Public_Transportation     Obesity_Type_III  \n","4  0.931721  Sometimes  Public_Transportation  Overweight_Level_II  "],"text/html":["\n","  <div id=\"df-ed499657-92f1-44e8-92a5-afab9a961fc3\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>Gender</th>\n","      <th>Age</th>\n","      <th>Height</th>\n","      <th>Weight</th>\n","      <th>family_history_with_overweight</th>\n","      <th>FAVC</th>\n","      <th>FCVC</th>\n","      <th>NCP</th>\n","      <th>CAEC</th>\n","      <th>SMOKE</th>\n","      <th>CH2O</th>\n","      <th>SCC</th>\n","      <th>FAF</th>\n","      <th>TUE</th>\n","      <th>CALC</th>\n","      <th>MTRANS</th>\n","      <th>NObeyesdad</th>\n","    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CALC                 MTRANS  \n","0  2.825629  no  0.855400  0.000000  Sometimes  Public_Transportation  \n","1  3.000000  no  1.000000  0.000000  Sometimes  Public_Transportation  \n","2  2.621877  no  0.000000  0.250502  Sometimes  Public_Transportation  \n","3  2.786417  no  0.094851  0.000000  Sometimes  Public_Transportation  \n","4  2.653531  no  0.000000  0.741069  Sometimes  Public_Transportation  "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["test.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1710701351630,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"CKqKvrIw5CWB","outputId":"798df9d4-d39c-4ad3-ac50-84a391142411"},"outputs":[{"data":{"text/plain":["(20758, 18)"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["train.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1710701352433,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"OAfwb7Pf5CWB","outputId":"91e5112a-24f9-4899-8c02-9a78ee4b4631"},"outputs":[{"data":{"text/plain":["(13840, 17)"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["test.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1710701352842,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"xLvOntw05CWB","outputId":"0da74a43-a932-45c1-ea52-0d253943b4eb"},"outputs":[{"data":{"text/plain":["Index(['id', 'Gender', 'Age', 'Height', 'Weight',\n","       'family_history_with_overweight', 'FAVC', 'FCVC', 'NCP', 'CAEC',\n","       'SMOKE', 'CH2O', 'SCC', 'FAF', 'TUE', 'CALC', 'MTRANS', 'NObeyesdad'],\n","      dtype='object')"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["train.columns"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1710701353245,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"1WKtHDsm5CWC","outputId":"d22f3215-0530-4aa9-d916-105086f3058d"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 20758 entries, 0 to 20757\n","Data columns (total 18 columns):\n"," #   Column                          Non-Null Count  Dtype  \n","---  ------                          --------------  -----  \n"," 0   id                              20758 non-null  int64  \n"," 1   Gender                          20758 non-null  object \n"," 2   Age                             20758 non-null  float64\n"," 3   Height                          20758 non-null  float64\n"," 4   Weight                          20758 non-null  float64\n"," 5   family_history_with_overweight  20758 non-null  object \n"," 6   FAVC                            20758 non-null  object \n"," 7   FCVC                            20758 non-null  float64\n"," 8   NCP                             20758 non-null  float64\n"," 9   CAEC                            20758 non-null  object \n"," 10  SMOKE                           20758 non-null  object \n"," 11  CH2O                            20758 non-null  float64\n"," 12  SCC                             20758 non-null  object \n"," 13  FAF                             20758 non-null  float64\n"," 14  TUE                             20758 non-null  float64\n"," 15  CALC                            20758 non-null  object \n"," 16  MTRANS                          20758 non-null  object \n"," 17  NObeyesdad                      20758 non-null  object \n","dtypes: float64(8), int64(1), object(9)\n","memory usage: 2.9+ MB\n"]}],"source":["train.info()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1710701354175,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"sjcCxNhz5CWC","outputId":"099eed9d-5d38-4b2b-b539-a29b2f64802a","scrolled":false},"outputs":[{"name":"stdout","output_type":"stream","text":["id : Null Count is 0 \n"," [    0     1     2 ... 20755 20756 20757] \n"," **************************************************\n","Gender : Null Count is 0 \n"," ['Male' 'Female'] \n"," **************************************************\n","Age : Null Count is 0 \n"," [24.443011 18.       20.952737 ... 25.746113 38.08886  33.852953] \n"," **************************************************\n","Height : Null Count is 0 \n"," [1.699998 1.56     1.71146  ... 1.791366 1.672594 1.536819] \n"," **************************************************\n","Weight : Null Count is 0 \n"," [ 81.66995   57.        50.165754 ... 152.063947  79.5       80.615325] \n"," **************************************************\n","family_history_with_overweight : Null Count is 0 \n"," ['yes' 'no'] \n"," **************************************************\n","FAVC : Null Count is 0 \n"," ['yes' 'no'] \n"," **************************************************\n","FCVC : Null Count is 0 \n"," [2.         1.880534   3.         2.679664   2.919751   1.99124\n"," 1.397468   2.636719   1.         1.392665   2.203962   2.971588\n"," 2.668949   1.98989905 2.417635   2.219186   2.919526   2.263245\n"," 2.649406   1.754401   2.303656   2.020785   2.068834   2.689929\n"," 2.979383   2.225731   2.843456   2.312528   2.962415   2.945967\n"," 2.108638   1.826885   2.200588   2.598051   2.984425   1.387489\n"," 2.76533    2.941627   2.490776   2.801514   2.336044   1.270448\n"," 2.9673     2.325623   2.722161   2.680375   2.938801   2.431346\n"," 1.994679   2.393837   1.428289   2.341999   2.967853   1.899116\n"," 1.906194   2.859097   2.997951   2.499388   1.4925     2.239634\n"," 2.587789   2.795086   2.805512   2.048962   2.319776   2.823179\n"," 1.188089   2.671238   1.882235   2.61939    2.191429   2.995599\n"," 2.594653   1.369529   2.457548   2.73691    1.947495   2.073224\n"," 2.57649    2.748243   2.736628   2.204914   1.475906   2.007845\n"," 2.890535   2.96405    2.915921   2.318355   2.766612   2.684335\n"," 2.819934   2.948425   1.961347   1.996638   2.111887   2.838037\n"," 1.469384   2.05687    2.966126   2.061952   2.92711    2.490507\n"," 1.164062   2.596579   2.591292   2.927218   1.003566   2.66889\n"," 2.630401   2.76802    2.156065   2.880759   2.446872   2.996717\n"," 2.802696   2.927409   2.724121   2.497548   2.942154   2.971574\n"," 1.963965   2.600217   2.630137   2.108711   2.716909   2.923916\n"," 2.177243   2.938616   1.936479   2.388168   2.195964   1.975675\n"," 2.262292   2.81646    2.568063   2.846452   1.522001   2.049112\n"," 2.661556   1.518966   2.619835   2.02472    1.220024   2.901924\n"," 2.037585   2.065752   1.0816     2.883745   2.247795   2.839048\n"," 2.910733   1.889883   2.758394   2.596364   2.997524   2.76632\n"," 1.096455   2.465575   2.00876    1.005578   2.96008    2.512719\n"," 1.868212   2.392665   1.31415    2.869436   1.950742   2.14128\n"," 1.760038   2.906269   2.766441   2.020502   1.036414   2.032883\n"," 2.739      2.549782   1.813234   2.225149   2.044326   2.197261\n"," 1.853314   2.397284   1.800122   2.998441   2.164062   2.290095\n"," 2.253371   2.818502   2.18354    1.362441   2.9553     2.959658\n"," 2.19331    2.178889   2.310751   2.598207   2.492758   2.650629\n"," 2.654076   2.886157   2.333503   1.303878   2.347942   2.061461\n"," 2.274846   1.528331   2.824559   1.006436   1.758394   2.869778\n"," 1.116068   1.524428   2.821727   2.95841    2.845961   2.772027\n"," 2.784471   2.09283    2.736647   2.956671   1.924632   1.455602\n"," 2.938031   2.342323   2.744994   2.913486   1.264234   2.315932\n"," 2.033745   2.041376   1.626369   1.99953    2.175276   2.4277\n"," 1.871213   2.499626   2.272453   1.123939   2.002796   2.913452\n"," 2.01695    2.910345   1.063449   1.203754   2.689577   1.834155\n"," 2.928234   2.467548   2.674431   1.570089   2.735297   2.969205\n"," 2.031185   2.09663    2.57691    2.274164   2.929889   2.432302\n"," 2.934671   2.206119   2.714447   2.866383   1.588114   2.328469\n"," 1.431346   2.451009   1.915279   2.412566   2.762496   1.517912\n"," 1.961069   2.133955   2.826251   2.84837    2.576449   2.877743\n"," 1.735664   2.838969   2.918113   2.151335   1.036159   2.220181\n"," 2.291846   2.973569   2.260543   2.562409   1.031149   1.624366\n"," 2.871137   2.734762   2.778079   2.540949   2.591439   2.39728\n"," 2.972426   1.451337   2.79606    1.263216   2.081238   2.19005\n"," 2.652779   2.051283   2.387426   1.276858   2.424977   2.332074\n"," 2.871016   2.780699   2.95801    1.952987   2.644094   1.766612\n"," 2.737149   2.030256   1.412566   2.191108   2.654792   2.281963\n"," 2.801992   1.973499   1.967061   2.444599   1.878251   1.887951\n"," 1.786841   2.075321   2.535154   2.162519   2.762325   2.104772\n"," 2.813775   2.923433   2.826036   2.869833   2.252472   1.064162\n"," 2.33361    2.765769   2.902469   2.036613   2.882522   2.619987\n"," 2.911312   2.720701   2.371338   2.87599    2.121909   2.482575\n"," 1.617093   1.69427    2.88853    2.407817   2.058687   1.851262\n"," 2.746408   2.79166    2.293705   1.849347   2.117121   2.615788\n"," 2.85916    1.537505   2.642744   2.880161   2.002076   1.140466\n"," 2.002784   2.425503   1.585183   1.368978   2.152264   1.992889\n"," 2.277436   2.872121   1.853991   2.939727   1.757466   2.273548\n"," 2.14961    2.974006   2.02091    1.328469   2.408561   2.964419\n"," 2.113843   2.736298   2.177896   2.908757   2.842102   1.650505\n"," 2.464518   2.685484   2.247037   2.21965    1.925064   2.252698\n"," 2.907062   2.911877   2.533605   2.19011    2.633855   1.289315\n"," 2.993634   2.047069   1.977298   1.253371   2.903545   2.475892\n"," 2.749629   2.927187   1.289421   2.222282   1.993101   2.684528\n"," 1.750809   2.631565   2.392179   2.935157   2.499108   1.123672\n"," 2.1239     2.055209   2.919584   2.725282   1.142468   2.278644\n"," 2.457547   2.640801   2.765063   1.557287   1.979944   2.372494\n"," 2.494451   2.128574   2.059138   2.317459   1.972926   2.045027\n"," 2.611847   1.572036   2.880483   1.521604   1.32534    2.943749\n"," 2.774562   2.061384   2.262171   2.009952   2.794197   2.723953\n"," 2.738485   2.34222    2.323351   2.637202   1.642241   1.780746\n"," 2.971351   2.787589   2.048216   2.33998    1.052699   2.938687\n"," 2.609123   2.13683    1.836554   1.57223    2.459976   1.874935\n"," 2.667229   2.44004    2.011656   2.954996   2.050619   1.562804\n"," 2.907542   1.712747   2.244654   2.915279   2.133964   1.492834\n"," 1.718156   2.870152   2.921576   2.330023   2.653721   2.031246\n"," 2.673638   2.294067   2.366949   2.176317   2.712747   2.815157\n"," 2.996186   2.907744   2.836055   1.00876    2.271306   2.786008\n"," 1.457758   2.992205   1.344854   1.897796   2.53915    1.34138\n"," 2.252653   2.969233   2.844607   2.595746   2.108163   2.658112\n"," 2.094184   2.885693   1.84199    2.510583   1.893428   2.423291\n"," 2.153639   2.300408   2.870895   2.749268   1.948248   2.100177\n"," 2.562687   2.442536   2.241606   2.580872   2.341133   2.310423\n"," 2.5621     2.185938   2.317734   2.432355   2.871768   2.884212\n"," 2.008245   2.450784   2.427689   1.601236   1.061461   1.712848\n"," 2.530233   2.592247   2.042762   1.710548   2.086898   1.631144\n"," 2.522399   1.72989    2.805533   2.5596     1.92822    1.118436\n"," 1.889199   2.944287   2.63165    2.037042   2.357496   2.976509\n"," 2.282803   2.025479   2.443538   2.288604   2.519592   2.21232\n"," 2.88626    2.060922   2.061969   1.901611   2.314175   2.843709\n"," 2.911749   2.982261   2.530066   2.693859   2.180047   2.569075\n"," 2.897899   2.696381   2.23372    2.766036   1.3307     2.352323\n"," 2.432886   2.699282   1.674431   1.482722   2.535315   1.75375\n"," 1.766849   2.49619    2.501224   2.802128   2.72989    2.977018\n"," 2.740633   2.002564   2.734314   2.909853   2.68601    2.784464\n"," 2.777165   2.008656   2.54527    2.948248   2.253707   1.919629\n"," 2.382705   2.311436   2.834155   2.181057   2.21498    2.186322\n"," 2.155182   2.524428   2.808027   2.052932   2.150054   2.450218\n"," 2.748971   1.826251   2.003951   2.021446   2.896562   2.165605\n"," 1.450218   2.70825    2.362918   2.097373   2.218599   2.205633\n"," 2.103335   2.753752   2.55996    2.86099    2.102696   2.22259\n"," 2.076689   1.780699   2.663866   1.947405   2.561638   2.217267\n"," 2.496455   2.486189   1.592183   1.588782   2.066101   2.104105\n"," 2.119643   1.972545   1.317729   2.992606   2.206276   2.737762\n"," 1.108663   2.403421   1.053534   2.38695    2.146598   2.070964\n"," 2.585942   2.323003   2.319648   2.555401   1.340405   2.071622\n"," 2.107854   2.951591   2.87781    2.348745   2.522183   2.607747\n"," 1.771693   2.312825   2.306844   1.202075   2.954417   1.996646\n"," 2.048582   2.206399   1.620845   2.71897    2.252382   1.133844\n"," 2.029634   2.265973   1.168856   2.381164   2.667676   2.027574\n"," 2.767731   2.706134   1.899793   2.827773   2.129668   2.652958\n"," 1.773265   2.334474   2.977585   2.067817   2.724285   2.253998\n"," 2.754646   1.27785    2.507841   1.122127   1.609938   2.074843\n"," 2.595957   2.015258   2.588089   2.750715   1.443674   1.846452\n"," 2.846981   1.943927   1.926381   2.046651   2.964319   1.206276\n"," 2.493448   2.277077   2.286481   1.83746    2.983042   2.08868\n"," 1.452524   2.552388   2.198315   2.184843   1.261288   2.992329\n"," 1.794825   1.321028   2.443674   2.145114   1.649974   1.111887\n"," 2.000466   1.140615   2.921225   1.595746   2.159033   2.028571\n"," 2.215464   2.577427   2.734715   2.703436   2.663421   1.317734\n"," 2.501236   2.612941   1.421656   2.92416    1.473088   1.078719\n"," 1.812283   2.232836   2.129969   2.052152   2.396265   2.708965\n"," 2.956297   1.989905   2.688054   2.010684   1.859097   2.138334\n"," 2.976975   2.247704   2.541785   1.679935   1.729824   2.557287\n"," 2.501683   2.116432   2.231915   2.812388   1.081585   2.519792\n"," 2.679724   2.941929   2.880792   2.620963   2.04516    2.947495\n"," 2.009796   2.559571   2.933409   1.918251   2.628791   2.939671\n"," 2.123159   1.002564   2.038774   2.543563   2.120185   2.08841\n"," 2.503244   2.274491   2.165408   2.694281   2.988668   2.990741\n"," 2.656912   1.94313    2.742796   2.416044   2.8557     2.822179\n"," 2.721356   2.069267   2.244142   2.399531   2.122127   1.067909\n"," 2.922511   2.966617   2.349419   1.021136   2.793561   2.078082\n"," 1.687569   2.814517   1.655684   2.99448    2.874643   1.723921\n"," 2.760607   1.921031   2.971832   2.977298   2.240757   2.759286\n"," 2.642748   2.743277   2.205439   2.37464    2.707666   1.904732\n"," 2.014194   2.8        2.933015   1.584785   2.182401   2.95118\n"," 2.970983   2.252699   1.773079   2.949242   2.340405   2.076094\n"," 2.490937   1.910176   1.204855   2.178308   2.487781   2.571274\n"," 2.213135   2.822183   1.81646    2.592607   2.09449    2.754645\n"," 2.821977   2.01054    2.721507   1.960138   1.588185   1.666416\n"," 2.973499   2.984004   2.690754   2.099687   2.043359   1.873716\n"," 2.8813     1.386151   1.369421   1.340361   1.763941   2.836554\n"," 1.785286   1.21232    1.252698   1.83841    2.195368   2.767063\n"," 2.483979   2.14084    2.487167   2.342459   2.3307     2.800122\n"," 1.709585   2.851664   1.078529   2.613249   1.567101   2.814453\n"," 2.303041   2.490613   2.353603   2.303367   1.246822   2.94313\n"," 2.880534   2.936509   2.925941   2.763215   1.962947   2.770964\n"," 2.219156   2.81746    2.914453   2.320201   1.734314   1.252653\n"," 2.997062   1.658571   1.557747   2.294259   1.3899     2.868212\n"," 1.941357   1.842102   1.562409   1.472522   1.073224   2.115354\n"," 1.996186   2.622827   2.607335   2.731368  ] \n"," **************************************************\n","NCP : Null Count is 0 \n"," [2.983297 3.       1.411685 1.971472 2.164839 1.       2.954446 1.893811\n"," 3.998618 1.703299 2.937989 2.996444 2.581015 2.473913 1.437959 2.989791\n"," 4.       2.853676 1.104642 3.362758 1.169173 1.411808 2.98212  1.81698\n"," 3.762778 2.976211 2.993623 3.994588 3.087544 2.372311 2.376374 2.884479\n"," 2.994198 2.812283 3.654061 1.845858 2.475444 1.015488 2.806298 1.338033\n"," 1.077331 3.995957 2.884848 2.283673 2.806341 1.863012 3.590039 2.608416\n"," 2.129909 2.18162  1.672706 2.951837 2.692889 3.986652 2.449723 2.966803\n"," 2.9948   1.473088 1.882158 2.7976   2.13229  2.999346 1.320768 1.894384\n"," 2.122545 2.99321  3.205009 1.163666 1.08687  2.374791 2.993634 2.937607\n"," 2.831771 3.715148 2.272214 2.918124 3.546352 1.337035 1.226342 3.520555\n"," 1.105617 1.834472 1.867836 1.250548 1.818026 2.567567 3.559841 1.134042\n"," 3.821168 3.24934  2.992606 2.175153 2.725012 1.193486 1.717608 2.999744\n"," 3.821461 3.98955  1.851088 2.141839 2.952821 2.992083 2.968098 3.897078\n"," 1.057935 2.119826 2.574108 3.737914 2.057935 2.622055 2.279546 2.650088\n"," 1.532833 2.870005 2.372339 2.669766 1.262831 1.00061  1.80815  2.993856\n"," 1.820779 1.941307 3.981997 3.281391 3.731212 2.701689 1.66338  1.600812\n"," 1.924168 2.358298 1.73762  1.135278 3.765526 2.301129 1.79558  2.015675\n"," 2.050121 2.487674 1.009426 3.058539 1.724887 2.175432 2.994046 2.938902\n"," 1.271624 2.902639 2.711238 2.77684  3.647154 2.791366 3.156309 2.977909\n"," 1.114564 2.95833  3.392811 1.835543 2.449067 2.799979 2.933409 1.92822\n"," 3.196043 2.956422 2.39007  3.623364 2.915921 2.491315 2.608055 3.558637\n"," 3.471536 2.976098 2.832018 2.844138 1.240046 2.970675 2.695396 2.977999\n"," 1.001542 1.630506 2.693646 2.427137 2.806566 2.834253 2.475228 2.658639\n"," 1.095223 3.891994 2.991671 2.038373 2.473911 1.915921 1.089048 2.6648\n"," 3.095663 1.134321 2.902766 2.625942 1.101404 2.983201 1.672751 2.842848\n"," 1.888067 2.845307 2.658837 2.740492 2.865657 2.978103 1.773916 2.879541\n"," 1.513835 3.699594 2.37985  2.       2.962004 3.985442 3.378859 2.911568\n"," 2.935381 2.272801 3.12544  2.468421 1.211606 1.154318 1.685134 3.804944\n"," 2.627173 3.715306 3.207071 2.812377 2.049565 2.986637 1.865238 1.109956\n"," 1.194815 1.000283 2.499108 2.842035 2.720642 2.116195 1.72626  1.9154\n"," 2.964024 1.152521 1.546665 2.753418 1.873484 2.595126 2.10601  2.809716\n"," 2.395785 1.082304 3.715118 2.547086 3.263201 1.625942 3.34175  2.920373\n"," 3.266644 2.965494 3.96981  2.992903 1.989398 2.877583 3.240578 1.273128\n"," 2.625475 2.943307 3.21043  3.566082 3.987707 1.193729 1.630846 2.997414\n"," 1.80993  1.000414 2.27374  2.601675 1.735493 2.996084 2.996834 2.092179\n"," 3.051804 3.205587 2.138375 2.120936 2.987652 2.270163 1.193589 2.961192\n"," 3.697831 2.986172 2.961706 2.043359 2.946063 2.240424 3.592415 2.19011\n"," 2.888193 2.974204 1.317884 2.9774   1.476204 2.582591 1.458507 2.127797\n"," 2.701521 1.874532 3.494849 3.019574 1.627555 1.418985 2.967089 1.402771\n"," 1.259803 2.909117 3.642802 3.435904 1.680838 3.390143 2.646717 2.378211\n"," 3.105007 2.87747  2.734762 2.218285 1.10548  2.880817 3.691226 1.202179\n"," 2.142328 2.9796   2.991666 2.604998 2.849848 3.409363 2.271734 3.098399\n"," 3.734323 3.489918 3.732126 3.220181 3.443456 2.838388 1.508685 1.619796\n"," 3.714833 3.45259  1.058123 2.752318 2.510135 2.993084 3.129155 2.434347\n"," 3.193671 1.971659 1.146052 3.433908 2.743277 2.029858 3.995147 1.001383\n"," 1.977221 2.645858 2.756622 1.07976  3.042774 3.90779  1.792695 1.075553\n"," 2.735706 1.578521 1.217651 3.371832 1.326982 2.883984 2.658478 2.683061\n"," 3.339914 1.010319 1.124977 2.977543 1.590982 1.471053 2.973476 2.57038\n"," 2.938135 1.384322 2.278652 2.910794 1.704828 3.712183 1.890213 3.36313\n"," 2.721238 1.231915 2.675148 1.91863  2.443812 3.238258 2.562895 3.612941\n"," 2.837388 1.782109 2.623079 1.94313  1.865657 3.376844 1.355354 1.001633\n"," 2.123138 1.060796 2.973504 2.857787 1.068443 3.394788 1.049534 1.131695\n"," 3.285167 1.237454 2.91753  3.156153 1.682804 2.741413 3.376717 1.13715\n"," 2.96405  1.281165 3.586082 1.346987 3.884861 1.599464 3.047959 1.974233\n"," 3.171082 3.292386 2.27372  2.850948 1.097312 2.699971 2.675411 2.644692\n"," 2.749334 2.371658 1.709546 1.477581 3.595761 1.105616 1.474836 1.400943\n"," 3.245148 1.355752 2.849347 2.988771 3.39007  1.999014 3.832911 2.973729\n"," 2.975675 1.213431 2.675823 2.894142 2.377056 2.974568 2.392811 2.65772\n"," 2.956622 3.092116 2.696051 2.282392 1.416309 3.322522 2.815255 3.118013\n"," 2.070033 1.802305 1.548407 1.496776 2.326233 2.716106 2.880794 1.976744\n"," 1.068196 2.040582 3.054899 2.988539 2.677693 2.794156 1.014916 2.975362\n"," 3.914454 3.576103 3.754599 2.900915 2.732331 1.734762 2.401341 2.9154\n"," 2.870661 2.391753 3.488342 3.618722 2.110937 1.081805 2.400943 1.02075\n"," 2.64155  1.289315 1.116401 2.752705 2.667711 2.375026 1.120102 2.093831\n"," 2.971574 2.174968 1.311797 1.374791 3.563744 1.706551 1.139317 2.961113\n"," 2.044035 1.394539 3.990925 3.98525  1.130751 1.521546 2.419656 2.358455\n"," 1.171027 3.887906 1.391778 3.420618 2.100918 2.893778 1.09749  3.435905\n"," 2.298612 2.762883 1.028426 1.198643 3.087119 2.779379 1.313403 2.049908\n"," 3.30846  3.53009  2.463113 3.131032 1.2919   1.030416 2.217651 2.119682\n"," 2.687502 3.394539 1.836226 2.137068 1.032887 2.89292  2.714115 3.483449\n"," 3.053598 3.989492 1.69608  2.765213 2.698883 3.998766 1.259628 3.747163\n"," 3.60885  3.728377 2.73762  2.737571 1.854536 1.656588 3.03779  3.704828\n"," 1.288716 2.783336 1.028538 2.337035 2.843319 1.612747 2.036794 1.047197\n"," 2.372705 3.292956 2.113575 2.047866 2.656588 2.996543 3.28926  2.041558\n"," 1.044628 1.293342 1.343117 2.609801 1.630728 1.281683 2.521546 2.988602\n"," 2.139775 1.660768 2.568063 2.478794 1.487674 2.118153 3.937099 2.805436\n"," 2.930044 1.672958 1.099151 2.948721 1.468948 2.404788 1.296156 3.169089\n"," 2.597608 1.555557 3.755976 1.72326  1.923607 1.418833 1.834373 2.984523\n"," 1.25535  3.11158  3.904858 3.648194 2.52751  2.679724 2.600812 3.711238\n"," 1.146794 3.502604 2.845858 2.598079 2.042078 1.631184 3.770379 2.874532\n"," 1.713762 3.097373 1.73988  2.676148 3.725797 2.101841 1.788602 2.752815\n"," 1.890682 2.85916  1.352649 1.289733 2.818026 1.164839 2.152733 2.814518\n"," 2.705445 1.478334 2.389717 2.157164 1.340361 2.656622 1.9      1.015467\n"," 2.989112 2.270546 2.415522 2.960131 3.179995 2.655265 1.24884  1.109115\n"," 1.569176 3.335876 3.671076 3.259033 3.531038 2.733077 1.350099 1.595784\n"," 3.054416 2.014671 2.929123 2.269799 2.083831 1.890413 3.788602 1.667596\n"," 2.256119] \n"," **************************************************\n","CAEC : Null Count is 0 \n"," ['Sometimes' 'Frequently' 'no' 'Always'] \n"," **************************************************\n","SMOKE : Null Count is 0 \n"," ['no' 'yes'] \n"," **************************************************\n","CH2O : Null Count is 0 \n"," [2.763573 2.       1.910378 ... 2.151166 1.485836 1.365188] \n"," **************************************************\n","SCC : Null Count is 0 \n"," ['no' 'yes'] \n"," **************************************************\n","FAF : Null Count is 0 \n"," [0.       1.       0.866045 ... 0.540397 0.271174 0.988668] \n"," **************************************************\n","TUE : Null Count is 0 \n"," [0.976473 1.       1.673584 ... 1.217929 1.439004 0.768375] \n"," **************************************************\n","CALC : Null Count is 0 \n"," ['Sometimes' 'no' 'Frequently'] \n"," **************************************************\n","MTRANS : Null Count is 0 \n"," ['Public_Transportation' 'Automobile' 'Walking' 'Motorbike' 'Bike'] \n"," **************************************************\n","NObeyesdad : Null Count is 0 \n"," ['Overweight_Level_II' 'Normal_Weight' 'Insufficient_Weight'\n"," 'Obesity_Type_III' 'Obesity_Type_II' 'Overweight_Level_I'\n"," 'Obesity_Type_I'] \n"," **************************************************\n"]}],"source":["for i in train.columns:\n","    print(\n","        i,': Null Count is',train[i].isnull().sum(),\n","          '\\n',train[i].unique(),\n","          '\\n','*'*50\n","    )"]},{"cell_type":"markdown","metadata":{"id":"XIMP8z9C5CWC"},"source":["**Note:**\n","- We have columns that should be **categorical** but the values in it are **continous**, so we will **round** it like:\n","1. **Age:** should be int.\n","2. **Height, Weight and CH2O:** round to closest to 2 numbers.\n","3. **FCVC, NCP, TUE and FAF:** should round to int."]},{"cell_type":"markdown","metadata":{"id":"lOjZTgYl5CWC"},"source":["**Check Datatype**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":404,"status":"ok","timestamp":1710701359736,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"axrgbcuq5CWC","outputId":"c13ab639-6a9f-48e2-f77f-5f55a25558bf","scrolled":true},"outputs":[{"data":{"text/plain":["id                                  int64\n","Gender                             object\n","Age                               float64\n","Height                            float64\n","Weight                            float64\n","family_history_with_overweight     object\n","FAVC                               object\n","FCVC                              float64\n","NCP                               float64\n","CAEC                               object\n","SMOKE                              object\n","CH2O                              float64\n","SCC                                object\n","FAF                               float64\n","TUE                               float64\n","CALC                               object\n","MTRANS                             object\n","NObeyesdad                         object\n","dtype: object"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["train.dtypes"]},{"cell_type":"markdown","metadata":{"id":"Z4Kk2xAR5CWD"},"source":["- Datatypes are correct."]},{"cell_type":"markdown","metadata":{"id":"qLRkxALJ5CWD"},"source":["**Check for null values**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1710701361700,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"TM0Hf6Er5CWD","outputId":"bb1a662e-7b6f-4f95-8efb-a275e2531050"},"outputs":[{"data":{"text/plain":["id                                0\n","Gender                            0\n","Age                               0\n","Height                            0\n","Weight                            0\n","family_history_with_overweight    0\n","FAVC                              0\n","FCVC                              0\n","NCP                               0\n","CAEC                              0\n","SMOKE                             0\n","CH2O                              0\n","SCC                               0\n","FAF                               0\n","TUE                               0\n","CALC                              0\n","MTRANS                            0\n","NObeyesdad                        0\n","dtype: int64"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["train.isnull().sum()"]},{"cell_type":"markdown","metadata":{"id":"IvERyhqF5CWD"},"source":["- There is missing in data."]},{"cell_type":"markdown","metadata":{"id":"-pPomNMq5CWD"},"source":["**Check for range of data**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":426,"status":"ok","timestamp":1710701364637,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"4O_rqac_5CWD","outputId":"aae8b3d1-ca2e-40c6-8699-fa145409c607"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n  \"name\": \"train\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7481.0646574335215,\n        \"min\": 0.0,\n        \"max\": 20758.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          20758.0,\n          10378.5,\n          15567.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 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\"num_unique_values\": 7,\n        \"samples\": [\n          20758.0,\n          0.6167562236968879,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}","type":"dataframe"},"text/html":["\n","  <div id=\"df-2f0a0c8c-5fd2-4994-9b62-a24e27c0b0b8\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>Age</th>\n","      <th>Height</th>\n","      <th>Weight</th>\n","      <th>FCVC</th>\n","      <th>NCP</th>\n","      <th>CH2O</th>\n","      <th>FAF</th>\n","      <th>TUE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>20758.00000</td>\n","      <td>20758.000000</td>\n","      <td>20758.000000</td>\n","      <td>20758.000000</td>\n","      <td>20758.000000</td>\n","      <td>20758.000000</td>\n","      <td>20758.000000</td>\n","      <td>20758.000000</td>\n","      <td>20758.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>10378.50000</td>\n","      <td>23.841804</td>\n","      <td>1.700245</td>\n","      <td>87.887768</td>\n","      <td>2.445908</td>\n","      <td>2.761332</td>\n","      <td>2.029418</td>\n","      <td>0.981747</td>\n","      <td>0.616756</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>5992.46278</td>\n","      <td>5.688072</td>\n","      <td>0.087312</td>\n","      <td>26.379443</td>\n","      <td>0.533218</td>\n","      <td>0.705375</td>\n","      <td>0.608467</td>\n","      <td>0.838302</td>\n","      <td>0.602113</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.00000</td>\n","      <td>14.000000</td>\n","      <td>1.450000</td>\n","      <td>39.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>5189.25000</td>\n","      <td>20.000000</td>\n","      <td>1.631856</td>\n","      <td>66.000000</td>\n","      <td>2.000000</td>\n","      <td>3.000000</td>\n","      <td>1.792022</td>\n","      <td>0.008013</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>10378.50000</td>\n","      <td>22.815416</td>\n","      <td>1.700000</td>\n","      <td>84.064875</td>\n","      <td>2.393837</td>\n","      <td>3.000000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>0.573887</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>15567.75000</td>\n","      <td>26.000000</td>\n","      <td>1.762887</td>\n","      <td>111.600553</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>2.549617</td>\n","      <td>1.587406</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>20757.00000</td>\n","      <td>61.000000</td>\n","      <td>1.975663</td>\n","      <td>165.057269</td>\n","      <td>3.000000</td>\n","      <td>4.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>2.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f0a0c8c-5fd2-4994-9b62-a24e27c0b0b8')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path 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 2.761332      2.029418      0.981747      0.616756  \n","std        0.705375      0.608467      0.838302      0.602113  \n","min        1.000000      1.000000      0.000000      0.000000  \n","25%        3.000000      1.792022      0.008013      0.000000  \n","50%        3.000000      2.000000      1.000000      0.573887  \n","75%        3.000000      2.549617      1.587406      1.000000  \n","max        4.000000      3.000000      3.000000      2.000000  "]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["train.describe()"]},{"cell_type":"markdown","metadata":{"id":"qCQCtgYy5CWD"},"source":["- All values in the correct range."]},{"cell_type":"markdown","metadata":{"id":"YE4hAi-l5CWD"},"source":["**Check for uniqueness**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OGCpIcQb5CWE"},"outputs":[],"source":["train.drop(columns='id', axis=1, inplace=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1710701367641,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"aLcxOTXz5CWE","outputId":"0d8a9e1b-b699-4462-d44a-99357984cf99"},"outputs":[{"data":{"text/plain":["0"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["train.duplicated().sum()"]},{"cell_type":"markdown","metadata":{"id":"8Y1dgBq85CWE"},"source":["- There is no duplication in data."]},{"cell_type":"markdown","metadata":{"id":"ULmWibLf5CWE"},"source":["- **Numerical Columns:** Age, Height, Weight.\n","- **Categorical Columns:** The rest."]},{"cell_type":"markdown","metadata":{"id":"I1AWZe7E5CWE"},"source":["**Categorical Columns**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1710701370805,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"jeDyiYSh5CWE","outputId":"ff9ed415-dcb2-4871-a279-f8f284eafa0e"},"outputs":[{"data":{"text/plain":["Obesity_Type_III       4046\n","Obesity_Type_II        3248\n","Normal_Weight          3082\n","Obesity_Type_I         2910\n","Insufficient_Weight    2523\n","Overweight_Level_II    2522\n","Overweight_Level_I     2427\n","Name: NObeyesdad, dtype: int64"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["train['NObeyesdad'].value_counts()"]},{"cell_type":"markdown","metadata":{"id":"eHrjto-C5CWE"},"source":["- We have **7 categories** in the **label**."]},{"cell_type":"markdown","metadata":{"id":"me8NqJcH5CWE"},"source":["**Split training data into train and validation**"]},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":314,"status":"ok","timestamp":1710711785136,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"rS_iS0Ji5CWF"},"outputs":[],"source":["train_data, test_data = train_test_split(train, test_size=.2, random_state=42)"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1710711785468,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"ZnnCWq9B5CWF","outputId":"a59e774e-ea9a-4586-8705-b2aa8c5b64b6"},"outputs":[{"output_type":"stream","name":"stdout","text":["(16606, 18)\n","(4152, 18)\n"]}],"source":["print(train_data.shape)\n","print(test_data.shape)"]},{"cell_type":"markdown","metadata":{"id":"2Or5OnqY5CWF"},"source":["## EDA:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":696},"executionInfo":{"elapsed":481,"status":"ok","timestamp":1710701387767,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"tgucnW9h5CWF","outputId":"4ab29118-9c7a-459f-8c8a-974a39e0e786","scrolled":false},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA/cAAAKnCAYAAADDf72MAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABR/ElEQVR4nO3deZQU1b0H8N8AzrAOCIEZUESCQcAAKhocF7YgIyFEnyYGJIIKGn0QFxQJ7xlFTMQlqNFgTDSCeYG4JHHFIIiCBlERJSoiKqKYyGIQGEBkrfeHh47NOuDATOnnc06fM1X3dvW93VV159u1dE6SJEkAAAAAqVWpvBsAAAAAfDHCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAylUp7wakwebNm+PDDz+MWrVqRU5OTnk3BwAAgC+5JEli1apV0ahRo6hUadfH5YX7Uvjwww+jcePG5d0MAAAAvmI++OCDOPDAA3dZT7gvhVq1akXEZ29qfn5+ObcGAACAL7uSkpJo3LhxJo/uinBfCltOxc/PzxfuAQAA2GdKe2m4G+oBAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHJVyrsBAABA6bUb8ofybgKlMOvGvuXdBL5iHLkHAACAlBPuAQAAIOWEewAAAEg54R4AAABSTrgHAACAlBPuAQAAIOWEewAAAEg54R4AAABSTrgHAACAlBPuAQAAIOWEewAAAEg54R4AAABSTrgHAACAlBPuAQAAIOWEewAAAEg54R4AAABSTrgHAACAlBPuAQAAIOWEewAAAEg54R4AAABSTrgHAACAlBPuAQAAIOWEewAAAEg54R4AAABSTrgHAACAlBPuAQAAIOWEewAAAEg54R4AAABSTrgHAACAlBPuAQAAIOWEewAAAEg54R4AAABSTrgHAACAlBPuAQAAIOWEewAAAEg54R4AAABSTrgHAACAlBPuAQAAIOWEewAAAEi5cg33v/nNb6JNmzaRn58f+fn5UVRUFH/7298y5Z9++mkMHDgw6tWrFzVr1ozTTjstlixZkrWMhQsXRo8ePaJ69erRoEGDGDJkSGzcuDGrztSpU+PII4+MvLy8OOSQQ2Ls2LH7onsAAACwT5RruD/wwAPjuuuui1mzZsVLL70UXbp0iZNPPjnmzJkTERGXXHJJPProo/HAAw/EtGnT4sMPP4xTTz018/xNmzZFjx49Yv369fHcc8/FPffcE2PHjo0rr7wyU2fBggXRo0eP6Ny5c8yePTsuvvjiGDBgQDzxxBP7vL8AAACwN+QkSZKUdyM+r27dunHjjTfG97///ahfv36MHz8+vv/970dExJtvvhktW7aMGTNmxDHHHBN/+9vf4rvf/W58+OGHUVBQEBERd9xxRwwdOjQ++uijyM3NjaFDh8aECRPi9ddfz7xGr169YsWKFTFx4sRStamkpCRq164dK1eujPz8/LLvNAAAlFK7IX8o7yZQCrNu7FveTSDldjeHVphr7jdt2hT33ntvrFmzJoqKimLWrFmxYcOG6Nq1a6ZOixYt4qCDDooZM2ZERMSMGTOidevWmWAfEVFcXBwlJSWZo/8zZszIWsaWOluWsT3r1q2LkpKSrAcAAABUVOUe7l977bWoWbNm5OXlxfnnnx8PPvhgtGrVKhYvXhy5ublRp06drPoFBQWxePHiiIhYvHhxVrDfUr6lbGd1SkpKYu3atdtt08iRI6N27dqZR+PGjcuiqwAAALBXlHu4P/TQQ2P27NnxwgsvxAUXXBD9+vWLN954o1zbNGzYsFi5cmXm8cEHH5RrewAAAGBnqpR3A3Jzc+OQQw6JiIh27drFzJkz41e/+lX88Ic/jPXr18eKFSuyjt4vWbIkCgsLIyKisLAwXnzxxazlbbmb/ufrbH2H/SVLlkR+fn5Uq1Ztu23Ky8uLvLy8MukfAAAA7G3lfuR+a5s3b45169ZFu3btYr/99ospU6ZkyubNmxcLFy6MoqKiiIgoKiqK1157LZYuXZqpM3ny5MjPz49WrVpl6nx+GVvqbFkGAAAApF25HrkfNmxYdO/ePQ466KBYtWpVjB8/PqZOnRpPPPFE1K5dO/r37x+DBw+OunXrRn5+fvzkJz+JoqKiOOaYYyIiolu3btGqVas488wz44YbbojFixfHFVdcEQMHDswceT///PPj17/+dVx++eVxzjnnxFNPPRX3339/TJgwoTy7DgAAAGWmXMP90qVLo2/fvrFo0aKoXbt2tGnTJp544ok48cQTIyLi5ptvjkqVKsVpp50W69ati+Li4rj99tszz69cuXI89thjccEFF0RRUVHUqFEj+vXrFyNGjMjUadq0aUyYMCEuueSS+NWvfhUHHnhg3HXXXVFcXLzP+wsAAAB7Q4X7nfuKyO/cAwBQUfid+3TwO/d8Uan9nXsAAABgzwj3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKSccA8AAAApJ9wDAABAygn3AAAAkHLCPQAAAKRclfJuAMBXWbshfyjvJlBKs27sW95NAADYIUfuAQAAIOWEewAAAEg54R4AAABSTrgHAACAlBPuAQAAIOWEewAAAEg54R4AAABSTrgHAACAlBPuAQAAIOWEewAAAEg54R4AAABSrlzD/ciRI+Poo4+OWrVqRYMGDeKUU06JefPmZdXp1KlT5OTkZD3OP//8rDoLFy6MHj16RPXq1aNBgwYxZMiQ2LhxY1adqVOnxpFHHhl5eXlxyCGHxNixY/d29wAAAGCfKNdwP23atBg4cGA8//zzMXny5NiwYUN069Yt1qxZk1Xv3HPPjUWLFmUeN9xwQ6Zs06ZN0aNHj1i/fn0899xzcc8998TYsWPjyiuvzNRZsGBB9OjRIzp37hyzZ8+Oiy++OAYMGBBPPPHEPusrAAAA7C1VyvPFJ06cmDU9duzYaNCgQcyaNSs6dOiQmV+9evUoLCzc7jImTZoUb7zxRjz55JNRUFAQhx9+eFxzzTUxdOjQGD58eOTm5sYdd9wRTZs2jVGjRkVERMuWLePvf/973HzzzVFcXLz3OggAAAD7QIW65n7lypUREVG3bt2s+ePGjYuvfe1r8c1vfjOGDRsWn3zySaZsxowZ0bp16ygoKMjMKy4ujpKSkpgzZ06mTteuXbOWWVxcHDNmzNhuO9atWxclJSVZDwAAAKioyvXI/edt3rw5Lr744jjuuOPim9/8Zmb+GWecEU2aNIlGjRrFq6++GkOHDo158+bFX//614iIWLx4cVawj4jM9OLFi3dap6SkJNauXRvVqlXLKhs5cmRcffXVZd5HAAAA2BsqTLgfOHBgvP766/H3v/89a/55552X+bt169bRsGHD+Pa3vx3z58+PZs2a7ZW2DBs2LAYPHpyZLikpicaNG++V1wIAAIAvqkKclj9o0KB47LHH4umnn44DDzxwp3Xbt28fERHvvPNOREQUFhbGkiVLsupsmd5ynf6O6uTn529z1D4iIi8vL/Lz87MeAAAAUFGVa7hPkiQGDRoUDz74YDz11FPRtGnTXT5n9uzZERHRsGHDiIgoKiqK1157LZYuXZqpM3ny5MjPz49WrVpl6kyZMiVrOZMnT46ioqIy6gkAAACUn3IN9wMHDow//vGPMX78+KhVq1YsXrw4Fi9eHGvXro2IiPnz58c111wTs2bNivfeey8eeeSR6Nu3b3To0CHatGkTERHdunWLVq1axZlnnhn/+Mc/4oknnogrrrgiBg4cGHl5eRERcf7558e7774bl19+ebz55ptx++23x/333x+XXHJJufUdAAAAykq5hvvf/OY3sXLlyujUqVM0bNgw87jvvvsiIiI3NzeefPLJ6NatW7Ro0SIuvfTSOO200+LRRx/NLKNy5crx2GOPReXKlaOoqCh+9KMfRd++fWPEiBGZOk2bNo0JEybE5MmTo23btjFq1Ki46667/AweAAAAXwrlekO9JEl2Wt64ceOYNm3aLpfTpEmTePzxx3dap1OnTvHKK6/sVvsAAAAgDSrEDfUAAACAPVdhfgoPAIBttRvyh/JuAqUw68a+5d0E4CvOkXsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJSrUt4N+LJrN+QP5d0ESmHWjX3LuwkAAAB7zJF7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFKuXMP9yJEj4+ijj45atWpFgwYN4pRTTol58+Zl1fn0009j4MCBUa9evahZs2acdtppsWTJkqw6CxcujB49ekT16tWjQYMGMWTIkNi4cWNWnalTp8aRRx4ZeXl5ccghh8TYsWP3dvcAAABgnyjXcD9t2rQYOHBgPP/88zF58uTYsGFDdOvWLdasWZOpc8kll8Sjjz4aDzzwQEybNi0+/PDDOPXUUzPlmzZtih49esT69evjueeei3vuuSfGjh0bV155ZabOggULokePHtG5c+eYPXt2XHzxxTFgwIB44okn9ml/AQAAYG+oUp4vPnHixKzpsWPHRoMGDWLWrFnRoUOHWLlyZfz+97+P8ePHR5cuXSIiYsyYMdGyZct4/vnn45hjjolJkybFG2+8EU8++WQUFBTE4YcfHtdcc00MHTo0hg8fHrm5uXHHHXdE06ZNY9SoURER0bJly/j73/8eN998cxQXF+/zfgPAjrQb8ofybgKlNOvGvuXdBADIqFDX3K9cuTIiIurWrRsREbNmzYoNGzZE165dM3VatGgRBx10UMyYMSMiImbMmBGtW7eOgoKCTJ3i4uIoKSmJOXPmZOp8fhlb6mxZxtbWrVsXJSUlWQ8AAACoqMr1yP3nbd68OS6++OI47rjj4pvf/GZERCxevDhyc3OjTp06WXULCgpi8eLFmTqfD/ZbyreU7axOSUlJrF27NqpVq5ZVNnLkyLj66qvLrG/weY7KpYMjcgAApEmFOXI/cODAeP311+Pee+8t76bEsGHDYuXKlZnHBx98UN5NAgAAgB2qEEfuBw0aFI899lg888wzceCBB2bmFxYWxvr162PFihVZR++XLFkShYWFmTovvvhi1vK23E3/83W2vsP+kiVLIj8/f5uj9hEReXl5kZeXVyZ9AwAAgL2tXI/cJ0kSgwYNigcffDCeeuqpaNq0aVZ5u3btYr/99ospU6Zk5s2bNy8WLlwYRUVFERFRVFQUr732WixdujRTZ/LkyZGfnx+tWrXK1Pn8MrbU2bIMAAAASLNyPXI/cODAGD9+fDz88MNRq1atzDXytWvXjmrVqkXt2rWjf//+MXjw4Khbt27k5+fHT37ykygqKopjjjkmIiK6desWrVq1ijPPPDNuuOGGWLx4cVxxxRUxcODAzNH3888/P37961/H5ZdfHuecc0489dRTcf/998eECRPKre8AAABQVsr1yP1vfvObWLlyZXTq1CkaNmyYedx3332ZOjfffHN897vfjdNOOy06dOgQhYWF8de//jVTXrly5XjssceicuXKUVRUFD/60Y+ib9++MWLEiEydpk2bxoQJE2Ly5MnRtm3bGDVqVNx1111+Bg8AAIAvhXI9cp8kyS7rVK1aNUaPHh2jR4/eYZ0mTZrE448/vtPldOrUKV555ZXdbiMAAABUdBXmbvkAAADAnhHuAQAAIOWEewAAAEi5CvE79wAAAOyZdkP+UN5NoBRm3dh3ry7fkXsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUm6Pwn2XLl1ixYoV28wvKSmJLl26fNE2AQAAALthj8L91KlTY/369dvM//TTT+PZZ5/9wo0CAAAASq/K7lR+9dVXM3+/8cYbsXjx4sz0pk2bYuLEiXHAAQeUXesAAACAXdqtcH/44YdHTk5O5OTkbPf0+2rVqsVtt91WZo0DAAAAdm23wv2CBQsiSZL4+te/Hi+++GLUr18/U5abmxsNGjSIypUrl3kjAQAAgB3brXDfpEmTiIjYvHnzXmkMAAAAsPt2K9x/3ttvvx1PP/10LF26dJuwf+WVV37hhgEAAACls0fh/s4774wLLrggvva1r0VhYWHk5ORkynJycoR7AAAA2If2KNz//Oc/j1/84hcxdOjQsm4PAAAAsJv26Hfuly9fHj/4wQ/Kui0AAADAHtijcP+DH/wgJk2aVNZtAQAAAPbAHp2Wf8ghh8TPfvazeP7556N169ax3377ZZVfeOGFZdI4AAAAYNf2KNz/7ne/i5o1a8a0adNi2rRpWWU5OTnCPQAAAOxDexTuFyxYUNbtAAAAAPbQHl1zDwAAAFQce3Tk/pxzztlp+d13371HjQEAAAB23x6F++XLl2dNb9iwIV5//fVYsWJFdOnSpUwaBgAAAJTOHoX7Bx98cJt5mzdvjgsuuCCaNWv2hRsFAAAAlF6ZXXNfqVKlGDx4cNx8881ltUgAAACgFMr0hnrz58+PjRs3luUiAQAAgF3Yo9PyBw8enDWdJEksWrQoJkyYEP369SuThgEAAACls0fh/pVXXsmarlSpUtSvXz9GjRq1yzvpAwAAAGVrj8L9008/XdbtAAAAAPbQHoX7LT766KOYN29eREQceuihUb9+/TJpFAAAAFB6e3RDvTVr1sQ555wTDRs2jA4dOkSHDh2iUaNG0b9///jkk0/Kuo0AAADATuxRuB88eHBMmzYtHn300VixYkWsWLEiHn744Zg2bVpceumlZd1GAAAAYCf26LT8v/zlL/HnP/85OnXqlJn3ne98J6pVqxann356/OY3vymr9gEAAAC7sEdH7j/55JMoKCjYZn6DBg2clg8AAAD72B6F+6Kiorjqqqvi008/zcxbu3ZtXH311VFUVFRmjQMAAAB2bY9Oy7/lllvipJNOigMPPDDatm0bERH/+Mc/Ii8vLyZNmlSmDQQAAAB2bo/CfevWrePtt9+OcePGxZtvvhkREb17944+ffpEtWrVyrSBAAAAwM7tUbgfOXJkFBQUxLnnnps1/+67746PPvoohg4dWiaNAwAAAHZtj665/+1vfxstWrTYZv5hhx0Wd9xxxxduFAAAAFB6exTuFy9eHA0bNtxmfv369WPRokVfuFEAAABA6e1RuG/cuHFMnz59m/nTp0+PRo0afeFGAQAAAKW3R9fcn3vuuXHxxRfHhg0bokuXLhERMWXKlLj88svj0ksvLdMGAgAAADu3R+F+yJAhsWzZsvjv//7vWL9+fUREVK1aNYYOHRrDhg0r0wYCAAAAO7dH4T4nJyeuv/76+NnPfhZz586NatWqxTe+8Y3Iy8sr6/YBAAAAu7BH4X6LmjVrxtFHH11WbQEAAAD2wB7dUA8AAACoOIR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASDnhHgAAAFJOuAcAAICUE+4BAAAg5YR7AAAASLlyDffPPPNM9OzZMxo1ahQ5OTnx0EMPZZWfddZZkZOTk/U46aSTsup8/PHH0adPn8jPz486depE//79Y/Xq1Vl1Xn311TjhhBOiatWq0bhx47jhhhv2dtcAAABgnynXcL9mzZpo27ZtjB49eod1TjrppFi0aFHm8ac//SmrvE+fPjFnzpyYPHlyPPbYY/HMM8/EeeedlykvKSmJbt26RZMmTWLWrFlx4403xvDhw+N3v/vdXusXAAAA7EtVyvPFu3fvHt27d99pnby8vCgsLNxu2dy5c2PixIkxc+bMOOqooyIi4rbbbovvfOc78ctf/jIaNWoU48aNi/Xr18fdd98dubm5cdhhh8Xs2bPjpptuyvoSAAAAANKqwl9zP3Xq1GjQoEEceuihccEFF8SyZcsyZTNmzIg6depkgn1ERNeuXaNSpUrxwgsvZOp06NAhcnNzM3WKi4tj3rx5sXz58u2+5rp166KkpCTrAQAAABVVhQ73J510UvzhD3+IKVOmxPXXXx/Tpk2L7t27x6ZNmyIiYvHixdGgQYOs51SpUiXq1q0bixcvztQpKCjIqrNlekudrY0cOTJq166deTRu3LisuwYAAABlplxPy9+VXr16Zf5u3bp1tGnTJpo1axZTp06Nb3/723vtdYcNGxaDBw/OTJeUlAj4AAAAVFgV+sj91r7+9a/H1772tXjnnXciIqKwsDCWLl2aVWfjxo3x8ccfZ67TLywsjCVLlmTV2TK9o2v58/LyIj8/P+sBAAAAFVWqwv0///nPWLZsWTRs2DAiIoqKimLFihUxa9asTJ2nnnoqNm/eHO3bt8/UeeaZZ2LDhg2ZOpMnT45DDz009t9//33bAQAAANgLyjXcr169OmbPnh2zZ8+OiIgFCxbE7NmzY+HChbF69eoYMmRIPP/88/Hee+/FlClT4uSTT45DDjkkiouLIyKiZcuWcdJJJ8W5554bL774YkyfPj0GDRoUvXr1ikaNGkVExBlnnBG5ubnRv3//mDNnTtx3333xq1/9Kuu0ewAAAEizcg33L730UhxxxBFxxBFHRETE4MGD44gjjogrr7wyKleuHK+++mp873vfi+bNm0f//v2jXbt28eyzz0ZeXl5mGePGjYsWLVrEt7/97fjOd74Txx9/fNZv2NeuXTsmTZoUCxYsiHbt2sWll14aV155pZ/BAwAA4EujXG+o16lTp0iSZIflTzzxxC6XUbdu3Rg/fvxO67Rp0yaeffbZ3W4fAAAApEGqrrkHAAAAtiXcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACknHAPAAAAKSfcAwAAQMoJ9wAAAJBywj0AAACkXLmG+2eeeSZ69uwZjRo1ipycnHjooYeyypMkiSuvvDIaNmwY1apVi65du8bbb7+dVefjjz+OPn36RH5+ftSpUyf69+8fq1evzqrz6quvxgknnBBVq1aNxo0bxw033LC3uwYAAAD7TLmG+zVr1kTbtm1j9OjR2y2/4YYb4tZbb4077rgjXnjhhahRo0YUFxfHp59+mqnTp0+fmDNnTkyePDkee+yxeOaZZ+K8887LlJeUlES3bt2iSZMmMWvWrLjxxhtj+PDh8bvf/W6v9w8AAAD2hSrl+eLdu3eP7t27b7csSZK45ZZb4oorroiTTz45IiL+8Ic/REFBQTz00EPRq1evmDt3bkycODFmzpwZRx11VERE3HbbbfGd73wnfvnLX0ajRo1i3LhxsX79+rj77rsjNzc3DjvssJg9e3bcdNNNWV8CAAAAQFpV2GvuFyxYEIsXL46uXbtm5tWuXTvat28fM2bMiIiIGTNmRJ06dTLBPiKia9euUalSpXjhhRcydTp06BC5ubmZOsXFxTFv3rxYvnz5dl973bp1UVJSkvUAAACAiqrChvvFixdHRERBQUHW/IKCgkzZ4sWLo0GDBlnlVapUibp162bV2d4yPv8aWxs5cmTUrl0782jcuPEX7xAAAADsJRU23JenYcOGxcqVKzOPDz74oLybBAAAADtUYcN9YWFhREQsWbIka/6SJUsyZYWFhbF06dKs8o0bN8bHH3+cVWd7y/j8a2wtLy8v8vPzsx4AAABQUVXYcN+0adMoLCyMKVOmZOaVlJTECy+8EEVFRRERUVRUFCtWrIhZs2Zl6jz11FOxefPmaN++fabOM888Exs2bMjUmTx5chx66KGx//7776PeAAAAwN5TruF+9erVMXv27Jg9e3ZEfHYTvdmzZ8fChQsjJycnLr744vj5z38ejzzySLz22mvRt2/faNSoUZxyyikREdGyZcs46aST4txzz40XX3wxpk+fHoMGDYpevXpFo0aNIiLijDPOiNzc3Ojfv3/MmTMn7rvvvvjVr34VgwcPLqdeAwAAQNkq15/Ce+mll6Jz586Z6S2Bu1+/fjF27Ni4/PLLY82aNXHeeefFihUr4vjjj4+JEydG1apVM88ZN25cDBo0KL797W9HpUqV4rTTTotbb701U167du2YNGlSDBw4MNq1axdf+9rX4sorr/QzeAAAAHxplGu479SpUyRJssPynJycGDFiRIwYMWKHderWrRvjx4/f6eu0adMmnn322T1uJwAAAFRkFfaaewAAAKB0hHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUq5Ch/vhw4dHTk5O1qNFixaZ8k8//TQGDhwY9erVi5o1a8Zpp50WS5YsyVrGwoULo0ePHlG9evVo0KBBDBkyJDZu3LivuwIAAAB7TZXybsCuHHbYYfHkk09mpqtU+U+TL7nkkpgwYUI88MADUbt27Rg0aFCceuqpMX369IiI2LRpU/To0SMKCwvjueeei0WLFkXfvn1jv/32i2uvvXaf9wUAAAD2hgof7qtUqRKFhYXbzF+5cmX8/ve/j/Hjx0eXLl0iImLMmDHRsmXLeP755+OYY46JSZMmxRtvvBFPPvlkFBQUxOGHHx7XXHNNDB06NIYPHx65ubn7ujsAAABQ5ir0afkREW+//XY0atQovv71r0efPn1i4cKFERExa9as2LBhQ3Tt2jVTt0WLFnHQQQfFjBkzIiJixowZ0bp16ygoKMjUKS4ujpKSkpgzZ84OX3PdunVRUlKS9QAAAICKqkKH+/bt28fYsWNj4sSJ8Zvf/CYWLFgQJ5xwQqxatSoWL14cubm5UadOnaznFBQUxOLFiyMiYvHixVnBfkv5lrIdGTlyZNSuXTvzaNy4cdl2DAAAAMpQhT4tv3v37pm/27RpE+3bt48mTZrE/fffH9WqVdtrrzts2LAYPHhwZrqkpETABwAAoMKq0Efut1anTp1o3rx5vPPOO1FYWBjr16+PFStWZNVZsmRJ5hr9wsLCbe6ev2V6e9fxb5GXlxf5+flZDwAAAKioUhXuV69eHfPnz4+GDRtGu3btYr/99ospU6ZkyufNmxcLFy6MoqKiiIgoKiqK1157LZYuXZqpM3ny5MjPz49WrVrt8/YDAADA3lChT8u/7LLLomfPntGkSZP48MMP46qrrorKlStH7969o3bt2tG/f/8YPHhw1K1bN/Lz8+MnP/lJFBUVxTHHHBMREd26dYtWrVrFmWeeGTfccEMsXrw4rrjiihg4cGDk5eWVc+8AAACgbFTocP/Pf/4zevfuHcuWLYv69evH8ccfH88//3zUr18/IiJuvvnmqFSpUpx22mmxbt26KC4ujttvvz3z/MqVK8djjz0WF1xwQRQVFUWNGjWiX79+MWLEiPLqEgAAAJS5Ch3u77333p2WV61aNUaPHh2jR4/eYZ0mTZrE448/XtZNAwAAgAojVdfcAwAAANsS7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUk64BwAAgJQT7gEAACDlhHsAAABIOeEeAAAAUu4rFe5Hjx4dBx98cFStWjXat28fL774Ynk3CQAAAL6wr0y4v++++2Lw4MFx1VVXxcsvvxxt27aN4uLiWLp0aXk3DQAAAL6Qr0y4v+mmm+Lcc8+Ns88+O1q1ahV33HFHVK9ePe6+++7ybhoAAAB8IVXKuwH7wvr162PWrFkxbNiwzLxKlSpF165dY8aMGdvUX7duXaxbty4zvXLlyoiIKCkp2e3X3rRu7R60mH1tTz7bPWWdSId9tU5YH9LDOsHWrBN8nv8l2Jp1gq3t7jqxpX6SJKWqn5OUtmaKffjhh3HAAQfEc889F0VFRZn5l19+eUybNi1eeOGFrPrDhw+Pq6++el83EwAAALJ88MEHceCBB+6y3lfiyP3uGjZsWAwePDgzvXnz5vj444+jXr16kZOTU44tK38lJSXRuHHj+OCDDyI/P7+8m0MFYJ1ga9YJPs/6wNasE2zNOsHWrBOfSZIkVq1aFY0aNSpV/a9EuP/a174WlStXjiVLlmTNX7JkSRQWFm5TPy8vL/Ly8rLm1alTZ282MXXy8/O/0hsa27JOsDXrBJ9nfWBr1gm2Zp1ga9aJiNq1a5e67lfihnq5ubnRrl27mDJlSmbe5s2bY8qUKVmn6QMAAEAafSWO3EdEDB48OPr16xdHHXVUfOtb34pbbrkl1qxZE2effXZ5Nw0AAAC+kK9MuP/hD38YH330UVx55ZWxePHiOPzww2PixIlRUFBQ3k1Llby8vLjqqqu2uWyBry7rBFuzTvB51ge2Zp1ga9YJtmad2DNfibvlAwAAwJfZV+KaewAAAPgyE+4BAAAg5YR7AAAASDnhvgI6+OCD45Zbbtlryx87dmzUqVNnry2fvevNN9+MY445JqpWrRqHH374due99957kZOTE7Nnzy7VMs8666w45ZRT9lqby9Pw4cMz71NpderUKS6++OK90p4vyv6B8vJl25a+iC9rv/aGqVOnRk5OTqxYsaLc2rC7+83dHUMrKuMFFYXx4z/2dr+E+33sgw8+iHPOOScaNWoUubm50aRJk7joooti2bJl+6wNP/zhD+Ott97KTO/JBrczOTk5O30MHz68zF5rT5R1kN3Xwfiqq66KGjVqxLx582LKlCnbnde4ceNYtGhRfPOb3yzVMn/1q1/F2LFjy7Sdu7te9erVK0466aSseRMnTtzuOjN8+PA46KCDSrXcyy67LPM+laWcnJx46KGHynSZ9g8Vb//wRbfvivCZlpWKsi2lLVBs/Y/cF/nH7qyzzoqcnJy47rrrsuY/9NBDkZOT8wVaWf5Wr14d++23X9x7771Z83v16hU5OTnx3nvvZc0/+OCD42c/+1mplj1z5sw477zzyqqpEVH+62FF2LcYL3Y+Xhg//sP4sWf2ZLwQ7vehd999N4466qh4++23409/+lO88847cccdd8SUKVOiqKgoPv74433SjmrVqkWDBg322vIXLVqUedxyyy2Rn5+fNe+yyy7ba6/9VTB//vw4/vjjo0mTJlGvXr3tzqtcuXIUFhZGlSql+7XL2rVrl/vOrnPnzjF9+vTYuHFjZt7TTz8djRs3jqlTp2bVffrpp6Nz586lWm7NmjUz71NFZv/w5ds/lOdnumHDhjJfZlq2pS+7qlWrxvXXXx/Lly8vs2WuX7++zJa1p2rWrBlHHXXUNvv7qVOnbjMOLFiwIN5///3o0qVLqZZdv379qF69ehm2tnwZL75848XWjB/sKeF+Hxo4cGDk5ubGpEmTomPHjnHQQQdF9+7d48knn4x//etf8b//+7+ZuqtWrYrevXtHjRo14oADDojRo0dnLWvFihUxYMCAqF+/fuTn50eXLl3iH//4R6b8H//4R3Tu3Dlq1aoV+fn50a5du3jppZciIvtbq7Fjx8bVV18d//jHPzLfhI4dOzbOOeec+O53v5v1mhs2bIgGDRrE73//+532s7CwMPOoXbt25OTkRGFhYdSqVSuaN28eEydOzKr/0EMPRY0aNWLVqlWZU+HuvffeOPbYY6Nq1arxzW9+M6ZNm5b1nNdffz26d+8eNWvWjIKCgjjzzDPj3//+d+k+iL2oU6dOceGFF8bll18edevWjcLCwqxvlpMkyRx1zsvLi0aNGsWFF16YKd/eN5B16tTJHFXPycmJWbNmxYgRIzLfWm9v3vZOKZwzZ05897vfjfz8/KhVq1accMIJMX/+/IjY9tvlzZs3x8iRI6Np06ZRrVq1aNu2bfz5z3/OlG85zXLKlClx1FFHRfXq1ePYY4+NefPmRcSO16ud6dy5c6xevTqznm55nZ/+9KfxwgsvxKeffhoREZ9++mm88MILmXC/q21h6yMJGzdujAsvvDDq1KkT9erVi6FDh0a/fv22+XZ98+bNO/wcDz744IiI+K//+q/IycnJTH8R9g9fvv1DaT7T//mf/4n27dtv89y2bdvGiBEjMtN33XVXtGzZMqpWrRotWrSI22+/PVO25X257777omPHjlG1atX44x//GPXr18/abg8//PBo2LBhZvrvf/975OXlxSeffBIRX55taWf9eOuttyInJyfefPPNrOfcfPPN0axZs8x0RVmHtqdr165RWFgYI0eO3GGdv/zlL3HYYYdFXl5eHHzwwTFq1Kis8oMPPjiuueaa6Nu3b+Tn58d5552X2fYfe+yxOPTQQ6N69erx/e9/Pz755JO455574uCDD479998/Lrzwwti0aVNmWf/3f/8XRx11VNSqVSsKCwvjjDPOiKVLl+5R3zp37pwV4ufOnRuffvppXHDBBVnzp06dGnl5eVFUVBQRn63LJ5xwQlSrVi0aN24cF154YaxZsyarv58/Pf3NN9+M448/PqpWrRqtWrWKJ598crvj77vvvhudO3eO6tWrR9u2bWPGjBmZ1z/77LNj5cqV5XIU2Xjx5Rsvtmb8+IzxYw8k7BPLli1LcnJykmuvvXa75eeee26y//77J5s3b06aNGmS1KpVKxk5cmQyb9685NZbb00qV66cTJo0KVO/a9euSc+ePZOZM2cmb731VnLppZcm9erVS5YtW5YkSZIcdthhyY9+9KNk7ty5yVtvvZXcf//9yezZs5MkSZIxY8YktWvXTpIkST755JPk0ksvTQ477LBk0aJFyaJFi5JPPvkkmT59elK5cuXkww8/zLzmX//616RGjRrJqlWrSt3vz7/Wln5+5zvfyarzve99L+nbt2+SJEmyYMGCJCKSAw88MPnzn/+cvPHGG8mAAQOSWrVqJf/+97+TJEmS5cuXJ/Xr10+GDRuWzJ07N3n55ZeTE088MencuXOp2tSvX7/k5JNPLnUfdmd5HTt2TPLz85Phw4cnb731VnLPPfckOTk5mc/ugQceSPLz85PHH388ef/995MXXngh+d3vfpdZVkQkDz74YNbya9eunYwZMyZJkiRZtGhRcthhhyWXXnppsmjRomTVqlXbnbflfXzllVeSJEmSf/7zn0ndunWTU089NZk5c2Yyb9685O67707efPPN7b4nP//5z5MWLVokEydOTObPn5+MGTMmycvLS6ZOnZokSZI8/fTTSUQk7du3T6ZOnZrMmTMnOeGEE5Jjjz02SZIdr1e70qhRo8w2UlJSklSpUiVZunRp0qJFi+Spp55KkiRJpkyZkkRE8t577yVJsutt4aqrrkratm2b1be6desmf/3rX5O5c+cm559/fpKfn5/V/119jkuXLk0iIhkzZkyyaNGiZOnSpbvs287YP/ynnxVt/7Cn+4vSfqavv/56EhHJO++8kynbMu/tt99OkiRJ/vjHPyYNGzZM/vKXvyTvvvtu8pe//CWpW7duMnbs2Kz35eCDD87U+fDDD5NTTz01GThwYJIkSfLxxx8nubm5Se3atZO5c+cmSfLZtnDcccdlXjct29LW683WdtWPo446KrniiiuyntOuXbvMvNKsQx07dkwuuuiiXbZ1e3V357lb27I+/vWvf02qVq2afPDBB0mSJMmDDz6YbPmX7qWXXkoqVaqUjBgxIpk3b14yZsyYpFq1aplxJEmSpEmTJkl+fn7yy1/+MnnnnXeSd955JxkzZkyy3377JSeeeGLy8ssvJ9OmTUvq1auXdOvWLTn99NOTOXPmJI8++miSm5ub3HvvvZll/f73v08ef/zxZP78+cmMGTOSoqKipHv37pnyLePF8uXLd9m/SZMmJRGR2beMHj066dGjR/L8888nTZo0ydQ788wzk06dOiVJkiTvvPNOUqNGjeTmm29O3nrrrWT69OnJEUcckZx11llZ/b355puTJEmSjRs3Joceemhy4oknJrNnz06effbZ5Fvf+lbW+Ltlm2rRokXy2GOPJfPmzUu+//3vJ02aNEk2bNiQrFu3LrnllluS/Pz8zL5xd/Z9X4Tx4j/9rOjjhfHD+FHW40dpCPf7yPPPP7/d4LbFTTfdlEREsmTJkqRJkybJSSedlFX+wx/+MDNYPvvss0l+fn7y6aefZtVp1qxZ8tvf/jZJkiSpVatWZsPd2tYr9tYb3BatWrVKrr/++sx0z549swbL0tj6tV544YWsnfySJUuSKlWqZELjlp3Mddddl3nOhg0bkgMPPDDTlmuuuSbp1q1b1ut88MEHSUQk8+bN22Wb9na4P/7447PKjz766GTo0KFJkiTJqFGjkubNmyfr16/f7rJ2Fe6TJEnatm2bXHXVVVl1tp63dbgfNmxY0rRp0x2+7uf78OmnnybVq1dPnnvuuaw6/fv3T3r37p0kyX/+WXvyyScz5RMmTEgiIlm7dm2SJDter3amT58+mc92woQJSatWrZIkSZLzzjsvufLKK5MkSZKf/exnSdOmTZMkKd22sHU7CgoKkhtvvDEzvXHjxuSggw7aZkDZ2eeYJNv/rPaU/cNnKuL+YU/3F7vzmbZt2zYZMWJEpmzYsGFJ+/btM9PNmjVLxo8fn/X8a665JikqKkqS5D/vyy233JJV59Zbb00OO+ywJEmS5KGHHkrat2+fnHzyyclvfvObJEk++yfmf/7nf5IkSde2tLN/zkrTj5tvvjlp1qxZpmzevHlJRGT+aS3NOlTe4T5JkuSYY45JzjnnnCRJssP9GWeckZx44olZzxsyZEhmf5okn4XdU045JavOmDFjtgkKP/7xj5Pq1atnhbDi4uLkxz/+8Q7bOHPmzCQiMs/ZnXC/Zs2aJDc3N7O+/+AHP0huuOGGZMOGDUmNGjWSd999N0mSJDnooIOSq6++OkmSz8am8847L2s5zz77bFKpUqXMePT5cP+3v/0tqVKlSrJo0aJM/cmTJ2833N91112ZOnPmzMlaT3YVEvYW48Vn0jBeGD+MH0lStuNHaTgtfx9LkqRU9bacavb56blz50bEZ6dIrV69OurVqxc1a9bMPBYsWJA5zXrw4MExYMCA6Nq1a1x33XWZ+btjwIABMWbMmIiIWLJkSfztb3+Lc845Z7eX83nf+ta34rDDDot77rknIiL++Mc/RpMmTaJDhw5Z9T7f/ypVqsRRRx2V1f+nn346q+8tWrSIiNijfpa1Nm3aZE03bNgwc4riD37wg1i7dm18/etfj3PPPTcefPDBrGvM95bZs2fHCSecEPvtt98u677zzjvxySefxIknnpj1Hv/hD3/Y5v39fF+3nK61p6djRnx2WcP06dNjw4YNMXXq1OjUqVNERHTs2DFzSubUqVMzp+SXZlv4vJUrV8aSJUviW9/6VmZe5cqVo127dtvU3dnnuLfYP3z59g+l+Uz79OkT48ePz9T/05/+FH369ImIiDVr1sT8+fOjf//+WX36+c9/vk1/jjrqqKzpjh07xhtvvBEfffRRTJs2LTp16hSdOnWKqVOnxoYNG+K5557LbGNflm2pNP3o1atXvPfee/H8889HRMS4cePiyCOPzKwnFW0d2pHrr78+7rnnnsy6v8XcuXPjuOOOy5p33HHHxdtvv511Ov3W60tERPXq1bNOLy0oKIiDDz44atasmTXv85/frFmzomfPnnHQQQdFrVq1omPHjhERsXDhwt3uU/Xq1ePoo4/O7O+3rLdVqlSJY489NqZOnRrvvvtuLFy4MGscGDt2bNbnVVxcHJs3b44FCxZs8xrz5s2Lxo0bR2FhYWbe59fjzyvrMa4sGS++fOPF1owfxo89Ubq7bfGFHXLIIZGTkxNz586N//qv/9qmfO7cubH//vtH/fr1d7ms1atXR8OGDbe56UxEZK59Gj58eJxxxhkxYcKE+Nvf/hZXXXVV3Hvvvdt97R3p27dv/PSnP40ZM2bEc889F02bNo0TTjih1M/fkQEDBsTo0aPjpz/9aYwZMybOPvvs3brL7+rVq6Nnz55x/fXXb1P2+euBysvWATonJyc2b94cERGNGzeOefPmxZNPPhmTJ0+O//7v/44bb7wxpk2bFvvtt1/k5ORsszMvixubVKtWrdR1V69eHREREyZMiAMOOCCrLC8vL2v6833d8hlu6eue6Ny5c6xZsyZmzpwZTz/9dAwZMiQiPhtkzjnnnPj444/jhRdeiB//+MeZtu5qW9hTO/scy5r9w398WfYPu/OZ9u7dO4YOHRovv/xyrF27Nj744IP44Q9/GBH/2R7vvPPOba6trFy5ctZ0jRo1sqZbt24ddevWjWnTpsW0adPiF7/4RRQWFsb1118fM2fOjA0bNsSxxx6beZ0vw7ZUmn4UFhZGly5dYvz48XHMMcfE+PHj44ILLshaRkVYh3alQ4cOUVxcHMOGDYuzzjprt5+/9foSsf3Pamef35o1a6K4uDiKi4tj3LhxUb9+/Vi4cGEUFxfv8U36OnfuHPfdd1/MmTMn1q5dG0ceeWREfDYOPP3007F58+aoXr16ZntYvXp1/PjHP866f80Wpf1VlR0p6zGuLBgv/uPLMl5szfjxH8aP3Sfc7yP16tWLE088MW6//fa45JJLssLW4sWLY9y4cdG3b9/MTmnLN0JbPP/889GyZcuIiDjyyCNj8eLFUaVKlZ3ePKJ58+bRvHnzuOSSS6J3794xZsyY7e4kcnNzs77N/3ybTznllBgzZkzMmDEjzj777D3p+jZ+9KMfxeWXXx633nprvPHGG9GvX79t6jz//POZb183btwYs2bNikGDBkXEZ/3/y1/+EgcffHCp7wZfkVSrVi169uwZPXv2jIEDB0aLFi3itddeiyOPPDLq168fixYtytR9++23Mzcr+SLatGkT99xzT2zYsGGXR+9btWoVeXl5sXDhwswRmD2xo/VqZ5o1axaNGzeORx55JGbPnp15/QMOOCAOOOCAGDVqVKxfvz5zxKa028IWtWvXjoKCgpg5c2Zm/dq0aVO8/PLLu/3zPfvtt99u929H7B/+48uyf9idz/TAAw+Mjh07xrhx42Lt2rVx4oknZu5AXVBQEI0aNYp33303czSmtHJycuKEE06Ihx9+OObMmRPHH398VK9ePdatWxe//e1v46ijjsr8Q/dl2ZZK248+ffrE5ZdfHr1794533303evXqlbWMirAOlcZ1110Xhx9+eBx66KGZeS1btozp06dn1Zs+fXo0b958m3/ov6g333wzli1bFtddd100btw4IiLrpqh7onPnzvHzn/88xo8fH8cff3ymzR06dIjf/e53kSRJHHfccZGbmxsRn31eb7zxRhxyyCGlWv6hhx4aH3zwQSxZsiQKCgoi4rOfyttdezLGlQXjxX98WcaLrRk/Ss/4sS2n5e9Dv/71r2PdunVRXFwczzzzTHzwwQcxceLEOPHEE+OAAw6IX/ziF5m606dPjxtuuCHeeuutGD16dDzwwANx0UUXRcRnd8otKiqKU045JSZNmhTvvfdePPfcc/G///u/8dJLL8XatWtj0KBBMXXq1Hj//fdj+vTpMXPmzMzOfGsHH3xwLFiwIGbPnh3//ve/Y926dZmyAQMGZE77295Oc0/sv//+ceqpp8aQIUOiW7duceCBB25TZ/To0fHggw/Gm2++GQMHDozly5dnTuEaOHBgfPzxx9G7d++YOXNmzJ8/P5544ok4++yzy2Wg3R1jx46N3//+9/H666/Hu+++G3/84x+jWrVq0aRJk4iI6NKlS/z617+OV155JV566aU4//zzS3Uq/a4MGjQoSkpKolevXvHSSy/F22+/Hf/3f/+Xubv959WqVSsuu+yyuOSSS+Kee+6J+fPnx8svvxy33XZb5vS30tjZerUznTt3jttvvz0OOeSQzD9eEZ8dtbntttuiefPm0ahRo4jY9bawPT/5yU9i5MiR8fDDD8e8efPioosuiuXLl+/2b0QffPDBMWXKlFi8eHGZ/CSV/cNnvkz7h935TPv06RP33ntvPPDAA9v8E3b11VfHyJEj49Zbb4233norXnvttRgzZkzcdNNNu2xDp06d4k9/+lMcfvjhUbNmzahUqVJ06NAhxo0bl/XlXdq2pU2bNsXs2bOzHnPnzi11P0499dRYtWpVXHDBBdG5c+fMPiWiYq1Du9K6devo06dP3HrrrZl5l156aUyZMiWuueaaeOutt+Kee+6JX//613vlZ8MOOuigyM3Njdtuuy3efffdeOSRR+Kaa675Qss89thjIy8vL2677basdfRb3/pWLF26NB5++OGsn0IdOnRoPPfcczFo0KCYPXt2vP322/Hwww9nAtzWTjzxxGjWrFn069cvXn311Zg+fXpcccUVERG7te4efPDBsXr16pgyZUr8+9//LpMv4kvLePGZL9N4sTXjR+kYP7Yl3O9D3/jGN+Kll16Kr3/963H66adHs2bN4rzzzovOnTvHjBkzom7dupm6l156abz00ktxxBFHxM9//vO46aabori4OCI+G3wef/zx6NChQ5x99tnRvHnz6NWrV7z//vtRUFAQlStXjmXLlkXfvn2jefPmcfrpp0f37t3j6quv3m67TjvttDjppJOic+fOUb9+/fjTn/6UKevatWs0bNgwiouLs1beL6p///6xfv36HV5zdd1118V1110Xbdu2jb///e/xyCOPxNe+9rWIiGjUqFFMnz49Nm3aFN26dYvWrVvHxRdfHHXq1IlKlSr2Kl2nTp24884747jjjos2bdrEk08+GY8++mjmtz9HjRoVjRs3jhNOOCHOOOOMuOyyy8rkt3nr1asXTz31VKxevTo6duwY7dq1izvvvHOHXxxcc8018bOf/SxGjhwZLVu2jJNOOikmTJgQTZs2LfVr7my92pnOnTvHqlWrMtdybdGxY8dYtWpV1j91u9oWtmfo0KHRu3fv6Nu3bxQVFWWuz6xatWqp+xbx2Wc1efLkaNy4cRxxxBG79dztsX/4jy/L/mF3PtPvf//7sWzZsvjkk0+2+SmgAQMGxF133RVjxoyJ1q1bR8eOHWPs2LGl2h47duwYmzZtytqeOnXqtM28tG1Lq1evjiOOOCLr0bNnz1L3o1atWtGzZ8/4xz/+sc0/wxVpHSqNESNGZJ2meuSRR8b9998f9957b3zzm9+MK6+8MkaMGLFHp+7vSv369WPs2LHxwAMPRKtWreK6666LX/7yl19omVWrVo1jjjlmm3EgLy8vM//z40CbNm1i2rRp8dZbb8UJJ5wQRxxxRFx55ZU73CdVrlw5HnrooVi9enUcffTRMWDAgMxPx+3OunvsscfG+eefHz/84Q+jfv36ccMNN+xZh/eA8eI/vizjxdaMH6Vj/NhWTlLaO3LwlbR69eo44IADYsyYMXHqqaeW2XL/7//+Ly655JL48MMPM6fWRXz2e5tNmzaNV155ZbdPzYE9sXnz5mjZsmWcfvrpX/iI01eN/QOfZ1siraZPnx7HH398vPPOO1k3FKTsGC/YGeNH2ak4F5hQoWzevDn+/e9/x6hRo6JOnTrxve99r0yW+8knn8SiRYviuuuuix//+MdZO2LYF95///2YNGlSdOzYMdatWxe//vWvY8GCBXHGGWeUd9NSw/6BCNsS6fXggw9GzZo14xvf+Ea88847cdFFF8Vxxx0n2O8Fxgu2x/ix91S888uoEBYuXBgFBQUxfvz4uPvuu7NuCrFw4cKsn3jY+rGzn7+54YYbokWLFlFYWBjDhg3bK+3e07axd40bN26Hn8thhx22z9pRqVKlGDt2bBx99NFx3HHHxWuvvRZPPvnkDq8hZFv2D0TsvW3psMMO2+FnNG7cuDJqfdl49tlnd7pOka179+47fK+uvfbafdaOVatWZW5oe9ZZZ8XRRx8dDz/88D57/a8S4wXbY/zYe+OH0/LZbRs3boz33ntvh+XleYfIity2r7pVq1bFkiVLtlu23377ZW4qSLpV5G2wIreN/3j//fd3+BOgBQUFUatWrX3coh1bu3Zt/Otf/9pheWnv4P5V8a9//SvWrl273bK6detmXUfMl19F3idX5LaxY8YP4R4AAABSz2n5AAAAkHLCPQAAAKSccA8AAAApJ9wDADF8+PBU/h701KlTIycnJ1asWPGFltOpU6e4+OKLy6RNAFAehHsASKGzzjorcnJy4rrrrsua/9BDD0VOTk5metOmTXHzzTdH69ato2rVqrH//vtH9+7dY/r06fu6yQDAXiTcA0BKVa1aNa6//vpYvnz5dsuTJIlevXrFiBEj4qKLLoq5c+fG1KlTo3HjxtGpU6d46KGH9m2DAYC9RrgHgJTq2rVrFBYWxsiRI7dbfv/998ef//zn+MMf/hADBgyIpk2bRtu2beN3v/tdfO9734sBAwbEmjVrsp7z29/+Nho3bhzVq1eP008/PVauXJlVftddd0XLli2jatWq0aJFi7j99tszZV26dIlBgwZl1f/oo48iNzc3pkyZEhER69ati8suuywOOOCAqFGjRrRv3z6mTp2aqf/+++9Hz549Y//9948aNWrEYYcdFo8//nim/PHHH4/mzZtHtWrVonPnztv8FvWyZcuid+/eccABB0T16tWjdevW8ac//Smrzpo1a6Jv375Rs2bNaNiwYYwaNWrnbzQApIBwDwApVbly5bj22mvjtttui3/+85/blI8fPz6aN28ePXv23Kbs0ksvjWXLlsXkyZMz89555524//7749FHH42JEyfGK6+8Ev/93/+dKR83blxceeWV8Ytf/CLmzp0b1157bfzsZz+Le+65JyIiBgwYEOPHj49169ZlnvPHP/4xDjjggOjSpUtERAwaNChmzJgR9957b7z66qvxgx/8IE466aR4++23IyJi4MCBsW7dunjmmWfitddei+uvvz5q1qwZEREffPBBnHrqqdGzZ8+YPXt2DBgwIH76059m9evTTz+Ndu3axYQJE+L111+P8847L84888x48cUXM3WGDBkS06ZNi4cffjgmTZoUU6dOjZdffnm3338AqFASACB1+vXrl5x88slJkiTJMccck5xzzjlJkiTJgw8+mGwZ3lu0aJGps7WPP/44iYjk+uuvT5IkSa666qqkcuXKyT//+c9Mnb/97W9JpUqVkkWLFiVJkiTNmjVLxo8fn7Wca665JikqKkqSJEnWrl2b7L///sl9992XKW/Tpk0yfPjwJEmS5P33308qV66c/Otf/8paxre//e1k2LBhSZIkSevWrTP1tzZs2LCkVatWWfOGDh2aRESyfPny7T4nSZKkR48eyaWXXpokSZKsWrUqyc3NTe6///5M+bJly5Jq1aolF1100Q6XAQAVXZVy/m4BAPiCrr/++ujSpUtcdtll25QlSVLq5Rx00EFxwAEHZKaLiopi8+bNMW/evKhVq1bMnz8/+vfvH+eee26mzsaNG6N27doR8dk9AM4888y4++674/TTT4+XX345Xn/99XjkkUciIuK1116LTZs2RfPmzbNed926dVGvXr2IiLjwwgvjggsuiEmTJkXXrl3jtNNOizZt2kRExNy5c6N9+/ZZzy0qKsqa3rRpU1x77bVx//33x7/+9a9Yv359rFu3LqpXrx4REfPnz4/169dnLadu3bpx6KGHlvp9AoCKSLgHgJTr0KFDFBcXx7Bhw+Kss87KzG/evHnMnTt3u8/ZMn/roL0jq1evjoiIO++8c5uAXbly5czfAwYMiMMPPzz++c9/xpgxY6JLly7RpEmTzDIqV64cs2bNynpORGROvR8wYEAUFxfHhAkTYtKkSTFy5MgYNWpU/OQnPylVO2+88cb41a9+Fbfccku0bt06atSoERdffHGsX7++VM8HgLRyzT0AfAlcd9118eijj8aMGTMy83r16hVvv/12PProo9vUHzVqVNSrVy9OPPHEzLyFCxfGhx9+mJl+/vnno1KlSnHooYdGQUFBNGrUKN5999045JBDsh5NmzbNPKd169Zx1FFHxZ133hnjx4+Pc845J1N2xBFHxKZNm2Lp0qXbLKOwsDBTr3HjxnH++efHX//617j00kvjzjvvjIiIli1bZl07v6WNnzd9+vQ4+eST40c/+lG0bds2vv71r8dbb72VKW/WrFnst99+8cILL2TmLV++PKsOAKSRI/cA8CXQunXr6NOnT9x6662Zeb169YoHHngg+vXrFzfeeGN8+9vfjpKSkhg9enQ88sgj8cADD0SNGjUy9atWrRr9+vWLX/7yl1FSUhIXXnhhnH766ZngffXVV8eFF14YtWvXjpNOOinWrVsXL730UixfvjwGDx6cWc6AAQNi0KBBUaNGjfiv//qvzPzmzZtHnz59om/fvjFq1Kg44ogj4qOPPoopU6ZEmzZtokePHnHxxRdH9+7do3nz5rF8+fJ4+umno2XLlhERcf7558eoUaNiyJAhMWDAgJg1a1aMHTs26334xje+EX/+85/jueeei/333z9uuummWLJkSbRq1SoiPjtDoH///jFkyJCoV69eNGjQIP73f/83KlVyvAOAdDOSAcCXxIgRI2Lz5s2Z6ZycnLj//vvjf/7nf+Lmm2+OQw89NE444YR4//33Y+rUqXHKKadkPf+QQw6JU089Nb7zne9Et27dok2bNlk/dTdgwIC46667YsyYMdG6devo2LFjjB07NuvIfURE7969o0qVKtG7d++oWrVqVtmYMWOib9++cemll8ahhx4ap5xySsycOTMOOuigiPjsmvmBAwdGy5Yt46STTormzZtn2nDQQQfFX/7yl3jooYeibdu2cccdd8S1116btfwrrrgijjzyyCguLo5OnTpFYWHhNv288cYb44QTToiePXtG165d4/jjj4927drt0XsOABVFTrI7d9oBANiF9957L5o1axYzZ86MI488srybAwBfCcI9AFAmNmzYEMuWLYvLLrssFixYENOnTy/vJgHAV4bT8gGAMjF9+vRo2LBhzJw5M+64447ybg4AfKU4cg8AAAAp58g9AAAApJxwDwAAACkn3AMAAEDKCfcAAACQcsI9AAAApJxwDwAAACkn3AMAAEDKCfcAAACQcsI9AAAApNz/A2nMHtB71RdwAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 1200x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(12, 8))\n","sns.countplot(data=train_data, x='NObeyesdad');"]},{"cell_type":"markdown","metadata":{"id":"DurumFnj5CWK"},"source":["- **Note:** There is imbalance between classes in target column."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"executionInfo":{"elapsed":963,"status":"ok","timestamp":1710701475381,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"8E59aBEs5CWK","outputId":"f43eb831-2dd0-4791-b9df-90a03295c039"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.histplot(data=train_data, x='Age');"]},{"cell_type":"markdown","metadata":{"id":"wzlkXOux5CWK"},"source":["- There is **skewness** in age **column**\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":599},"executionInfo":{"elapsed":1493,"status":"ok","timestamp":1710701477817,"user":{"displayName":"Eman 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\n","text/plain":["<Figure size 1200x700 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(12, 7))\n","\n","freq, bins, patches = plt.hist(train_data['Age'], bins=[0, 10, 20, 30, 40, 50, 60], rwidth=.9)\n","bin_centers = np.diff(bins)*0.5 + bins[:-1]\n","\n","n = 0\n","for fr, x, patch in zip(freq, bin_centers, patches):\n","    height = int(freq[n])\n","    plt.annotate(\"{}%\".format(round(height*100 / train_data.shape[0], 2)),\n","               xy = (x, height),\n","               xytext = (0,0.2),\n","               textcoords = \"offset points\",\n","               ha = 'center', va = 'bottom'\n","               )\n","    n = n+1\n","\n","plt.xticks([0, 10, 20, 30, 40, 50, 60]);"]},{"cell_type":"markdown","metadata":{"id":"AG2SoBxj5CWL"},"source":["- Nearly **61%** of people in the data have age from 20 to 30 (young)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"executionInfo":{"elapsed":737,"status":"ok","timestamp":1710701536518,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"woCndqQr5CWL","outputId":"4cec5661-28da-43bb-c78e-69f2a73e66ac"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.histplot(data=train_data, x='Height');"]},{"cell_type":"markdown","metadata":{"id":"HlLteSHU5CWL"},"source":["- **Height** is approximately normal distribution."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"executionInfo":{"elapsed":460,"status":"ok","timestamp":1710701539340,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"-iKdeU9J5CWL","outputId":"39210388-5900-4cc0-c0a6-4cbf9be26b26"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.histplot(data=train_data, x='Weight');"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":599},"executionInfo":{"elapsed":509,"status":"ok","timestamp":1710701561252,"user":{"displayName":"Eman 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\n","text/plain":["<Figure size 1200x700 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(12, 7))\n","\n","freq, bins, patches = plt.hist(train_data['Weight'], bins=[0, 20, 50, 70, 90, 120, 160], rwidth=.9)\n","bin_centers = np.diff(bins)*0.5 + bins[:-1]\n","\n","n = 0\n","for fr, x, patch in zip(freq, bin_centers, patches):\n","    height = int(freq[n])\n","    plt.annotate(\"{}%\".format(round(height*100 / train_data.shape[0], 2)),\n","               xy = (x, height),\n","               xytext = (0,0.2),\n","               textcoords = \"offset points\",\n","               ha = 'center', va = 'bottom'\n","               )\n","    n = n+1\n","\n","plt.xticks([0, 20, 50, 70, 90, 120, 160]);"]},{"cell_type":"markdown","metadata":{"id":"vWtKNXD0wyO1"},"source":["- There is skewness in weight column.\n","- Nearly 31% of people in dataset have weight from 90 to 120"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":696},"executionInfo":{"elapsed":594,"status":"ok","timestamp":1710701736920,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"otNvMTI75CWL","outputId":"02290385-1dc8-4fb2-f4d0-6fbc4073e972"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 1200x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(12, 8))\n","sns.countplot(data=train_data, x='MTRANS');"]},{"cell_type":"markdown","metadata":{"id":"4e45LQmv5CWM"},"source":["- The **most** used one is **Public Transportation** and the **least** one is **Bike**."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"executionInfo":{"elapsed":476,"status":"ok","timestamp":1710701751546,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"WLHAKRDn5CWM","outputId":"f1f6983b-9fe4-4f25-ab1d-f142eb631563"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(data=train_data, x='CALC');"]},{"cell_type":"markdown","metadata":{"id":"tQNwkk9g5CWM"},"source":["- **Note:** The **most** of people is **sometimes** using alcohol and the **least** ones who is using **frequantly**."]},{"cell_type":"markdown","metadata":{"id":"BYPKJww05CWM"},"source":["#### What is the range of ages in each category in the target"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":506},"executionInfo":{"elapsed":1139,"status":"ok","timestamp":1710701835991,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"FacVtH-A5CWM","outputId":"581f5c51-c5e1-4890-a437-724a6600fa0f"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 511.111x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.catplot(data=train_data, x='Age', y='NObeyesdad');"]},{"cell_type":"markdown","metadata":{"id":"Wy218xTv5CWM"},"source":["- **Insufficient Weight** and **Obesity Type |||** appear nearly for ages from 18 to 30."]},{"cell_type":"markdown","metadata":{"id":"eJpf-9x95CWN"},"source":["#### What is the relation between weight and target\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":506},"executionInfo":{"elapsed":1096,"status":"ok","timestamp":1710701932167,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"vm3nl3W35CWN","outputId":"54fbbf40-b460-45cf-d180-0044306b526d"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 511.111x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.catplot(data=train_data, x='Weight', y='NObeyesdad');"]},{"cell_type":"markdown","metadata":{"id":"Re8HZWRm5CWN"},"source":["- Insufficient weight have low weights from 40 to 80.\n","- Obesity Type | have high weights from 70 to 130\n","- Obesity Type || have higher weights from 80 to 140\n","- Obesity Type ||| have higher weights from 100 to 170"]},{"cell_type":"markdown","metadata":{"id":"ZD9ZYXs45CWN"},"source":["#### What is the relation between height and target"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":506},"executionInfo":{"elapsed":1170,"status":"ok","timestamp":1710701896168,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"7mQp8E8T5CWN","outputId":"521714c1-3c3b-4ddc-eda6-8981d1b0cce2"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 511.111x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.catplot(data=train_data, x='Height', y='NObeyesdad');"]},{"cell_type":"markdown","metadata":{"id":"GxKefOwg5CWN"},"source":["- Here almost Heights have all categories.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"qsvl_7eM5CWO"},"source":["#### How many people in each cluster of weight drink ?"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8Dd2NZJP5CWO"},"outputs":[],"source":["pivot_mean_df = pd.pivot_table(train_data, values='CH2O', index='NObeyesdad', aggfunc='mean')\n","pivot_mean_df = pivot_mean_df.reset_index()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"executionInfo":{"elapsed":429,"status":"ok","timestamp":1710702187686,"user":{"displayName":"Eman 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\n","text/plain":["<Figure size 1200x1200 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(12, 12))\n","sns.heatmap(train_data.corr(), annot=True);"]},{"cell_type":"markdown","metadata":{"id":"3OsRhPDa5CWU"},"source":["- There is **no strong correlation** between 2 columns and this is good for model to learn variability in data."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2j0bmqKt5CWU","outputId":"a4915940-9b05-439c-8087-be7afe9cbaeb"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Gender</th>\n","      <th>Age</th>\n","      <th>Height</th>\n","      <th>Weight</th>\n","      <th>family_history_with_overweight</th>\n","      <th>FAVC</th>\n","      <th>FCVC</th>\n","      <th>NCP</th>\n","      <th>CAEC</th>\n","      <th>SMOKE</th>\n","      <th>CH2O</th>\n","      <th>SCC</th>\n","      <th>FAF</th>\n","      <th>TUE</th>\n","      <th>CALC</th>\n","      <th>MTRANS</th>\n","      <th>NObeyesdad</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>18741</th>\n","      <td>Male</td>\n","      <td>38.895069</td>\n","      <td>1.7</td>\n","      <td>79.843221</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>3.0</td>\n","      <td>2.938135</td>\n","      <td>Sometimes</td>\n","      <td>no</td>\n","      <td>1.718569</td>\n","      <td>no</td>\n","      <td>2.834373</td>\n","      <td>0.0</td>\n","      <td>Sometimes</td>\n","      <td>Automobile</td>\n","      <td>Overweight_Level_II</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      Gender        Age  Height     Weight family_history_with_overweight  \\\n","18741   Male  38.895069     1.7  79.843221                            yes   \n","\n","      FAVC  FCVC       NCP       CAEC SMOKE      CH2O SCC       FAF  TUE  \\\n","18741  yes   3.0  2.938135  Sometimes    no  1.718569  no  2.834373  0.0   \n","\n","            CALC      MTRANS           NObeyesdad  \n","18741  Sometimes  Automobile  Overweight_Level_II  "]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["train_data.sample()"]},{"cell_type":"markdown","metadata":{"id":"3c10yMPh5CWU"},"source":["- Distribution of Numerical Values and do bins and make conclusion from it.\n","- Categories for each categorical column."]},{"cell_type":"markdown","metadata":{"id":"2QvUT86XQ8Rt"},"source":["## Note:\n","- Here We have done preprocessing and models using steps.\n","- Finally We have used **Pipeline** contains the final sequance."]},{"cell_type":"markdown","metadata":{"id":"PwhK9uJW5CWU"},"source":["## Data Preparation:"]},{"cell_type":"markdown","metadata":{"id":"gxkgQ8-T5CWU"},"source":["**Correct columns values**"]},{"cell_type":"code","execution_count":7,"metadata":{"executionInfo":{"elapsed":287,"status":"ok","timestamp":1710711918120,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"Pm11eKGO5CWU"},"outputs":[],"source":["train_data['Age'] = train_data['Age'].astype(int)\n","train_data[['Height', 'Weight', 'CH2O']] = train_data[['Height', 'Weight', 'CH2O']].round(2)\n","train_data[['FCVC', 'NCP', 'TUE', 'FAF']] = train_data[['FCVC', 'NCP', 'TUE', 'FAF']].round().astype(int)"]},{"cell_type":"markdown","metadata":{"id":"ERnxt5bA5CWU"},"source":["**Split Data into X and y**"]},{"cell_type":"code","execution_count":8,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1710711919222,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"jidbY6c35CWU"},"outputs":[],"source":["X = train_data.drop(columns='NObeyesdad', axis=1)\n","y = train_data['NObeyesdad']"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1710711919996,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"28Bj8WKF5CWV","outputId":"fb16ff6d-41b8-4079-f7cc-bb4f8d9e7ee7"},"outputs":[{"output_type":"stream","name":"stdout","text":["(16606, 17)\n","(16606,)\n"]}],"source":["print(X.shape)\n","print(y.shape)"]},{"cell_type":"markdown","metadata":{"id":"sL-oN7395CWV"},"source":["**Encode String to Numerical**"]},{"cell_type":"markdown","metadata":{"id":"-48o0WU-5CWV"},"source":["- Features"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":411,"status":"ok","timestamp":1710711923793,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"c8_mox095CWV"},"outputs":[],"source":["categorical = X[['Gender', 'family_history_with_overweight', 'FAVC', 'CAEC', 'SMOKE', 'SCC', 'CALC', 'MTRANS']]"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1710711924107,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"qNhyLBvL5CWV","outputId":"dcf12a88-540e-434d-97e0-b663be6a5623"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(16606, 8)"]},"metadata":{},"execution_count":11}],"source":["categorical.shape"]},{"cell_type":"code","execution_count":13,"metadata":{"executionInfo":{"elapsed":296,"status":"ok","timestamp":1710711939694,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"vvSqzZWM5CWW"},"outputs":[],"source":["encoder = OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1710711940984,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"yPhwRgFK5CWW","outputId":"c26c4377-7eb9-43ed-8eed-147fd246f676"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)"],"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":14}],"source":["encoder.fit(categorical)"]},{"cell_type":"code","execution_count":15,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1710711941646,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"J-I0PGQ15CWW"},"outputs":[],"source":["X[['Gender', 'family_history_with_overweight', 'FAVC', 'CAEC', 'SMOKE', 'SCC', 'CALC', 'MTRANS']] = encoder.transform(X[['Gender', 'family_history_with_overweight', 'FAVC', 'CAEC', 'SMOKE', 'SCC', 'CALC', 'MTRANS']])"]},{"cell_type":"markdown","metadata":{"id":"WQiGmqFQ5CWW"},"source":["- Target"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1710711942949,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"_Tkck0A65CWW","outputId":"d542d332-02ac-4ede-a9ea-56e570a7282b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["LabelEncoder()"],"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LabelEncoder</label><div class=\"sk-toggleable__content\"><pre>LabelEncoder()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":16}],"source":["target_encoder = LabelEncoder()\n","target_encoder.fit(y)"]},{"cell_type":"code","execution_count":17,"metadata":{"executionInfo":{"elapsed":441,"status":"ok","timestamp":1710711962930,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"zD2s94EN5CWX"},"outputs":[],"source":["target = target_encoder.transform(y)"]},{"cell_type":"code","execution_count":18,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1710711963515,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"viUPw8MI5CWX"},"outputs":[],"source":["y = target"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1710711963870,"user":{"displayName":"Eman 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\"std\": null,\n        \"min\": 2.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMOKE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CH2O\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SCC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        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oversample.fit_resample(X, y)"]},{"cell_type":"markdown","metadata":{"id":"bE8O3qNv5CWY"},"source":["## Test Data"]},{"cell_type":"code","execution_count":24,"metadata":{"id":"f3UT-HKDTzTQ","executionInfo":{"status":"ok","timestamp":1710711974369,"user_tz":-120,"elapsed":2,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["test.drop(columns='id', inplace=True)"]},{"cell_type":"code","execution_count":25,"metadata":{"id":"w8CyjonF5CWZ","executionInfo":{"status":"ok","timestamp":1710711975288,"user_tz":-120,"elapsed":3,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["test[['Gender', 'family_history_with_overweight', 'FAVC', 'CAEC', 'SMOKE', 'SCC', 'CALC', 'MTRANS']] = encoder.transform(test[['Gender', 'family_history_with_overweight', 'FAVC', 'CAEC', 'SMOKE', 'SCC', 'CALC', 'MTRANS']])"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1710711976108,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"qhXY0xPd5CWZ","outputId":"cc38a5d6-52bd-48f9-8dc0-eb47647f39ea"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Gender        Age    Height      Weight  family_history_with_overweight  \\\n","0     1.0  26.899886  1.848294  120.644178                             1.0   \n","1     0.0  21.000000  1.600000   66.000000                             1.0   \n","2     0.0  26.000000  1.643355  111.600553                             1.0   \n","3     1.0  20.979254  1.553127  103.669116                             1.0   \n","4     0.0  26.000000  1.627396  104.835346                             1.0   \n","\n","   FAVC      FCVC       NCP  CAEC  SMOKE      CH2O  SCC       FAF       TUE  \\\n","0   1.0  2.938616  3.000000   2.0    0.0  2.825629  0.0  0.855400  0.000000   \n","1   1.0  2.000000  1.000000   2.0    0.0  3.000000  0.0  1.000000  0.000000   \n","2   1.0  3.000000  3.000000   2.0    0.0  2.621877  0.0  0.000000  0.250502   \n","3   1.0  2.000000  2.977909   2.0    0.0  2.786417  0.0  0.094851  0.000000   \n","4   1.0  3.000000  3.000000   2.0    0.0  2.653531  0.0  0.000000  0.741069   \n","\n","   CALC  MTRANS  \n","0   1.0     3.0  \n","1   1.0     3.0  \n","2   1.0     3.0  \n","3   1.0     3.0  \n","4   1.0     3.0  "],"text/html":["\n","  <div id=\"df-000ae801-131e-451e-bb0e-e043b58a982f\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" 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2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FAVC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28736861669367647,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FCVC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5316064424734112,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 828,\n        \"samples\": [\n          2.923433,\n          1.813234\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NCP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7109268631672955,\n        \"min\": 1.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 649,\n        \"samples\": [\n          3.648194,\n          2.157164\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CAEC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4570497142748982,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMOKE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1133029460940065,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CH2O\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6112295139643,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1366,\n        \"samples\": [\n          1.152899,\n          2.232601\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SCC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1800121092176261,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FAF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8403611101051178,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1260,\n        \"samples\": [\n          0.68183,\n          1.554817\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TUE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.608005301939806,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 1172,\n        \"samples\": [\n          1.281141,\n          0.66488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CALC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4766478649662603,\n        \"min\": -1.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.0,\n          -1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MTRANS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1561855180695828,\n        \"min\": 0.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":26}],"source":["test.head()"]},{"cell_type":"markdown","metadata":{"id":"9ILs7Oe25CWZ"},"source":["## Split data into train and test:"]},{"cell_type":"code","execution_count":27,"metadata":{"executionInfo":{"elapsed":408,"status":"ok","timestamp":1710711981172,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"xd6uQ6s75CWZ"},"outputs":[],"source":["X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=42)"]},{"cell_type":"code","execution_count":28,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1710711981455,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"5wg3_ZVl5CWZ"},"outputs":[],"source":["def model_prediction(model):\n","    model.fit(X_train, y_train)\n","    y_pred_train = model.predict(X_train)\n","    y_pred_test = model.predict(X_test)\n","    train_accuracy = accuracy_score(y_train, y_pred_train)\n","    test_accuracy = accuracy_score(y_test, y_pred_test)\n","\n","    print(f'Training accuracy: {train_accuracy}')\n","    print(\"train Classification Report:\")\n","    print(classification_report(y_train, y_pred_train))\n","    print(\"**************************************************************************\")\n","    print(f'Testing accuracy: {test_accuracy}')\n","    print(\"Test Classification Report:\")\n","    print(classification_report(y_test, y_pred_test))"]},{"cell_type":"markdown","metadata":{"id":"GqpBuq-05CWZ"},"source":["## SVC Model:"]},{"cell_type":"code","execution_count":71,"metadata":{"id":"DldLXq455CWZ","executionInfo":{"status":"ok","timestamp":1710718917371,"user_tz":-120,"elapsed":677,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["svc = SVC()"]},{"cell_type":"code","execution_count":72,"metadata":{"id":"quPhHZ6H5CWa","colab":{"base_uri":"https://localhost:8080/","height":75},"executionInfo":{"status":"ok","timestamp":1710718956346,"user_tz":-120,"elapsed":38274,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}},"outputId":"52df9d43-a43f-4292-98ea-bdf60a50a23e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["SVC()"],"text/html":["<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":72}],"source":["svc.fit(X_train, y_train)"]},{"cell_type":"markdown","metadata":{"id":"zKlfyKLV5CWa"},"source":["**Training**"]},{"cell_type":"code","execution_count":73,"metadata":{"id":"5UN9OdUS5CWa","executionInfo":{"status":"ok","timestamp":1710718980353,"user_tz":-120,"elapsed":24014,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["y_pred_train = svc.predict(X_train)"]},{"cell_type":"code","execution_count":74,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"wOi9YwXD5CWa","outputId":"16801729-3a2e-4ff3-8cae-4d3ea1003c30","executionInfo":{"status":"ok","timestamp":1710718981385,"user_tz":-120,"elapsed":1065,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":74},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["sns.heatmap(confusion_matrix(y_train, y_pred_train), annot=True)"]},{"cell_type":"code","execution_count":75,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kusRJZiR5CWa","outputId":"78aa9cc4-7c78-4013-84c2-8526f48cd802","executionInfo":{"status":"ok","timestamp":1710718981385,"user_tz":-120,"elapsed":16,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.15      0.42      0.22      2587\n","           1       0.15      0.12      0.13      2580\n","           2       0.15      0.38      0.22      2585\n","           3       0.00      0.00      0.00      2600\n","           4       0.00      0.00      0.00      2614\n","           5       0.15      0.14      0.14      2604\n","           6       0.16      0.02      0.03      2585\n","\n","    accuracy                           0.15     18155\n","   macro avg       0.11      0.15      0.11     18155\n","weighted avg       0.11      0.15      0.11     18155\n","\n"]}],"source":["print(classification_report(y_train, y_pred_train))"]},{"cell_type":"markdown","metadata":{"id":"TD5KbksK5CWa"},"source":["**Testing**"]},{"cell_type":"code","execution_count":76,"metadata":{"id":"XAolnDuS5CWb","executionInfo":{"status":"ok","timestamp":1710718986249,"user_tz":-120,"elapsed":4875,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["y_pred_test = svc.predict(X_test)"]},{"cell_type":"code","execution_count":77,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"CF5yad1_5CWb","outputId":"6e3603af-e308-4663-f5b3-e752bcf314ba","executionInfo":{"status":"ok","timestamp":1710718987245,"user_tz":-120,"elapsed":1030,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":77},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["sns.heatmap(confusion_matrix(y_test, y_pred_test), annot=True)"]},{"cell_type":"code","execution_count":78,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QN9GjKnJ5CWb","outputId":"8e38c1a0-c37f-4399-dd83-d4c9320c3f8d","executionInfo":{"status":"ok","timestamp":1710718987246,"user_tz":-120,"elapsed":16,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.15      0.40      0.22       655\n","           1       0.17      0.12      0.14       662\n","           2       0.16      0.38      0.22       657\n","           3       0.00      0.00      0.00       642\n","           4       0.00      0.00      0.00       628\n","           5       0.16      0.15      0.15       638\n","           6       0.15      0.02      0.03       657\n","\n","    accuracy                           0.15      4539\n","   macro avg       0.11      0.15      0.11      4539\n","weighted avg       0.11      0.15      0.11      4539\n","\n"]}],"source":["print(classification_report(y_test, y_pred_test))"]},{"cell_type":"markdown","metadata":{"id":"zgDOSJzy5CWb"},"source":["## Hyperparameter Tuning:"]},{"cell_type":"code","execution_count":79,"metadata":{"id":"pKTe9MUN5CWb","executionInfo":{"status":"ok","timestamp":1710719145103,"user_tz":-120,"elapsed":5,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["param_grid = {\n","    'C': [0.1, 1, 10, 100],\n","    'kernel': ['linear', 'rbf'],\n","    'gamma': [1, 0.1, 0.01, 0.001]\n","}"]},{"cell_type":"code","execution_count":80,"metadata":{"id":"KSGP6NI55CWb","executionInfo":{"status":"ok","timestamp":1710719146289,"user_tz":-120,"elapsed":4,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["grid = GridSearchCV(SVC(), param_grid, refit=True, verbose=2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bBRzgtrS5CWb"},"outputs":[],"source":["grid.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pPgsqQ145CWc"},"outputs":[],"source":["grid.score(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M6WF8zab5CWc"},"outputs":[],"source":["grid.best_params_"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wZl2zucv5CWc"},"outputs":[],"source":["grid.best_score_"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qqPitV7L5CWc"},"outputs":[],"source":["pred = grid.predict(X_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8LWIhDaV5CWc"},"outputs":[],"source":["accuracy_score(y_test, pred)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aFAZWNmS5CWc"},"outputs":[],"source":["print(classification_report(y_test, pred))"]},{"cell_type":"markdown","metadata":{"id":"6mz9lx-35CWd"},"source":["## Decision Tree Model:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qJjgRvuN5CWd"},"outputs":[],"source":["dt = DecisionTreeClassifier()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1710706539159,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"vw6KTBCK5CWd","outputId":"896d9746-7c36-4003-aeab-9528e87e2b70"},"outputs":[{"data":{"text/plain":["DecisionTreeClassifier()"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["dt.fit(X_train, y_train)"]},{"cell_type":"markdown","metadata":{"id":"lh_heEj-5CWd"},"source":["**Training**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rqhB7k805CWd"},"outputs":[],"source":["y_pred_train = dt.predict(X_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"executionInfo":{"elapsed":522,"status":"ok","timestamp":1710706542688,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"Huwy4LHv5CWd","outputId":"82bc7ea3-7029-406f-aa25-8a2174a83b68"},"outputs":[{"data":{"text/plain":["<AxesSubplot:>"]},"execution_count":28,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAWcAAAD4CAYAAAAw/yevAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAyy0lEQVR4nO3dfXwU1b3H8c9v82BIgKACeZYYoGmxV1QUrGgF5VGEiNgIV57BUEEIiIiIFblXfKK0lfpwS9Wi3lpLa70IRgRRRFoB0aJIYoRAlIQlAWp4SCDZ3Zz7R4Y0gZBskk12svm9fc2LzZmZPd/NxJOTM2dmxBiDUkope3H4O4BSSqlzaeOslFI2pI2zUkrZkDbOSillQ9o4K6WUDQU3dQWugmxbTgdpk3CTvyMopc7iLsuXxr6H68g+r9uckI5Jja6vqWjPWSmlbKjJe85KKdWsyj3+TuAT2jgrpQKLx+3vBD6hjbNSKqAYU+7vCD6hjbNSKrCUa+OslFL2EyA9Z52toZQKLOUe75daiEiCiHwoIlkisltE0q3yR0UkX0R2WsstVfZZICJ7RSRbRAZXKe8lIrusdctFpM4pfNpzVkoFFt/1nN3AXGPM5yLSDvhMRDZY635tjPll1Y1FpAcwGrgMiAXeF5EfGGM8wAtAGrAVyACGAO/WVrk2zkqpgGJ8NFvDGOMEnNbrEyKSBcTVsksK8IYxphTYLyJ7gd4ikgu0N8Z8AiAirwK3UUfjrMMaSqnAUl7u9SIiaSKyo8qSVtNbikgicCWwzSq6V0S+FJGXReRCqywOOFBltzyrLM56fXZ5rbRxVkoFFlPu9WKMWWGMubrKsuLstxORtsCbwGxjzHEqhii6AldQ0bNedmbTmtLUUl6rJmuck5OTE5KTkz8cPnY6KeNn8Npf3q5xu+3/3MWoyemkjJ/BxJkLGl1vWZmLuYueZuiYNMZMu598ZwEABw8Vkjp1TmVd7dv5ZkRn8KB+7P5qM19nbuGBeTN88p6+YNdcYN9smqt+7JrLVycEAUQkhIqG+Y/GmL8BGGMKjDEeUzGh+vdAb2vzPCChyu7xwEGrPL6G8trrbqrHVCUnJ8cAMV9tfvuz4pISUqfex/LHH6Jr4iWV2xw/cZKx0+fzu18+SkxUJ45+X8TFF3bw6v3znQUsfOIZVi5/vFr5G29lkJ2Ty6L7p5OxcTMbN29l2eIHcLlcGAOhoSGUlJziypt/Rr7zFB5Pwz+/w+Ega/fHDLllDHl5TrZ+ksHYcdPJytrT4Pf0BbvmsnM2zWWPXL648VFp1ode/099wY/6n7c+a0bFK8C/jDGzq5THWOPRiMgcoI8xZrSIXAa8TkVjHQtsBLobYzwi8ikwk4phkQzgt8aYjNqy1dlzFpEfish8a/rHM9brH9W1X3Z2tjM7O/tzgIjwcJK6xFNw+Gi1bTLe38yAn/6EmKhOANUa5jXrP2R02lxGTU5n8dLn8Hi8u17+gy3bSBlScce5QTf2ZdvnX2CMISQkhNDQEADKXK6a/9Cop97XXElOTi7793+Hy+Vi1arVjBg+uO4dm5hdc4F9s2muwMgFVFy+7e1Su77AOOCms6bNPW1Ni/sS6A/MATDG7AZWAZnAOmCGNVMD4B7gRWAvkEMdJwOhjsZZROYDb1DRlG0HPrVe/0lEHqzrzc/IdxaQtWcfl/dIrlaeeyCf4ydOMnHWQ6ROncPqdR8AkJN7gHUfbOG155/izZefwRHkYO2Gj7yqq/DIUaI7dwQgODiIthERFB07AYCz4DAjJ85kwB2TKSpyNarXDBAbF82BvH//dZKX7yQ2NrpR7+kLds0F9s2muerHrrmAep0QrI0xZosxRowxlxtjrrCWDGPMOGPMf1jlI870oq19lhhjuhpjko0x71Yp32GM+bG17l7jxZBFXQOvU4DLjDGuqoUi8itgN/BkTTtZZzzTAH7z+MNs+sdnzJ85lbYR4dW283g8ZH6zlxd//RilpWXcdc88el6WzLbPviAzO4fRaXMBKC0t46IOkQDMWvg4+c4CXC43zsLDjJqcDsDYO4Yz8pYB1PSZz0z3jonqxFsrf0vhkaNcN2w8xcVuPOUNb6Brmkduh6eZ2zUX2Deb5qofu+YC+HdntWWrq3Eup2Ls5NuzymOsdTWyzniuSE5ODtm5e0/ZsIE3MvDG687ZLqpTRzpEtie8TRjhbcLo1fMysvfuxwAjhvRnzrQJ5+yzfMlDwPnHnKM6deRQ4RGiO3fE7fZwsriYyPbtqm3TuePFlJWVExbmoLik4QcyP89JQnxs5dfxcTE4rROQ/mTXXGDfbJqrfuyaC2g1l2/PBjaKyLsissJa1lEx0J1e247JyckCvJTUJZ4Jd95W4zb9r+/D519m4nZ7OHW6lF1Z35DUJYFre13Ohk3/4Oj3RQAcO36Cg4cKvfpA/fv2rhweWf/R3+lz1eWICIcKj3C6tLTi/U6cJCwsCJercb/pP92xk27dLiUxMYGQkBBSU1NYs3Z9o97TF+yaC+ybTXMFRi7AZ8Ma/lZrz9kYs05EfkDF2cc4Ksab84BPTd1/O/QFxm37/MvKoYf0u8fhLDwMwJ0pQ+mamEDfPldx+6RZOBzCqGED6Z7UBYCZU8eSNncR5eXlhAQHs3DONGKjO9f5gW4fNpAFS37F0DFpRLZrx9JH5wGw79sDLH3uZUQEYwxFx8ooczXu4Hg8HtJnP0zGO68T5HCw8pU/k5n5TaPe0xfsmgvsm01zBUYuIGB6zk02le4MfYagUspbvphKd3r7X7xuc8J6/8y2zxDUe2sopQKLzYcrvKWNs1IqsATIsIY2zkqpwKI9Z6WUsiFtnJVSyn6Mx1X3Ri2ANs5KqcCiY85KKWVDOqyhlFI2pD1npZSyIe05K6WUDWnP2Tt2vUz61Lfv+ztCjdp0GeDvCEq1bG7fPH3b37TnrJQKLNpzVkopG9IxZ6WUsiHtOSullA1pz1kppWxIe85KKWVDOltDKaVsyCZPAW8sbZyVUoFFx5yVUsqGtHFWSikb0hOCSillQx6PvxP4hMPfAbw1eFA/dn+1ma8zt/DAvBmNeq+gIGHS7IcZPv5eUibO5LW/rqlxu+3/3MWoKbNJmTiTiekLG1UnQFmZi7mLlzL0P3/OmHvmke8sAODgoUJS0+5j1JTZJMS1oX27xv/O9OX3y9fsmk1z1Y9dc1Fe7v1iY2Ka+MxmcGhcoytwOBxk7f6YIbeMIS/PydZPMhg7bjpZWXsa9H5BQcL2df9Djx90pbjkFKlpc1n+2AK6JiZUbnP8xEnG3vsgv3t6ETFRnTj6fREXX9jBq/fPdxaw8MnlrHxmSbXyN/4vg+ycb1k09x4yNn7Mxi1bWbZoHi6XC2MgNDSE8MQBJMSFk+88hcfTsG+dr79fvmTXbJrLHrncZfnS2GynXrrf6/9x2kz5ZaPrayotoufc+5orycnJZf/+73C5XKxatZoRwwc3+P08HkOPH3QFICK8DUld4ik4crTaNhkbNzPghp8QE9UJoFrDvGb9Jkb/fB6jpsxm8bLn8Xj5Z9QHf99OypD+AAy68Tq2ffYlxhhCQkIIDQ0BQARo5I+Lr79fvmTXbJorMHIBFWPO3i421uDGWUQm+TJIbWLjojmQd7Dy67x8J7Gx0T5573xnAVl79nH5j35QrTz3wEGOnzzJxPSFpKbdx+r3PgQg59sDrPtwC689+wRvvvQbHA4Ha9/f7FVdhYf/RXSnjgAEBwfRtm04RcdOAOAsPMzIyel0SYigqMjV4F4zNO33q7Hsmk1z1Y9dcwGYcuP1YmeNGdxcDPyhphUikgakAUhQJA5HRCOqAZFzu5K+GI4pKTnFnEVPMf/eKbSNCK+2zuMpJzM7hxd/9V+UlpZx14z59OzxA7Z99iWZ3+Qwetr9AJSWlXFRh0gAZj38BPnOAlxuN86CI4yaMhuAsXcMZ+TQmzGcm/nMR4vp3Im3Xn6GtkkDiY4Ko7jYjaeBPzxN9f3yBbtm01z1Y9dcgO3Hkr1Va+MsIl+ebxUQdb79jDErgBXgmzHn/DwnCfGxlV/Hx8XgtE6mNZTL7Wb2oqcYNuBGBv70J+esj+p0MR0i2xHeJozwNmH06tmD7JxcDIYRg29iTtq4c/ZZ/tiCirznGXOO6nQxhw4fIbpzR9xuDydPlhDZvl21bTweQ1lZOWFhDopLGnbWuSm+X75i12yaq37smgvw2WwNEUkAXgWigXJghTHmGRG5CPgzkAjkAqnGmO+tfRYAUwAPMMsY855V3gtYCbQBMoB0U8dvs7qGNaKA8cDwGpajteznU5/u2Em3bpeSmJhASEgIqakprFm7vlHv+cjTz5J0STwTUlNqXN//+t58visTt9vDqdOl7MrcQ9Il8Vx7VU82fPQPjn5fBMCx4yc4eKjQqzr7X9eb1esqhkfWf/QP+lz1H4gIhwqPcLq0FACHA8LCgnC5Gv47rSm+X75i12yaKzByAb6creEG5hpjfgRcC8wQkR7Ag8BGY0x3YKP1Nda60cBlwBDgeREJst7rBSpGE7pby5C6Kq9rWGMt0NYYs/PsFSKyqa439xWPx0P67IfJeOd1ghwOVr7yZzIzv2nw+4Vd4GDN+k10T+pSOfSQfvdYnAVHALgzZQhduyTQt/dV3D4lHYc4GDVsAN2TugAwc8pdpN3/KOXGEBIcxML0acRGd66z3ttvGcCCx3/D0P/8OZHt27H0kbkA7Psuj6XP/wERITamDUXHyihzNfxPM19/v3zJrtk0V2DkAnw2rGGMcQJO6/UJEckC4oAUoJ+12SvAJmC+Vf6GMaYU2C8ie4HeIpILtDfGfAIgIq8CtwHv1lZ/i5hK1xT0GYJK2Y8vptKV/Gaa121OxJwV07DOj1lWWMOy1YhIIrAZ+DHwnTGmQ5V13xtjLhSRZ4Gtxpj/tcpfoqIBzgWeNMYMsMpvAOYbY26tLZteIaiUCiz16DlXPT92PiLSFngTmG2MOV7TydAzm9ZURS3ltdLGWSkVWHw4RU5EQqhomP9ojPmbVVwgIjHGGKeIxABnTjrlAQlVdo8HDlrl8TWU16pFXISilFJe83i8X2ohFV3kl4AsY8yvqqx6G5hgvZ4ArK5SPlpELhCRS6k48bfdGrs+ISLXWu85vso+56U9Z6VUQDG+m+fcFxgH7BKRnVbZQ8CTwCoRmQJ8B/wMwBizW0RWAZlUzPSYYYw58xvgHv49le5d6jgZCNo4K6UCjY+GNYwxWzj/zRRuPs8+S4AlNZTvoOJkote0cVZKBRab3zPDW9o4K6UCi83vmeEtbZyVUoHFHRg329fGWSkVWHRYQymlbEiHNVo2u14mferAB/6OUKM2CTf5O4JSXvHhVDq/arWNs1IqQGnPWSmlbEgbZ6WUsiEf3Wzf37RxVkoFFLs/G9Bb2jgrpQKLNs5KKWVDOltDKaVsSHvOSillQ9o4K6WU/RiPDmsopZT9aM9ZKaXsJ1Cm0rWYZwgOHtSP3V9t5uvMLTwwb4a/41TyZa6gIGFS+kKGj51OyvgZvPaXt2vcbvs/dzFqcjop42cwceaCRtUJUFbmYu6ipxk6Jo0x0+4n31kAwMFDhaROncOoyekkxLWhfTvf/C5vDcfSlzRXPZUb7xcbE2OaNmBwaFyjK3A4HGTt/pght4whL8/J1k8yGDtuOllZe3wR0Ta5goKE7e+toEdyV4pLSkideh/LH3+IromXVG5z/MRJxk6fz+9++SgxUZ04+n0RF1/Ywav3z3cWsPCJZ1i5/PFq5W+8lUF2Ti6L7p9OxsbNbNy8lWWLH8DlcmEMhIaGEH7JTSTEhZPvPIXH0/BD2lqOpeZqGHdZ/vkeC+W1Y+Nu9voHNPK1jY2ur6nU2XMWkR+KyM0i0vas8iFNF6u63tdcSU5OLvv3f4fL5WLVqtWMGD64uapvtlwej6FHclcAIsLDSeoST8Hho9W2yXh/MwN++hNiojoBVGuY16z/kNFpcxk1OZ3FS5/D4+VlrB9s2UbKkIq7zg26sS/bPv8CYwwhISGEhoYAIML5n6ZWD63lWGou/zHucq8XO6u1cRaRWVQ8wnsm8JWIpFRZ/XjNe/lebFw0B/IOVn6dl+8kNja6uao/r6bMle8sIGvPPi7vkVytPPdAPsdPnGTirIdInTqH1esqbjGak3uAdR9s4bXnn+LNl5/BEeRg7YaPvKqr8MhRojt3BCA4OIi2EREUHTsBgLPgMCMnzqRLQgRFRa5G9ZqhdR7LxtBcDVBej8XG6hpEvBvoZYw5KSKJwF9FJNEY8wy19KNEJA1IA5CgSByOiEaFFDm3qqYejvFGU+UqKTnFnF88yfyZU2kbEV5tncfjIfObvbz468coLS3jrnvm0fOyZLZ99gWZ2TmMTpsLQGlpGRd1iARg1sLHyXcW4HK5cRYeZtTkdADG3jGckbcMqDHzmY8WE9WJt1b+lraJNxMdFUZxsRtPI8bqWtuxbCzNVX+BckKwrsY5yBhzEsAYkysi/ahooLtQS+NsjFkBrADfjDnn5zlJiI+t/Do+LganddLKn5oil8vtZvYvnmTYwBsZeON156yP6tSRDpHtCW8TRnibMHr1vIzsvfsxwIgh/ZkzbcI5+yxf8lBF3vOMOUd16sihwiNEd+6I2+3hZHExke3bVdvG4zGUlZUTFuaguKThd/1qTcfSFzRXA9i8R+ytusacD4nIFWe+sBrqW4GOwH80Ya5qPt2xk27dLiUxMYGQkBBSU1NYs3Z9c1XfrLkeeeq3JHWJZ8Kdt9W4vv/1ffj8y0zcbg+nTpeyK+sbkrokcG2vy9mw6R8c/b4IgGPHT3DwUKFXdfbv27tyeGT9R3+nz1WXIyIcKjzC6dJSABwOCAsLwuVq3O/a1nQsNZd/mHLj9WJndfWcxwPuqgXGGDcwXkR+12SpzuLxeEif/TAZ77xOkMPBylf+TGbmN81VfbPlCrvAwZr3PqR7UpfKoYf0u8fhLDwMwJ0pQ+mamEDfPldx+6RZOBzCqGED6Z7UBYCZU8eSNncR5eXlhAQHs3DONGKjO9dZ7+3DBrJgya8YOiaNyHbtWProPAD2fXuApc+9jIgQG9OGomNllLka1y1pLcdSc/lRgPScW8RUutZEnyGoWjNfTKU7OuxGr9uci9/5yLZT6fQKQaVUQDEB0nPWxlkpFVi0cVZKKfvRnrNSStmQNs5KKWVDxmPbc3z1oo2zUiqgaM9ZKaVsyJQHRs+5xdzPWSmlvGHKvV/qIiIvi0ihiHxVpexREckXkZ3WckuVdQtEZK+IZIvI4CrlvURkl7VuudR0c5KzaOOslAooxojXixdWAjXdHvnXxpgrrCUDQER6AKOBy6x9nheRIGv7F6i4GVx3a6nzlsvaOCulAoove87GmM3Av7ysOgV4wxhTaozZD+wFeotIDNDeGPOJqbgk+1XgtrreTMecbcaul0mfytvk7wjn1Sa+n78jKBspr8dsjaq3N7assO6qWZd7RWQ8sAOYa4z5HogDtlbZJs8qc1mvzy6vlfaclVIBxZSL94sxK4wxV1dZvGmYXwC6AlcATmCZVV7TbwVTS3mttOeslAooTT1bwxhTeeNqEfk9sNb6Mg9IqLJpPHDQKo+vobxW2nNWSgUUY7xfGsIaQz5jJHBmJsfbwGgRuUBELqXixN92Y4wTOCEi11qzNMZT8fi/WmnPWSkVUHzZcxaRPwH9gI4ikgcsAvpZDyExQC4wDcAYs1tEVgGZVNwHf4Yx5sxjg+6hYuZHG+Bda6mVNs5KqYDi5RQ5L9/LjKmh+KVatl8CLKmhfAfw4/rUrY2zUiqgePTeGkopZT++7Dn7kzbOSqmAEij31tDGWSkVUJr4sajNRhtnpVRA0Z6zUkrZkKc8MC7faDGfYvCgfuz+ajNfZ27hgXkz/B2nUmvIFRQkTJq1gOFjf07KuOm89pea589v/+eXjJo0k5Rx05l474ONqhOgrMzF3EVPMXT03YxJu498Z8WFWQcPFZI6Jb2yrvbtfNPHaA3H0pfsmqupL0JpLmKaOGFwaFyjK3A4HGTt/pght4whL8/J1k8yGDtuOllZe3wRUXPVIShI2L7+9/RI7kZxSQmpU2az/PGH6XrpJZXbHD9xkrH3zON3yxYTE9WZo98XcfGFHbx6/3xnAQsf/zUrf/tktfI33nqH7Jz9LLr/XjLe/4iNH29l2eL5uFwujIHQ0BBKSk5x5U2jyHeewuNp+I9aazmWds/lLstv9JjEzi4jvP5BuOLbt207BlJnz1lEeovINdbrHiJyX9WbSzeH3tdcSU5OLvv3f4fL5WLVqtWMGD647h01l09yeTyGHsndAIgIDycpMYGCI0erbZPx/kcMuPE6YqI6A1RrmNe89yGj0+YwatJMFi99Fo/Hgzc++HgrKUNuBmBQv+vZ9tkXGGMICQkhNDQEgDKXq+bbytRTazmWgZ4LfH4/Z7+ptXEWkUXAcuAFEXkCeBZoCzwoIgubIR8AsXHRHMj7931C8vKdxMZGN1f159Uac+U7C8j6Zh+X90iuVp57IJ/jJ04yceaDpE5JZ/W6jQDk5B5g3Qebee35pbz5h9/icDhYu2GTV3UVHjlKdOdOAAQHB9E2IpyiY8cBcBYcZuSEexkwahJFRa5G9ZqhdR7LxrBrLgicYY26BuvuoOK2eBcAh4B4Y8xxEVkKbKOGyxSh+j1SJSgShyOiUSFreqJLUw/HeKO15SopOcWchx9n/qy7aRsRXm2dx+MhM3svL/5mCaWlpdx1z/307PFDtn22k8zsHEbfPQeA0tIyLrowEoBZDz1GvrMAl8uNs/AwoybNBGDsHSMYOWxgjf/znPlsMVGdeOuVZyk8cpTrbhlLcbEbT3nDP2NrO5aNZddcAOU27xF7q67G2W3duKNERHKMMccBjDGnROS8zxGw7om6Anwz5pyf5yQhPrby6/i4GJzOglr2aB6tKZfL7Wb2w48zbGA/Bt543Tnrozp1pENke8LbhBHeJoxePX9Mds5+jIERQ25izs8nnrPP8scfrsh7njHnqE4Xc6jwMNGdO+J2ezhZXEJk+3bVtunc8WLKysoJC3NQXOLdcElNWtOx9AW75oLWM1ujTETOdJF6nSkUkUig2R5A/umOnXTrdimJiQmEhISQmprCmrXrm6t6zQU88uQzJCUmMGH0yBrX97/+Wj7/Yjdut4dTp0+zKzObpC7xXNurJxs++jtHvy8C4NjxExw8VOhVnf2v71M5PLJ+0xb6XHU5IsKhwiOcLi2teL8TJwkLC8LlalwfoDUdy0DOBRW3ivN2sbO6es4/NcaUAhhT7YlbIcCEJkt1Fo/HQ/rsh8l453WCHA5WvvJnMjO/aa7qW32usAscrHnvQ7onJVYOPaSnjcdZcBiAO2+7ha6JCfTt04vbJ96LwyGMunUw3ZMSAZg5dRxp9/2C8nJDSHAQC++7h9joznXWe/uwQSx4bBlDR99NZPu2LH10PgD7vj3A0mdfQqRi3LDoWBllrsb1FVrLsQz0XBA4wxotYiqd8j99hqBqDr6YSvf36Du8bnP6HvqrbVtyvUJQKRVQmm28tYlp46yUCijGFxPfbUAbZ6VUQHEHyJizNs5KqYCiPWellLIhHXNWSikb0p6zUkrZkPaclVLKhjzac1ZKKfsJkKdUaeOslAos5dpzVq2JnS+RPnXwY39HqFGb2Bv8HaFVCpT7RWjjrJQKKHpCUCmlbKi8hgcBtETaOCulAkrDH7lgL9o4K6UCis7WUEopG9LZGkopZUOBMlsjMJ6EqJRSlnLxfqmLiLwsIoUi8lWVsotEZIOI7LH+vbDKugUisldEskVkcJXyXiKyy1q3XGp6fPlZtHFWSgWU8nosXlgJDDmr7EFgozGmO7DR+hoR6QGMBi6z9nleRIKsfV4A0oDu1nL2e55DG2elVEDxiPdLXYwxm4F/nVWcArxivX4FuK1K+RvGmFJjzH5gL9BbRGKA9saYT0zFQ1tfrbLPeemYs1IqoDTDRShRxhgngDHGKSJnHiUfB2ytsl2eVeayXp9dXivtOSulAkp9hjVEJE1EdlRZ0hpRdU19cVNLea1aTOM8eFA/dn+1ma8zt/DAvBn+jlNJc9WfL7MFBQmT7p3P8P9MI+Wuaby26v9q3G77518yasIMUu6axsQZ8xpVJ0BZWRlzf/EEQ1MnM+bu2eQ7CwA4eKiA1MkzGTVhBglxbWjfrvF/nNr1WNo1l5F6LMasMMZcXWVZ4UUVBdZQBda/hVZ5HpBQZbt44KBVHl9Dea1aROPscDhY/swSbh0+lv/o2Z8777yNH/2ou79jaa4GaIps82bezZrXV/D6il/zxt/WkrP/22rrj584yWPLnuXZpxax+o+/Y9ljC71+73xnARPvfeCc8r+tXU/7dm15d9XLjLvzNn71/MsAdLr4Iv73f5bx5ivPkXfwFB0iQwkKavi8W7seS7vmAp+fEKzJ28AE6/UEYHWV8tEicoGIXErFib/t1hDICRG51pqlMb7KPudV78ZZRF6t7z6N1fuaK8nJyWX//u9wuVysWrWaEcMH172j5rJVLvB9No/H0CO5GwAREeEkdUmg4PDRattkbNjEgBv7EhNdMTR48YUdKtetee8DRk9NZ9SEGSx+ejkej3cX/37w8Sek3DIAgEH9bmDbZzsxxhASEkJoaCgAItT8B2092PVY2jUXVFy+7e1SFxH5E/AJkCwieSIyBXgSGCgie4CB1tcYY3YDq4BMYB0wwxhzppp7gBepOEmYA7xbV921/s0lIm+fXQT0F5EOVpgRXny+RouNi+ZA3r//CsjLd9L7miubo+paaa76a8ps+c4CsvbkcPllydXKc7/Lw+3xMPHeBygpOcVdP0shZegAcnK/Y93Gj3jtf5YREhzMf//yWdau/5CUoQPqrKvw8FGiO3cEIDg4iLYR4RQdO86FHSJxFhxm+rxH6JIQwdF/leHxNPyyCLseS7vmAt9evm2MGXOeVTefZ/slwJIayncAP65P3XUNiMVT8VvgRf49sH01sKy2naxB9TQACYrE4YioT6aa3u+csooZKf6lueqvqbKVlJxizsLHmD9rGm0jqv+8eTzlZH69hxeXP0lpaSl3TbuPnpf9kG07dpL59V5GT0kHoLS0lIusXvWsBf9F/sECXG4XzoLDjJpQMaY6NjWFkcMG1Zj5zGeLierEW6++QNuEnxIdFUZxsRtPecM+o12PpV1zQeu5ZejVQDqwEJhnjNkpIqeMMR/VtpM1qL4CIDg0rtFHLD/PSUJ8bOXX8XExOK0TMP6kueqvKbK53G5mL3yMYYP6M7Bf33PWR3XuSIcO7QlvE0Z4mzB6XfFjsvfuxxjDiKEDmHPPpHP2Wf7EIxV5nQUsXLKMlc8+fc57Hio8QnTnTrjdHk4WlxDZvl21bTweQ1lZOWFhDopLGnavNLseS7vmgsBpnGsdczbGlBtjfg1MAhaKyLP4YW70pzt20q3bpSQmJhASEkJqagpr1q5v7hiayweaItsjT/yGpC4JTBh9e43r+99wLZ9/8RVut4dTp0+za3c2SYkJXHv1FWzYtIWj3xcBcOz4CQ4e8q6B6X/9tazOeB+A9Zs+pk+vnogIhwoPc7q0FACHA8LCgnC5Gt4/seuxtGsuqPgT39vFzrxqaI0xecDPRGQYcLxpI53L4/GQPvthMt55nSCHg5Wv/JnMzG+aO4bm8gFfZwu7wMGadRvp3jWxcughfdoEnAWHAbhz5DC6Jl5C3z5Xc/uEe3CIg1HDB9M9KRGAmXePJ232QspNOSHBwSy8bzqx0VF11nv7rYNZ8N9LGZo6mcj27Vi6+EEA9uUeYOmzv0dEiI1pQ9GxMspcDe/L2fVY2jUXBM4tQ6Wpx4l8MayhVG30GYKBw12W3+im9YkuY71ucxZ8+7+2bcr18m2lVEApt/2AhXe0cVZKBZRAOSGojbNSKqAERr9ZG2elVIDRnrNSStmQWwKj76yNs1IqoARG06yNs1IqwOiwhlJK2ZBOpVNKKRsKjKZZG2elVIDRYQ2lbMKul0nrZeX+4QmQvrM2zkqpgKI9Z6WUsiGjPWellLIf7TkrpZQN6VQ6pZSyocBomrVxVkoFGHeANM/aOCulAoqeEFRKKRvSE4JKKWVD2nNWSikb0p6zUkrZkMcERs/Z4e8A3ho8qB+7v9rM15lbeGDeDH/HqaS56s+u2XyZKyhImHTvfIb/Zxopd03jtVX/V+N22z//klETZpBy1zQmzpjXqDoBysrKmPuLJxiaOpkxd88m31kAwMFDBaROnsmoCTNIiGtD+3aN75fZ9TiWY7xe7ExME/+WCQ6Na3QFDoeDrN0fM+SWMeTlOdn6SQZjx00nK2uPLyJqLs3m81xBQcL291+mR3I3iotLSJ0yi+VP/IKul3ap3Ob4iZOM/fl9/G7ZY8REd+bo90VcfGEHr94/31nAwiXLWPns09XK3/jbWrL37mfRAzPJeH8TGz/6hGX/vQCXy4UxhtDQUMLjbiAhLpx85yk8nob979lUx9Fdli+NegNgTJfbvP5Qf/r2/xpdX1OpV89ZRK4XkftEZFBTBapJ72uuJCcnl/37v8PlcrFq1WpGDB/cnBE0l4/YNZuvc3k8hh7J3QCIiAgnqUsCBYePVtsmY8MmBtzYl5jozgDVGuY1733A6KnpjJowg8VPL8fj8XhV7wcff0LKLQMAGNTvBrZ9thNjDCEhIYSGhgIgAjSySbLrcYSKMWdvFzurtXEWke1VXt8NPAu0AxaJyINNnK1SbFw0B/IOVn6dl+8kNja6uao/L81Vf3bN1pS58p0FZO3J4fLLkquV536Xx/ETJ5l47wOkTp7J6nffByAn9zvWbfyI1/5nGW++8hwOh4O16z/0qq7Cw0eJ7twRgODgINpGhFN07DgAzoLDjBx/D10SIigqcjW41wz2PY4QOMMadQ08hVR5nQYMNMYcFpFfAluBJ2vaSUTSrO2RoEgcjohGhRQ599d8Uw/HeENz1Z9dszVVrpKSU8xZ+BjzZ02jbUT1/w88nnIyv97Di8ufpLS0lLum3UfPy37Ith07yfx6L6OnpANQWlrKRVavetaC/yL/YAEutwtnwWFGTagY6x2bmsLIYYNqzHzms8VEdeKtV1+gbcJPiY4Ko7jYjae8YZ/RrscRWs9UOoeIXEhFD1uMMYcBjDHFIuI+307GmBXACvDNmHN+npOE+NjKr+PjYnBaJzr8SXPVn12zNUUul9vN7IWPMWxQfwb263vO+qjOHenQoT3hbcIIbxNGryt+TPbe/RhjGDF0AHPumXTOPsufeKQi73nGnKM6d+RQ4RGiO3fC7fZwsriEyPbtqm3j8RjKysoJC3NQXOLdcMnZ7HocofXM1ogEPgN2ABeJSDSAiLSl0aNW3vt0x066dbuUxMQEQkJCSE1NYc3a9c1VvebyIbtma4pcjzzxG5K6JDBh9O01ru9/w7V8/sVXuN0eTp0+za7d2SQlJnDt1VewYdMWjn5fBMCx4yc4eMi7hq//9deyOqNieGT9po/p06snIsKhwsOcLi0FwOGAsLAgXK6GN2J2PY7g22ENEckVkV0islNEdlhlF4nIBhHZY/17YZXtF4jIXhHJFpFGDcLX2nM2xiSeZ1U5MLIxFdeHx+MhffbDZLzzOkEOBytf+TOZmd80V/Way4fsms3XucIucLBm3Ua6d02sHHpInzYBZ8FhAO4cOYyuiZfQt8/V3D7hHhziYNTwwXRPSgRg5t3jSZu9kHJTTkhwMAvvm05sdFSd9d5+62AW/PdShqZOJrJ9O5Yurjg1tC/3AEuf/T0iQmxMG4qOlVHmavgpMbseR2iSE339jTFHqnz9ILDRGPOkde7tQWC+iPQARgOXAbHA+yLyA2NMg/48aRFT6ZRqifQZgvXni6l0t14yzOs2Z+1379Ran4jkAldXbZxFJBvoZ4xxikgMsMkYkywiCwCMMU9Y270HPGqM+aQBH6PlXISilFLeqM+whoikiciOKkvaWW9ngPUi8lmVdVHGGCeA9W9nqzwOOFBl3zyrrEH08m2lVECpz2hA1ckL59HXGHNQRDoDG0Tk61q2rakX3uCRA22clVIBxePDqXTGmIPWv4Ui8hbQGygQkZgqwxqF1uZ5QEKV3eOBgzSQDmsopQKKr2ZriEiEiLQ78xoYBHwFvA1MsDabAKy2Xr8NjBaRC0TkUqA7sJ0G0p6zUiqg+HCSQxTwlnXBTTDwujFmnYh8CqwSkSnAd8DPrHp3i8gqIBNwAzMaOlPjTIVKKRUwfHVZtjFmH9CzhvKjwM3n2WcJsMQX9WvjrJQKKK3l8m2llGpRAuXybW2clVIBxe53m/OWNs5KqYCijbNSqlZ2vUz61IEP/B2hSdnl1qWNpY2zUiqgaM9ZKaVsSGdrKKWUDXmM3Z8O6B1tnJVSAUXHnJVSyoZ0zFkppWxIx5yVUsqGynVYQyml7Ed7zkopZUM6W0MppWxIhzWUUsqGAmVYo8U8pmrwoH7s/mozX2du4YF5M/wdp5Lmqj+7ZmsNuYKChEnpCxk+djop42fw2l/ernG77f/cxajJ6aSMn8HEmQsaVSdAWZmLuYueZuiYNMZMu598ZwEABw8Vkjp1TmVdycnJP29sXeXGeL3YmTT1hO3g0LhGV+BwOMja/TFDbhlDXp6TrZ9kMHbcdLKy9vgioubSbK0mV1CQsP29FfRI7kpxSQmpU+9j+eMP0TXxksptjp84ydjp8/ndLx8lJqoTR78v4uILO3j1/vnOAhY+8Qwrlz9erfyNtzLIzsll0f3Tydi4mY2bt7Js8QO4XC6MgdDQEEpKTtF7yJ3fAtdlZ2c3+MGoSR2v9LrN2XfknzU9MdsWau05i0gfEWlvvW4jIotFZI2IPCUikc0TEXpfcyU5Obns3/8dLpeLVatWM2L44OaqXnP5kF2ztZZcHo+hR3JXACLCw0nqEk/B4aPVtsl4fzMDfvoTYqI6AVRrmNes/5DRaXMZNTmdxUufw+Px7hF5H2zZRsqQmwAYdGNftn3+BcYYQkJCCA0NAaDM5QIf/DXvMR6vFzur6xvxMlBivX4GiASessr+0IS5qomNi+ZA3r9/keblO4mNjW6u6s9Lc9WfXbO1xlz5zgKy9uzj8h7J1cpzD+Rz/MRJJs56iNSpc1i9ruIWozm5B1j3wRZee/4p3nz5GRxBDtZu+MirugqPHCW6c0cAgoODaBsRQdGxEwA4Cw4zcuJMBtwxGeCpxvSaoeLybW8XO6vrhKDDGOO2Xl9tjLnKer1FRHaebycRSQPSACQoEocjolEhraffVmOHb6zmqj+7ZmttuUpKTjHnF08yf+ZU2kaEV1vn8XjI/GYvL/76MUpLy7jrnnn0vCyZbZ99QWZ2DqPT5gJQWlrGRR0q/oCetfBx8p0FuFxunIWHGTU5HYCxdwxn5C0Dasx85qPFRHXirZW/pfDIUW66fdKE5OTkv2ZnZxc09LO1lsu3vxKRScaYPwBfiMjVxpgdIvIDwHW+nYwxK4AV4Jsx5/w8JwnxsZVfx8fF4HQ2+Nj5jOaqP7tma025XG43s3/xJMMG3sjAG687Z31Up450iGxPeJswwtuE0avnZWTv3Y8BRgzpz5xpE87ZZ/mShyrynmfMOapTRw4VHiG6c0fcbg8ni4uJbN+u2jadO14MsBu4AfhrQz+fHX6p+kJdwxpTgRtFJAfoAXwiIvuA31vrmsWnO3bSrdulJCYmEBISQmpqCmvWrm+u6jWXD9k1W2vK9chTvyWpSzwT7rytxvX9r+/D519m4nZ7OHW6lF1Z35DUJYFre13Ohk3/4Oj3RQAcO36Cg4cKvaqzf9/elcMj6z/6O32uuhwR4VDhEU6Xlla834mTAH2B7MZ8vkCZrVFrz9kYcwyYKCLtgCRr+zxjTLN2KTweD+mzHybjndcJcjhY+cqfycz8pjkjaC4fsWu21pIr7AIHa977kO5JXSqHHtLvHoez8DAAd6YMpWtiAn37XMXtk2bhcAijhg2ke1IXAGZOHUva3EWUl5cTEhzMwjnTiI3uXGe9tw8byIIlv2LomDQi27Vj6aPzANj37QGWPvcyInKmx/vL7OzsXQ3+gATOPOcWMZVOKeU7dn6GYEhUcqOntnWKTPa6zTl8LNu2U+n0CkGlVEAJlDFnbZyVUgHF7mPJ3tLGWSkVULTnrJRSNtRa5jkrpVSLoj1npZSyIb3ZvlJK2ZCeEFRKKRsKlGGNFnOzfaWU8oapx391EZEhIpItIntF5MFmiF9Je85KqYDiq56ziAQBzwEDgTzgUxF52xiT6ZMK6qCNs1IqoPhwzLk3sNcYsw9ARN4AUoDAaJzdZfk+u3ZdRNKs25Hajl2zaa76sWsusG82u+WqT5tT9d7zlhVVPksccKDKujygT+MTeqeljTmn1b2J39g1m+aqH7vmAvtms2uuOhljVhhjrq6yVP0lU1Mj32xnG1ta46yUUs0lD0io8nU80KhHaNWHNs5KKVWzT4HuInKpiIQCo4G3m6vylnZC0DbjWjWwazbNVT92zQX2zWbXXI1ijHGLyL3Ae0AQ8LIxZndz1d/kN9tXSilVfzqsoZRSNqSNs1JK2VCLaZz9eRllbUTkZREpFJGv/J3lDBFJEJEPRSRLRHaLSLq/M50hImEisl1EvrCyLfZ3pqpEJEhE/ikia/2d5QwRyRWRXSKyU0R2+DvPGSLSQUT+KiJfWz9rP/F3pkDSIsacrcsov6HKZZTAmOa6jLI2IvJT4CTwqjHmx/7OAyAiMUCMMeZz68npnwG32eT7JUCEMeakiIQAW4B0Y8xWP0cDQETuA64G2htjbvV3HqhonIGrjTFH/J2lKhF5BfjYGPOiNZsh3BhT5OdYAaOl9JwrL6M0xpQBZy6j9DtjzGbgX/7OUZUxxmmM+dx6fQLIouJqJ78zFU5aX4ZYiy16CCISDwwDXvR3FrsTkfbAT4GXAIwxZdow+1ZLaZxruozSFo2N3YlIInAlsM3PUSpZQwc7gUJggzHGLtl+AzwA2O1u7QZYLyKfWZcb20EScBj4gzUM9KKIRPg7VCBpKY2zXy+jbKlEpC3wJjDbGHPc33nOMMZ4jDFXUHHFVW8R8ftwkIjcChQaYz7zd5Ya9DXGXAUMBWZYQ2n+FgxcBbxgjLkSKAZscy4oELSUxtmvl1G2RNZ47pvAH40xf/N3nppYfwZvAob4NwkAfYER1vjuG8BNIvK//o1UwRhz0Pq3EHiLimE+f8sD8qr81fNXKhpr5SMtpXH262WULY110u0lIMsY8yt/56lKRDqJSAfrdRtgAPC1X0MBxpgFxph4Y0wiFT9fHxhjxvo5FiISYZ3UxRo2GAT4fWaQMeYQcEBEkq2im2mmW2m2Fi3i8m1/X0ZZGxH5E9AP6CgiecAiY8xL/k1FX2AcsMsa2wV4yBiT4b9IlWKAV6wZOA5glTHGNtPWbCgKeKvi9y3BwOvGmHX+jVRpJvBHq8O0D5jk5zwBpUVMpVNKqdampQxrKKVUq6KNs1JK2ZA2zkopZUPaOCullA1p46yUUjakjbNSStmQNs5KKWVD/w/7gLpAn9MsPQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.heatmap(confusion_matrix(y_train, y_pred_train), annot=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1710706543472,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"AIFEcQvV5CWd","outputId":"42afacc7-8261-4a3d-c40a-a077e71fe9e0"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00      2587\n","           1       1.00      1.00      1.00      2580\n","           2       1.00      1.00      1.00      2585\n","           3       1.00      1.00      1.00      2600\n","           4       1.00      1.00      1.00      2614\n","           5       1.00      1.00      1.00      2604\n","           6       1.00      1.00      1.00      2585\n","\n","    accuracy                           1.00     18155\n","   macro avg       1.00      1.00      1.00     18155\n","weighted avg       1.00      1.00      1.00     18155\n","\n"]}],"source":["print(classification_report(y_train, y_pred_train))"]},{"cell_type":"markdown","metadata":{"id":"f8JB0Qjz5CWe"},"source":["**Testing**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IVHALs7f5CWe"},"outputs":[],"source":["y_pred_test = dt.predict(X_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"executionInfo":{"elapsed":543,"status":"ok","timestamp":1710706546507,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"qlmtgc4q5CWe","outputId":"a88d2d33-0ce7-4de5-b6db-845d4371951c"},"outputs":[{"data":{"text/plain":["<AxesSubplot:>"]},"execution_count":31,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.heatmap(confusion_matrix(y_test, y_pred_test), annot=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1710706546983,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"vvND-sQ45CWe","outputId":"03b2fad9-0c95-4088-9f17-8df4cfe67bee"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.89      0.92      0.90       655\n","           1       0.80      0.77      0.78       662\n","           2       0.81      0.84      0.83       657\n","           3       0.96      0.93      0.94       642\n","           4       1.00      0.99      1.00       628\n","           5       0.72      0.72      0.72       638\n","           6       0.75      0.76      0.76       657\n","\n","    accuracy                           0.85      4539\n","   macro avg       0.85      0.85      0.85      4539\n","weighted avg       0.85      0.85      0.85      4539\n","\n"]}],"source":["print(classification_report(y_test, y_pred_test))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1710706546983,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"uhpTwt6q5CWe","outputId":"44d565f3-75f5-4343-c65b-855de739a163"},"outputs":[{"name":"stdout","output_type":"stream","text":["None\n","None\n","2\n","1\n"]}],"source":["print(dt.max_features)\n","print(dt.max_depth)\n","print(dt.min_samples_split)\n","print(dt.min_samples_leaf)"]},{"cell_type":"markdown","metadata":{"id":"k0kJ4R5-5CWe"},"source":["## Hyperparameter Tuning:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EkMK33dB5CWf"},"outputs":[],"source":["max_features = ['auto', 'sqrt']\n","max_depth = [2, 8, 12, 25, 30]\n","min_samples_split = [2, 5]\n","min_samples_leaf = [3, 5]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sPAFDAum5CWf"},"outputs":[],"source":["param_grid = {\n","    'max_features': max_features,\n","    'max_depth': max_depth,\n","    'min_samples_split': min_samples_split,\n","    'min_samples_leaf': min_samples_leaf,\n","}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bQOFUJbV5CWf"},"outputs":[],"source":["dt_model = DecisionTreeClassifier()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"02OBPXqd5CWf"},"outputs":[],"source":["grid = GridSearchCV(dt_model, param_grid=param_grid, cv=10, n_jobs=-1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118},"executionInfo":{"elapsed":10335,"status":"ok","timestamp":1710706561646,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"FLf_SfZK5CWf","outputId":"2505e6f0-f530-4ab2-fc9b-cc62a67cd736"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(), n_jobs=-1,\n","             param_grid={&#x27;max_depth&#x27;: [2, 8, 12, 25, 30],\n","                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n","                         &#x27;min_samples_leaf&#x27;: [3, 5],\n","                         &#x27;min_samples_split&#x27;: [2, 5]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(), n_jobs=-1,\n","             param_grid={&#x27;max_depth&#x27;: [2, 8, 12, 25, 30],\n","                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n","                         &#x27;min_samples_leaf&#x27;: [3, 5],\n","                         &#x27;min_samples_split&#x27;: [2, 5]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"],"text/plain":["GridSearchCV(cv=10, estimator=DecisionTreeClassifier(), n_jobs=-1,\n","             param_grid={'max_depth': [2, 8, 12, 25, 30],\n","                         'max_features': ['auto', 'sqrt'],\n","                         'min_samples_leaf': [3, 5],\n","                         'min_samples_split': [2, 5]})"]},"execution_count":93,"metadata":{},"output_type":"execute_result"}],"source":["grid.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1710706561646,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"58YOx6K55CWf","outputId":"468ca75e-f9d2-4f13-d51f-3cfdcbde1edd"},"outputs":[{"data":{"text/plain":["0.9052602588818507"]},"execution_count":94,"metadata":{},"output_type":"execute_result"}],"source":["grid.score(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":92},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1710706561646,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"HZYHCCP95CWg","outputId":"cba34d8c-7dea-4a92-b76c-9fdd8ff8168b"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=25, max_features=&#x27;auto&#x27;, min_samples_leaf=3,\n","                       min_samples_split=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=25, max_features=&#x27;auto&#x27;, min_samples_leaf=3,\n","                       min_samples_split=5)</pre></div></div></div></div></div>"],"text/plain":["DecisionTreeClassifier(max_depth=25, max_features='auto', min_samples_leaf=3,\n","                       min_samples_split=5)"]},"execution_count":95,"metadata":{},"output_type":"execute_result"}],"source":["grid.best_estimator_"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1710706561647,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"nrU6qrXL5CWg","outputId":"6f9a6e87-80e4-44d3-cf68-7d8563cc646a"},"outputs":[{"data":{"text/plain":["0.8367389958859723"]},"execution_count":96,"metadata":{},"output_type":"execute_result"}],"source":["grid.best_score_"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1710706561647,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"7bMTFBFX5CWg","outputId":"0796dca4-43f9-41ce-cbc7-5c451da190a1"},"outputs":[{"data":{"text/plain":["{'max_depth': 25,\n"," 'max_features': 'auto',\n"," 'min_samples_leaf': 3,\n"," 'min_samples_split': 5}"]},"execution_count":97,"metadata":{},"output_type":"execute_result"}],"source":["grid.best_params_"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dC5gJoP35CWg"},"outputs":[],"source":["pred = grid.predict(X_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1710706561647,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"BheOADJR5CWg","outputId":"e4e1f432-94aa-4758-f3cb-da805ceb6a6c"},"outputs":[{"data":{"text/plain":["0.811191892487332"]},"execution_count":99,"metadata":{},"output_type":"execute_result"}],"source":["accuracy_score(y_test, pred)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":417,"status":"ok","timestamp":1710706648848,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"baGoo0R1DpQg","outputId":"f14a0c77-5d6d-4f1c-baeb-0ae63a1e9b77"},"outputs":[{"data":{"text/plain":["0.8116676840957849"]},"execution_count":102,"metadata":{},"output_type":"execute_result"}],"source":["precision_score(y_test, pred, average='weighted')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":399,"status":"ok","timestamp":1710706661417,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"oJuYvYHhDpGv","outputId":"cd0f596b-c58b-47b4-fccf-39f28f117360"},"outputs":[{"data":{"text/plain":["0.811191892487332"]},"execution_count":103,"metadata":{},"output_type":"execute_result"}],"source":["recall_score(y_test, pred, average='weighted')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1710706561647,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"i4Q3bAQE5CWg","outputId":"7c861019-5c36-463b-dd07-dc6e44ba20cc"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.82      0.84      0.83       655\n","           1       0.70      0.72      0.71       662\n","           2       0.80      0.82      0.81       657\n","           3       0.96      0.94      0.95       642\n","           4       0.99      0.99      0.99       628\n","           5       0.68      0.67      0.68       638\n","           6       0.74      0.70      0.72       657\n","\n","    accuracy                           0.81      4539\n","   macro avg       0.81      0.81      0.81      4539\n","weighted avg       0.81      0.81      0.81      4539\n","\n"]}],"source":["print(classification_report(y_test, pred))"]},{"cell_type":"markdown","metadata":{"id":"K_I269ae5CWh"},"source":["**Focus on values that get best score**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hlRIxKqL5CWh"},"outputs":[],"source":["max_features = ['auto', 'sqrt']\n","max_depth = [25, 30, 32, 35]\n","min_samples_split = [4, 5, 7]\n","min_samples_leaf = [3, 5, 7]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SXh58uKj5CWh"},"outputs":[],"source":["param_grid = {\n","    'max_features': max_features,\n","    'max_depth': max_depth,\n","    'min_samples_split': min_samples_split,\n","    'min_samples_leaf': min_samples_leaf,\n","}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5H_9fnbB5CWh"},"outputs":[],"source":["dt_model = DecisionTreeClassifier()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RO7ChG1e5CWh"},"outputs":[],"source":["grid = GridSearchCV(dt_model, param_grid=param_grid, cv=10, n_jobs=-1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118},"executionInfo":{"elapsed":20818,"status":"ok","timestamp":1710706762620,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"BHkE5xjP5CWh","outputId":"acdb7060-6a52-455b-bb73-bea13687c31f"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(), n_jobs=-1,\n","             param_grid={&#x27;max_depth&#x27;: [25, 30, 32, 35],\n","                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n","                         &#x27;min_samples_leaf&#x27;: [3, 5, 7],\n","                         &#x27;min_samples_split&#x27;: [4, 5, 7]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(), n_jobs=-1,\n","             param_grid={&#x27;max_depth&#x27;: [25, 30, 32, 35],\n","                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n","                         &#x27;min_samples_leaf&#x27;: [3, 5, 7],\n","                         &#x27;min_samples_split&#x27;: [4, 5, 7]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"],"text/plain":["GridSearchCV(cv=10, estimator=DecisionTreeClassifier(), n_jobs=-1,\n","             param_grid={'max_depth': [25, 30, 32, 35],\n","                         'max_features': ['auto', 'sqrt'],\n","                         'min_samples_leaf': [3, 5, 7],\n","                         'min_samples_split': [4, 5, 7]})"]},"execution_count":108,"metadata":{},"output_type":"execute_result"}],"source":["grid.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24,"status":"ok","timestamp":1710706762621,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"YeWBptXK5CWi","outputId":"abbab155-220e-4446-9cb3-1b7ed75f8e75"},"outputs":[{"data":{"text/plain":["0.921894794822363"]},"execution_count":109,"metadata":{},"output_type":"execute_result"}],"source":["grid.score(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":92},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1710706762621,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"KnyO_IRS5CWi","outputId":"fd49a276-c87a-4319-becd-24a16a640613"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=35, max_features=&#x27;sqrt&#x27;, min_samples_leaf=3,\n","                       min_samples_split=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=35, max_features=&#x27;sqrt&#x27;, min_samples_leaf=3,\n","                       min_samples_split=7)</pre></div></div></div></div></div>"],"text/plain":["DecisionTreeClassifier(max_depth=35, max_features='sqrt', min_samples_leaf=3,\n","                       min_samples_split=7)"]},"execution_count":110,"metadata":{},"output_type":"execute_result"}],"source":["grid.best_estimator_"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1710706762621,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"bnZy3RvN5CWi","outputId":"af8319e5-0ea5-4608-ffa6-1805b82d5b95"},"outputs":[{"data":{"text/plain":["0.8401556413150326"]},"execution_count":111,"metadata":{},"output_type":"execute_result"}],"source":["grid.best_score_"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1710706762621,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"SxB8IAfY5CWi","outputId":"9aba2763-caf1-437f-e113-f9a534678b8b"},"outputs":[{"data":{"text/plain":["{'max_depth': 35,\n"," 'max_features': 'sqrt',\n"," 'min_samples_leaf': 3,\n"," 'min_samples_split': 7}"]},"execution_count":112,"metadata":{},"output_type":"execute_result"}],"source":["grid.best_params_"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Gw7UtFTn5CWi"},"outputs":[],"source":["pred = grid.predict(X_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1710706762622,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"fueeOY7C5CWi","outputId":"3ee8dbd9-5842-465a-d339-9bac8016f492"},"outputs":[{"data":{"text/plain":["0.8567966512447676"]},"execution_count":114,"metadata":{},"output_type":"execute_result"}],"source":["accuracy_score(y_test, pred)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":570,"status":"ok","timestamp":1710706811208,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"1tz40RpTEfml","outputId":"a6fcbca4-ab95-4e1c-f657-c6cace98542d"},"outputs":[{"data":{"text/plain":["0.8568430596087265"]},"execution_count":116,"metadata":{},"output_type":"execute_result"}],"source":["precision_score(y_test, pred, average='weighted')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1710706811718,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"--cF_QjMEffz","outputId":"98f0a5af-59a2-436e-f11a-a75ff0f223ae"},"outputs":[{"data":{"text/plain":["0.8567966512447676"]},"execution_count":117,"metadata":{},"output_type":"execute_result"}],"source":["recall_score(y_test, pred, average='weighted')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1710706762623,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"CAs4BFlF5CWj","outputId":"7d281b64-9d8b-4af9-c45a-bfeeac7d6e82"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.91      0.91      0.91       655\n","           1       0.77      0.81      0.79       662\n","           2       0.83      0.85      0.84       657\n","           3       0.95      0.94      0.94       642\n","           4       1.00      1.00      1.00       628\n","           5       0.75      0.73      0.74       638\n","           6       0.80      0.76      0.78       657\n","\n","    accuracy                           0.86      4539\n","   macro avg       0.86      0.86      0.86      4539\n","weighted avg       0.86      0.86      0.86      4539\n","\n"]}],"source":["print(classification_report(y_test, pred))"]},{"cell_type":"markdown","metadata":{"id":"2Snt_zN05CWj"},"source":["## Random Forest Model:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-L1CHIau5CWj"},"outputs":[],"source":["rf = RandomForestClassifier()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"executionInfo":{"elapsed":2703,"status":"ok","timestamp":1710706859699,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"GSDkcX075CWj","outputId":"1c850564-b911-4e4b-dfb1-65028b7dcd1e"},"outputs":[{"data":{"text/plain":["RandomForestClassifier()"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["rf.fit(X_train, y_train)"]},{"cell_type":"markdown","metadata":{"id":"bKD6_Km05CWj"},"source":["**Training**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"I3wUp-b55CWj"},"outputs":[],"source":["y_pred_train = rf.predict(X_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"executionInfo":{"elapsed":679,"status":"ok","timestamp":1710706860375,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"otjQLPgC5CWk","outputId":"8bc31e28-f899-42a8-a86c-504a3bb472f4"},"outputs":[{"data":{"text/plain":["<AxesSubplot:>"]},"execution_count":40,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.heatmap(confusion_matrix(y_train, y_pred_train), annot=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1710706860375,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"HELcyOeN5CWk","outputId":"26bbbb88-f5c5-4ad0-d03e-4e2c6d65ad34"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00      2587\n","           1       1.00      1.00      1.00      2580\n","           2       1.00      1.00      1.00      2585\n","           3       1.00      1.00      1.00      2600\n","           4       1.00      1.00      1.00      2614\n","           5       1.00      1.00      1.00      2604\n","           6       1.00      1.00      1.00      2585\n","\n","    accuracy                           1.00     18155\n","   macro avg       1.00      1.00      1.00     18155\n","weighted avg       1.00      1.00      1.00     18155\n","\n"]}],"source":["print(classification_report(y_train, y_pred_train))"]},{"cell_type":"markdown","metadata":{"id":"9aWzIabJ5CWk"},"source":["**Testing**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4TG8She35CWk"},"outputs":[],"source":["y_pred_test = rf.predict(X_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1710706861550,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"o0JeQ9dX5CWk","outputId":"2dddcf2a-962f-41ae-b5ad-55f1a0813e5e"},"outputs":[{"data":{"text/plain":["<AxesSubplot:>"]},"execution_count":43,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.heatmap(confusion_matrix(y_test, y_pred_test), annot=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1710706862059,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"6mPe0xm_5CWk","outputId":"91256c1c-dcca-4e79-d3db-efed284fe962"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.94      0.95      0.94       655\n","           1       0.86      0.87      0.87       662\n","           2       0.89      0.90      0.90       657\n","           3       0.97      0.97      0.97       642\n","           4       1.00      1.00      1.00       628\n","           5       0.82      0.78      0.80       638\n","           6       0.82      0.85      0.83       657\n","\n","    accuracy                           0.90      4539\n","   macro avg       0.90      0.90      0.90      4539\n","weighted avg       0.90      0.90      0.90      4539\n","\n"]}],"source":["print(classification_report(y_test, y_pred_test))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1710706862060,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"1AyqznP75CWl","outputId":"596ec453-5cef-443d-e032-5cf17c8c1033"},"outputs":[{"name":"stdout","output_type":"stream","text":["100\n","auto\n","None\n","2\n","1\n","True\n"]}],"source":["print(rf.n_estimators)\n","print(rf.max_features)\n","print(rf.max_depth)\n","print(rf.min_samples_split)\n","print(rf.min_samples_leaf)\n","print(rf.bootstrap)"]},{"cell_type":"markdown","metadata":{"id":"VqRTrh5n5CWl"},"source":["## Hyperparameter Tuning:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7vjYY6ue5CWl"},"outputs":[],"source":["n_estimators = [int(x) for x in np.linspace(start=10, stop=100, num=10)]\n","max_features = ['auto', 'sqrt']\n","max_depth = [2, 8, 12, 25, 30]\n","min_samples_split = [2, 5]\n","min_samples_leaf = [3, 5]\n","bootstrap = [True, False]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Snk5616K5CWo"},"outputs":[],"source":["param_grid = {\n","    'n_estimators': n_estimators,\n","    'max_features': max_features,\n","    'max_depth': max_depth,\n","    'min_samples_split': min_samples_split,\n","    'min_samples_leaf': min_samples_leaf,\n","    'bootstrap': bootstrap\n","}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M5lPfPNS5CWp"},"outputs":[],"source":["rf_model = RandomForestClassifier()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"a5f37XME5CWp"},"outputs":[],"source":["grid = GridSearchCV(rf_model, param_grid=param_grid, cv=10, n_jobs=-1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iF5uBdsy5CWp","outputId":"e316187c-2081-4443-dd3c-ea6dada475e4"},"outputs":[{"data":{"text/plain":["GridSearchCV(cv=10, estimator=RandomForestClassifier(), n_jobs=-1,\n","             param_grid={'bootstrap': [True, False],\n","                         'max_depth': [2, 8, 12, 25, 30],\n","                         'max_features': ['auto', 'sqrt'],\n","                         'min_samples_leaf': [3, 5],\n","                         'min_samples_split': [2, 5],\n","                         'n_estimators': [10, 20, 30, 40, 50, 60, 70, 80, 90,\n","                                          100]})"]},"execution_count":50,"metadata":{},"output_type":"execute_result"}],"source":["grid.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VDSqi7Eo5CWp","outputId":"640a5c88-2592-4bf4-f44b-7eccb4f2453e"},"outputs":[{"data":{"text/plain":["0.9845221702010466"]},"execution_count":51,"metadata":{},"output_type":"execute_result"}],"source":["grid.score(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yHXYH2Qs5CWp","outputId":"d2afdfce-9989-4486-a243-c9cbdb61cd15"},"outputs":[{"data":{"text/plain":["RandomForestClassifier(bootstrap=False, max_depth=25, min_samples_leaf=3,\n","                       min_samples_split=5, n_estimators=80)"]},"execution_count":52,"metadata":{},"output_type":"execute_result"}],"source":["grid.best_estimator_"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"B0F-GNi15CWp","outputId":"490bb110-54f5-42c2-c0a1-6a3b2dd0e557"},"outputs":[{"data":{"text/plain":["0.9022310105459885"]},"execution_count":53,"metadata":{},"output_type":"execute_result"}],"source":["grid.best_score_"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"z_fcWh7I5CWq","outputId":"21dec908-ca95-40cd-bbfb-30873daa6495"},"outputs":[{"data":{"text/plain":["{'bootstrap': False,\n"," 'max_depth': 25,\n"," 'max_features': 'auto',\n"," 'min_samples_leaf': 3,\n"," 'min_samples_split': 5,\n"," 'n_estimators': 80}"]},"execution_count":54,"metadata":{},"output_type":"execute_result"}],"source":["grid.best_params_"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ADarfoG75CWq"},"outputs":[],"source":["pred = grid.predict(X_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DXOTYD9l5CWq","outputId":"3abbe7fd-439b-4729-db72-39a2bf225854"},"outputs":[{"data":{"text/plain":["0.8968935888962326"]},"execution_count":56,"metadata":{},"output_type":"execute_result"}],"source":["accuracy_score(y_test, pred)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hZZDaCzsE2gG","outputId":"56d3250c-ed2e-4036-b767-82e1cfad9aba"},"outputs":[{"data":{"text/plain":["0.8967598556138332"]},"execution_count":57,"metadata":{},"output_type":"execute_result"}],"source":["precision_score(y_test, pred, average='weighted')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"F1LG0EmmE7wD","outputId":"a8c78977-7dec-4c1b-c6da-438f8cbcad92"},"outputs":[{"data":{"text/plain":["0.8968935888962326"]},"execution_count":58,"metadata":{},"output_type":"execute_result"}],"source":["recall_score(y_test, pred, average='weighted')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Sx6Gt7Ex5CWq","outputId":"bf18cfcb-f2ef-4e0e-dea1-f65afa1ce922"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.93      0.94      0.94       655\n","           1       0.86      0.85      0.86       662\n","           2       0.89      0.90      0.89       657\n","           3       0.97      0.97      0.97       642\n","           4       1.00      1.00      1.00       628\n","           5       0.81      0.78      0.80       638\n","           6       0.82      0.84      0.83       657\n","\n","    accuracy                           0.90      4539\n","   macro avg       0.90      0.90      0.90      4539\n","weighted avg       0.90      0.90      0.90      4539\n","\n"]}],"source":["print(classification_report(y_test, pred))"]},{"cell_type":"markdown","metadata":{"id":"V06z097j5CWq"},"source":["**Focus on values that get best score**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"H2zDgMVy5CWr"},"outputs":[],"source":["n_estimators = [int(x) for x in np.linspace(start=60, stop=90, num=10)]\n","max_features = ['auto', 'sqrt']\n","max_depth = [26, 28, 30, 32, 34]\n","min_samples_split = [2, 3, 4]\n","min_samples_leaf = [4, 5, 6]\n","bootstrap = [True, False]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"01mzTwVy5CWr"},"outputs":[],"source":["param_grid = {\n","    'n_estimators': n_estimators,\n","    'max_features': max_features,\n","    'max_depth': max_depth,\n","    'min_samples_split': min_samples_split,\n","    'min_samples_leaf': min_samples_leaf,\n","    'bootstrap': bootstrap\n","}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TV_4r8jd5CWr"},"outputs":[],"source":["rf_model = RandomForestClassifier()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"atw_LCtd5CWr"},"outputs":[],"source":["grid = GridSearchCV(rf_model, param_grid=param_grid, cv=10, n_jobs=-1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"d3PMTt6g5CWs","outputId":"7f204bdc-321a-4d47-ad43-aee57bb99427"},"outputs":[{"data":{"text/plain":["GridSearchCV(cv=10, estimator=RandomForestClassifier(), n_jobs=-1,\n","             param_grid={'bootstrap': [True, False],\n","                         'max_depth': [26, 28, 30, 32, 34],\n","                         'max_features': ['auto', 'sqrt'],\n","                         'min_samples_leaf': [4, 5, 6],\n","                         'min_samples_split': [2, 3, 4],\n","                         'n_estimators': [60, 63, 66, 70, 73, 76, 80, 83, 86,\n","                                          90]})"]},"execution_count":338,"metadata":{},"output_type":"execute_result"}],"source":["grid.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"z6cPvXfb5CWs","outputId":"80df4f7e-9b61-49da-e3bd-2c3251853c43"},"outputs":[{"data":{"text/plain":["0.870897320084312"]},"execution_count":339,"metadata":{},"output_type":"execute_result"}],"source":["grid.score(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sIa1yCxH5CWs","outputId":"597c164f-2501-4687-f415-82c1b0874efa"},"outputs":[{"data":{"text/plain":["{'bootstrap': True,\n"," 'max_depth': 32,\n"," 'max_features': 'sqrt',\n"," 'min_samples_leaf': 5,\n"," 'min_samples_split': 3,\n"," 'n_estimators': 73}"]},"execution_count":340,"metadata":{},"output_type":"execute_result"}],"source":["grid.best_params_"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MsSkAzRI5CWs"},"outputs":[],"source":["pred = grid.predict(X_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2dUK78_Z5CWs","outputId":"a76c821f-894d-4a47-be43-72e0aba3fdb4"},"outputs":[{"data":{"text/plain":["0.8154726068633353"]},"execution_count":342,"metadata":{},"output_type":"execute_result"}],"source":["accuracy_score(y_test, pred)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MKYXi2sF5CWs","outputId":"690552f6-b391-4aa8-de3f-27575a4dc6cd"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.81      0.86      0.84       384\n","           1       0.72      0.72      0.72       499\n","           2       0.80      0.82      0.81       493\n","           3       0.94      0.92      0.93       506\n","           4       0.96      0.97      0.96       674\n","           5       0.69      0.62      0.65       382\n","           6       0.67      0.69      0.68       384\n","\n","    accuracy                           0.82      3322\n","   macro avg       0.80      0.80      0.80      3322\n","weighted avg       0.81      0.82      0.81      3322\n","\n"]}],"source":["print(classification_report(y_test, pred))"]},{"cell_type":"markdown","metadata":{"id":"BxXnhp4X5CWt"},"source":["## Logistic Regression"]},{"cell_type":"code","execution_count":35,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UurDEk8y5CWt","outputId":"59b1d5e1-e3b5-4c29-c8bc-375591046cef","executionInfo":{"status":"ok","timestamp":1710713135564,"user_tz":-120,"elapsed":1105,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Training accuracy: 0.656182869732856\n","train Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.71      0.87      0.78      2587\n","           1       0.53      0.39      0.45      2580\n","           2       0.65      0.55      0.59      2585\n","           3       0.78      0.85      0.81      2600\n","           4       0.97      0.99      0.98      2614\n","           5       0.49      0.52      0.51      2604\n","           6       0.41      0.42      0.42      2585\n","\n","    accuracy                           0.66     18155\n","   macro avg       0.65      0.66      0.65     18155\n","weighted avg       0.65      0.66      0.65     18155\n","\n","**************************************************************************\n","Testing accuracy: 0.637144745538665\n","Test Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.69      0.88      0.77       655\n","           1       0.52      0.36      0.43       662\n","           2       0.64      0.54      0.58       657\n","           3       0.77      0.82      0.79       642\n","           4       0.97      0.99      0.98       628\n","           5       0.46      0.49      0.47       638\n","           6       0.38      0.40      0.39       657\n","\n","    accuracy                           0.64      4539\n","   macro avg       0.63      0.64      0.63      4539\n","weighted avg       0.63      0.64      0.63      4539\n","\n"]}],"source":["logistic = LogisticRegression()\n","model_prediction(logistic)"]},{"cell_type":"code","execution_count":36,"metadata":{"id":"waO-v-0A5CWt","executionInfo":{"status":"ok","timestamp":1710713135565,"user_tz":-120,"elapsed":2,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["logistic = LogisticRegression()\n","parameters = {\n","    'penalty': ['l1', 'l2'],\n","    'C': [0.001,0.01, 0.1, 1.0, 10.0],\n","    'solver': ['liblinear', 'saga'],\n","    'max_iter': [100, 200, 300,400]\n","}\n","\n","grid_search = GridSearchCV(logistic, parameters, cv=5, scoring='accuracy')"]},{"cell_type":"code","execution_count":37,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pCoZxUKK5CWt","outputId":"5aa21ded-706d-4590-fc7d-ec64dd102116","executionInfo":{"status":"ok","timestamp":1710714336895,"user_tz":-120,"elapsed":1200713,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Training accuracy: 0.7453043238777196\n","train Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.87      0.96      0.92      2587\n","           1       0.67      0.53      0.59      2580\n","           2       0.65      0.66      0.66      2585\n","           3       0.88      0.97      0.92      2600\n","           4       0.99      1.00      0.99      2614\n","           5       0.56      0.59      0.57      2604\n","           6       0.55      0.51      0.53      2585\n","\n","    accuracy                           0.75     18155\n","   macro avg       0.74      0.74      0.74     18155\n","weighted avg       0.74      0.75      0.74     18155\n","\n","**************************************************************************\n","Testing accuracy: 0.7371667768230887\n","Test Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.88      0.96      0.92       655\n","           1       0.67      0.54      0.60       662\n","           2       0.66      0.67      0.67       657\n","           3       0.89      0.96      0.92       642\n","           4       0.99      1.00      0.99       628\n","           5       0.53      0.57      0.55       638\n","           6       0.51      0.47      0.49       657\n","\n","    accuracy                           0.74      4539\n","   macro avg       0.73      0.74      0.73      4539\n","weighted avg       0.73      0.74      0.73      4539\n","\n"]}],"source":["model_prediction(grid_search)"]},{"cell_type":"markdown","metadata":{"id":"BisUfbgx5CWu"},"source":["## KNN"]},{"cell_type":"code","execution_count":38,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eKN87BKmCHK5","outputId":"299be563-fad5-4f6b-8038-268a074233aa","executionInfo":{"status":"ok","timestamp":1710715945556,"user_tz":-120,"elapsed":3503,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Training accuracy: 0.5340126686863124\n","train Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.51      0.71      0.59      2587\n","           1       0.48      0.56      0.52      2580\n","           2       0.51      0.52      0.52      2585\n","           3       0.59      0.55      0.57      2600\n","           4       0.55      0.48      0.51      2614\n","           5       0.56      0.46      0.50      2604\n","           6       0.58      0.46      0.51      2585\n","\n","    accuracy                           0.53     18155\n","   macro avg       0.54      0.53      0.53     18155\n","weighted avg       0.54      0.53      0.53     18155\n","\n","**************************************************************************\n","Testing accuracy: 0.28288169200264374\n","Test Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.30      0.43      0.36       655\n","           1       0.26      0.31      0.28       662\n","           2       0.27      0.26      0.27       657\n","           3       0.32      0.28      0.30       642\n","           4       0.22      0.19      0.21       628\n","           5       0.27      0.25      0.26       638\n","           6       0.32      0.25      0.28       657\n","\n","    accuracy                           0.28      4539\n","   macro avg       0.28      0.28      0.28      4539\n","weighted avg       0.28      0.28      0.28      4539\n","\n"]}],"source":["knn = KNeighborsClassifier()\n","model_prediction(knn)"]},{"cell_type":"code","execution_count":39,"metadata":{"id":"tGIlnv2HCG-W","executionInfo":{"status":"ok","timestamp":1710715945556,"user_tz":-120,"elapsed":2,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["knn_param_grid = {'n_neighbors': [3, 5, 7, 9,15,20]}\n","knn_grid_search = GridSearchCV(knn, param_grid=knn_param_grid, cv=5)"]},{"cell_type":"code","execution_count":40,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I3E5T4dyCGyV","outputId":"32fecc31-a562-4c9f-d1eb-82ad76063bba","executionInfo":{"status":"ok","timestamp":1710715962814,"user_tz":-120,"elapsed":17259,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Training accuracy: 0.6447810520517764\n","train Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.58      0.88      0.70      2587\n","           1       0.54      0.71      0.61      2580\n","           2       0.58      0.67      0.62      2585\n","           3       0.71      0.64      0.67      2600\n","           4       0.73      0.54      0.62      2614\n","           5       0.78      0.53      0.63      2604\n","           6       0.78      0.55      0.64      2585\n","\n","    accuracy                           0.64     18155\n","   macro avg       0.67      0.65      0.64     18155\n","weighted avg       0.67      0.64      0.64     18155\n","\n","**************************************************************************\n","Testing accuracy: 0.3119629874421679\n","Test Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.32      0.53      0.40       655\n","           1       0.26      0.38      0.31       662\n","           2       0.28      0.29      0.28       657\n","           3       0.38      0.31      0.34       642\n","           4       0.29      0.19      0.23       628\n","           5       0.35      0.22      0.27       638\n","           6       0.38      0.25      0.30       657\n","\n","    accuracy                           0.31      4539\n","   macro avg       0.32      0.31      0.30      4539\n","weighted avg       0.32      0.31      0.31      4539\n","\n"]}],"source":["model_prediction(knn_grid_search)"]},{"cell_type":"markdown","metadata":{"id":"bJsyOx2J5CWu"},"source":["## XGBoost"]},{"cell_type":"code","execution_count":41,"metadata":{"id":"2PqToieX5CWu","outputId":"39f56bdb-9b0e-4d7c-de86-c19b996afc04","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1710715999283,"user_tz":-120,"elapsed":2899,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Training accuracy: 0.9922886257229413\n","train Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00      2587\n","           1       0.99      0.99      0.99      2580\n","           2       0.99      0.99      0.99      2585\n","           3       1.00      1.00      1.00      2600\n","           4       1.00      1.00      1.00      2614\n","           5       0.99      0.98      0.98      2604\n","           6       0.98      0.99      0.98      2585\n","\n","    accuracy                           0.99     18155\n","   macro avg       0.99      0.99      0.99     18155\n","weighted avg       0.99      0.99      0.99     18155\n","\n","**************************************************************************\n","Testing accuracy: 0.9087904824851288\n","Test Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.94      0.96      0.95       655\n","           1       0.89      0.88      0.88       662\n","           2       0.90      0.89      0.89       657\n","           3       0.97      0.96      0.96       642\n","           4       1.00      1.00      1.00       628\n","           5       0.86      0.80      0.83       638\n","           6       0.82      0.87      0.84       657\n","\n","    accuracy                           0.91      4539\n","   macro avg       0.91      0.91      0.91      4539\n","weighted avg       0.91      0.91      0.91      4539\n","\n"]}],"source":["XGBC=XGBClassifier()\n","model_prediction(XGBC)"]},{"cell_type":"code","execution_count":42,"metadata":{"id":"bssqLyJT5CWu","executionInfo":{"status":"ok","timestamp":1710715999283,"user_tz":-120,"elapsed":3,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["xgb = XGBClassifier()\n","xgb_param_grid = {'max_depth': [100],\n","                  'learning_rate': [0.5,0.1, 0.01, 0.0001]}"]},{"cell_type":"code","execution_count":43,"metadata":{"id":"TDlqprzd5CWu","executionInfo":{"status":"ok","timestamp":1710715999283,"user_tz":-120,"elapsed":2,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["xgb_grid_search = GridSearchCV(xgb, param_grid=xgb_param_grid, cv=6)"]},{"cell_type":"code","execution_count":44,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118},"id":"X7uO6aZaHVwx","outputId":"abc3f77b-7657-4bcb-decd-2b4a20ed6f28","executionInfo":{"status":"ok","timestamp":1710716194324,"user_tz":-120,"elapsed":195043,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=6,\n","             estimator=XGBClassifier(base_score=None, booster=None,\n","                                     callbacks=None, colsample_bylevel=None,\n","                                     colsample_bynode=None,\n","                                     colsample_bytree=None, device=None,\n","                                     early_stopping_rounds=None,\n","                                     enable_categorical=False, eval_metric=None,\n","                                     feature_types=None, gamma=None,\n","                                     grow_policy=None, importance_type=None,\n","                                     interaction_constraints=None,\n","                                     learning_rate=None, max_bin=None,\n","                                     max_cat_threshold=None,\n","                                     max_cat_to_onehot=None,\n","                                     max_delta_step=None, max_depth=None,\n","                                     max_leaves=None, min_child_weight=None,\n","                                     missing=nan, monotone_constraints=None,\n","                                     multi_strategy=None, n_estimators=None,\n","                                     n_jobs=None, num_parallel_tree=None,\n","                                     random_state=None, ...),\n","             param_grid={'learning_rate': [0.5, 0.1, 0.01, 0.0001],\n","                         'max_depth': [100]})"],"text/html":["<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=6,\n","             estimator=XGBClassifier(base_score=None, booster=None,\n","                                     callbacks=None, colsample_bylevel=None,\n","                                     colsample_bynode=None,\n","                                     colsample_bytree=None, device=None,\n","                                     early_stopping_rounds=None,\n","                                     enable_categorical=False, eval_metric=None,\n","                                     feature_types=None, gamma=None,\n","                                     grow_policy=None, importance_type=None,\n","                                     interaction_constraints=None,\n","                                     learning_rate=None, max_bin=None,\n","                                     max_cat_threshold=None,\n","                                     max_cat_to_onehot=None,\n","                                     max_delta_step=None, max_depth=None,\n","                                     max_leaves=None, min_child_weight=None,\n","                                     missing=nan, monotone_constraints=None,\n","                                     multi_strategy=None, n_estimators=None,\n","                                     n_jobs=None, num_parallel_tree=None,\n","                                     random_state=None, ...),\n","             param_grid={&#x27;learning_rate&#x27;: [0.5, 0.1, 0.01, 0.0001],\n","                         &#x27;max_depth&#x27;: [100]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=6,\n","             estimator=XGBClassifier(base_score=None, booster=None,\n","                                     callbacks=None, colsample_bylevel=None,\n","                                     colsample_bynode=None,\n","                                     colsample_bytree=None, device=None,\n","                                     early_stopping_rounds=None,\n","                                     enable_categorical=False, eval_metric=None,\n","                                     feature_types=None, gamma=None,\n","                                     grow_policy=None, importance_type=None,\n","                                     interaction_constraints=None,\n","                                     learning_rate=None, max_bin=None,\n","                                     max_cat_threshold=None,\n","                                     max_cat_to_onehot=None,\n","                                     max_delta_step=None, max_depth=None,\n","                                     max_leaves=None, min_child_weight=None,\n","                                     missing=nan, monotone_constraints=None,\n","                                     multi_strategy=None, n_estimators=None,\n","                                     n_jobs=None, num_parallel_tree=None,\n","                                     random_state=None, ...),\n","             param_grid={&#x27;learning_rate&#x27;: [0.5, 0.1, 0.01, 0.0001],\n","                         &#x27;max_depth&#x27;: [100]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n","              colsample_bylevel=None, colsample_bynode=None,\n","              colsample_bytree=None, device=None, early_stopping_rounds=None,\n","              enable_categorical=False, eval_metric=None, feature_types=None,\n","              gamma=None, grow_policy=None, importance_type=None,\n","              interaction_constraints=None, learning_rate=None, max_bin=None,\n","              max_cat_threshold=None, max_cat_to_onehot=None,\n","              max_delta_step=None, max_depth=None, max_leaves=None,\n","              min_child_weight=None, missing=nan, monotone_constraints=None,\n","              multi_strategy=None, n_estimators=None, n_jobs=None,\n","              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n","              colsample_bylevel=None, colsample_bynode=None,\n","              colsample_bytree=None, device=None, early_stopping_rounds=None,\n","              enable_categorical=False, eval_metric=None, feature_types=None,\n","              gamma=None, grow_policy=None, importance_type=None,\n","              interaction_constraints=None, learning_rate=None, max_bin=None,\n","              max_cat_threshold=None, max_cat_to_onehot=None,\n","              max_delta_step=None, max_depth=None, max_leaves=None,\n","              min_child_weight=None, missing=nan, monotone_constraints=None,\n","              multi_strategy=None, n_estimators=None, n_jobs=None,\n","              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":44}],"source":["xgb_grid_search.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":45,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wXKvLw6SHVxE","outputId":"756a7838-ef89-450e-ab80-b80b0be6f08a","executionInfo":{"status":"ok","timestamp":1710716194749,"user_tz":-120,"elapsed":428,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0"]},"metadata":{},"execution_count":45}],"source":["xgb_grid_search.score(X_train, y_train)"]},{"cell_type":"code","execution_count":46,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Dom-f3tyHVxF","outputId":"5df85dda-ad19-4daf-efc2-edbd946b32a1","executionInfo":{"status":"ok","timestamp":1710716194750,"user_tz":-120,"elapsed":8,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'learning_rate': 0.5, 'max_depth': 100}"]},"metadata":{},"execution_count":46}],"source":["xgb_grid_search.best_params_"]},{"cell_type":"code","execution_count":47,"metadata":{"id":"CKkHJyQhHVxF","executionInfo":{"status":"ok","timestamp":1710716194750,"user_tz":-120,"elapsed":6,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["pred = xgb_grid_search.predict(X_test)"]},{"cell_type":"code","execution_count":48,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3gnSoo52HVxG","outputId":"b84eb74c-7ee2-4d2f-d6db-5aea8243bdea","executionInfo":{"status":"ok","timestamp":1710716194750,"user_tz":-120,"elapsed":5,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9052654769773077"]},"metadata":{},"execution_count":48}],"source":["accuracy_score(y_test, pred)"]},{"cell_type":"code","execution_count":49,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xm0qo5PFHgHb","outputId":"e65b44c4-35f4-4562-8aaf-f565ce5cd9df","executionInfo":{"status":"ok","timestamp":1710716194750,"user_tz":-120,"elapsed":3,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.93      0.95      0.94       655\n","           1       0.87      0.88      0.87       662\n","           2       0.88      0.90      0.89       657\n","           3       0.96      0.96      0.96       642\n","           4       1.00      1.00      1.00       628\n","           5       0.85      0.81      0.83       638\n","           6       0.84      0.85      0.85       657\n","\n","    accuracy                           0.91      4539\n","   macro avg       0.91      0.91      0.91      4539\n","weighted avg       0.91      0.91      0.91      4539\n","\n"]}],"source":["print(classification_report(y_test, pred))"]},{"cell_type":"markdown","metadata":{"id":"-owlVgdM5CWv"},"source":["## Gradient Boosting"]},{"cell_type":"code","execution_count":50,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"22X2NOo-7il_","outputId":"d8fc4837-22d6-4477-b197-99cab5a7efae","executionInfo":{"status":"ok","timestamp":1710716266203,"user_tz":-120,"elapsed":29201,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Training accuracy: 0.9163866703387497\n","train Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.94      0.96      0.95      2587\n","           1       0.90      0.89      0.89      2580\n","           2       0.92      0.91      0.91      2585\n","           3       0.97      0.98      0.98      2600\n","           4       1.00      1.00      1.00      2614\n","           5       0.85      0.82      0.83      2604\n","           6       0.84      0.86      0.85      2585\n","\n","    accuracy                           0.92     18155\n","   macro avg       0.92      0.92      0.92     18155\n","weighted avg       0.92      0.92      0.92     18155\n","\n","**************************************************************************\n","Testing accuracy: 0.8977748402731879\n","Test Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.93      0.96      0.94       655\n","           1       0.87      0.86      0.87       662\n","           2       0.88      0.89      0.89       657\n","           3       0.97      0.96      0.96       642\n","           4       1.00      1.00      1.00       628\n","           5       0.83      0.79      0.81       638\n","           6       0.81      0.83      0.82       657\n","\n","    accuracy                           0.90      4539\n","   macro avg       0.90      0.90      0.90      4539\n","weighted avg       0.90      0.90      0.90      4539\n","\n"]}],"source":["gb = GradientBoostingClassifier()\n","model_prediction(gb)"]},{"cell_type":"code","execution_count":51,"metadata":{"id":"frFOki3g5CWv","executionInfo":{"status":"ok","timestamp":1710716266203,"user_tz":-120,"elapsed":4,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["gb = GradientBoostingClassifier()\n","gb_param_grid = {'max_depth': [3, 5, 7], 'learning_rate': [0.1, 0.01, 0.001]}\n","gb_grid_search = GridSearchCV(gb, param_grid=gb_param_grid, cv=5)"]},{"cell_type":"code","execution_count":52,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118},"id":"M5OgTrx_H6wu","outputId":"2ba620c4-943f-42c8-d76c-74c9f748647b","executionInfo":{"status":"ok","timestamp":1710717943001,"user_tz":-120,"elapsed":1676801,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=5, estimator=GradientBoostingClassifier(),\n","             param_grid={'learning_rate': [0.1, 0.01, 0.001],\n","                         'max_depth': [3, 5, 7]})"],"text/html":["<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=GradientBoostingClassifier(),\n","             param_grid={&#x27;learning_rate&#x27;: [0.1, 0.01, 0.001],\n","                         &#x27;max_depth&#x27;: [3, 5, 7]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=GradientBoostingClassifier(),\n","             param_grid={&#x27;learning_rate&#x27;: [0.1, 0.01, 0.001],\n","                         &#x27;max_depth&#x27;: [3, 5, 7]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":52}],"source":["gb_grid_search.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":53,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NLGdC_KKH6wv","outputId":"a930a510-4e52-4fbd-8776-71d8f2a796b9","executionInfo":{"status":"ok","timestamp":1710717943689,"user_tz":-120,"elapsed":695,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9953180941889287"]},"metadata":{},"execution_count":53}],"source":["gb_grid_search.score(X_train, y_train)"]},{"cell_type":"code","execution_count":54,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_EY93iXuH6wv","outputId":"8c83b1d9-94d6-4706-b8e9-232fc15ee370","executionInfo":{"status":"ok","timestamp":1710717943689,"user_tz":-120,"elapsed":41,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'learning_rate': 0.1, 'max_depth': 7}"]},"metadata":{},"execution_count":54}],"source":["gb_grid_search.best_params_"]},{"cell_type":"code","execution_count":55,"metadata":{"id":"qtH0Bko2H6wv","executionInfo":{"status":"ok","timestamp":1710717943689,"user_tz":-120,"elapsed":29,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["pred = gb_grid_search.predict(X_test)"]},{"cell_type":"code","execution_count":56,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Lc77GLMzH6ww","outputId":"3cea3502-bda4-474b-bfa9-c1a4008fafc4","executionInfo":{"status":"ok","timestamp":1710717943690,"user_tz":-120,"elapsed":28,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9039435999118749"]},"metadata":{},"execution_count":56}],"source":["accuracy_score(y_test, pred)"]},{"cell_type":"code","execution_count":57,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DUgRwGH7ICgy","outputId":"f8038336-ea2e-4980-ab76-f2f1e473d4c9","executionInfo":{"status":"ok","timestamp":1710717943690,"user_tz":-120,"elapsed":18,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.93      0.95      0.94       655\n","           1       0.87      0.87      0.87       662\n","           2       0.89      0.90      0.89       657\n","           3       0.97      0.96      0.97       642\n","           4       1.00      1.00      1.00       628\n","           5       0.85      0.80      0.82       638\n","           6       0.82      0.85      0.84       657\n","\n","    accuracy                           0.90      4539\n","   macro avg       0.90      0.90      0.90      4539\n","weighted avg       0.90      0.90      0.90      4539\n","\n"]}],"source":["print(classification_report(y_test, pred))"]},{"cell_type":"markdown","metadata":{"id":"AZPtdE-v5CWw"},"source":["## LightGBM"]},{"cell_type":"code","execution_count":58,"metadata":{"id":"uORKLrBkKNJT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1710717947356,"user_tz":-120,"elapsed":3679,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}},"outputId":"c274cae3-1e43-40f0-9b83-2d9722f29de0"},"outputs":[{"output_type":"stream","name":"stdout","text":["[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.833781\n","[LightGBM] [Debug] Dataset::GetMultiBinFromAllFeatures: sparse rate 0.419557\n","[LightGBM] [Debug] init for col-wise cost 0.002354 seconds, init for row-wise cost 0.004348 seconds\n","[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003469 seconds.\n","You can set `force_row_wise=true` to remove the overhead.\n","And if memory is not enough, you can set `force_col_wise=true`.\n","[LightGBM] [Debug] Using Sparse Multi-Val Bin\n","[LightGBM] [Info] Total Bins 3111\n","[LightGBM] [Info] Number of data points in the train set: 18155, number of used features: 17\n","[LightGBM] [Info] Start training from score -1.948447\n","[LightGBM] [Info] Start training from score -1.951157\n","[LightGBM] [Info] Start training from score -1.949220\n","[LightGBM] [Info] Start training from score -1.943435\n","[LightGBM] [Info] Start training from score -1.938064\n","[LightGBM] [Info] Start training from score -1.941897\n","[LightGBM] [Info] Start training from score -1.949220\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 18\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 17\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 18\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 18\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 19\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 18\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 17\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 18\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 19\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 18\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 17\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 17\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 19\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 17\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 17\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 17\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 17\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 17\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 17\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 20\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 19\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 18\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 17\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 17\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 16\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 18\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 22\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 19\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 17\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 18\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10\n","[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 15\n","Training accuracy: 0.9853483888735886\n","train Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.99      1.00      0.99      2587\n","           1       0.98      0.99      0.98      2580\n","           2       0.99      0.99      0.99      2585\n","           3       1.00      1.00      1.00      2600\n","           4       1.00      1.00      1.00      2614\n","           5       0.97      0.96      0.97      2604\n","           6       0.97      0.97      0.97      2585\n","\n","    accuracy                           0.99     18155\n","   macro avg       0.99      0.99      0.99     18155\n","weighted avg       0.99      0.99      0.99     18155\n","\n","**************************************************************************\n","Testing accuracy: 0.9039435999118749\n","Test Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.93      0.96      0.94       655\n","           1       0.88      0.87      0.87       662\n","           2       0.89      0.90      0.89       657\n","           3       0.97      0.96      0.96       642\n","           4       1.00      1.00      1.00       628\n","           5       0.85      0.79      0.82       638\n","           6       0.82      0.86      0.84       657\n","\n","    accuracy                           0.90      4539\n","   macro avg       0.90      0.90      0.90      4539\n","weighted avg       0.90      0.90      0.90      4539\n","\n"]}],"source":["lgbm = LGBMClassifier(verbose=100)\n","model_prediction(lgbm)"]},{"cell_type":"code","execution_count":59,"metadata":{"id":"dR7sbONs61r5","executionInfo":{"status":"ok","timestamp":1710717947357,"user_tz":-120,"elapsed":11,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["param = {\"objective\": \"multiclass\",\n","    \"metric\": \"multi_logloss\",\n","    \"verbosity\": -1,\n","    \"boosting_type\": \"gbdt\",\n","    \"random_state\": 42,\n","    \"num_class\": 7,\n","    'learning_rate': 0.030962211546832760,\n","    'n_estimators': 500,\n","    'lambda_l1': 0.009667446568254372,\n","    'lambda_l2': 0.04018641437301800,\n","    'max_depth': 50,\n","    'colsample_bytree': 0.40977129346872643,\n","    'subsample': 0.9535797422450176,\n","    'min_child_samples': 26}"]},{"cell_type":"code","execution_count":60,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fDVAcqZa7UHM","outputId":"22a32e08-9569-44ca-e12f-28e4aa5959a0","executionInfo":{"status":"ok","timestamp":1710717968955,"user_tz":-120,"elapsed":21607,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Training accuracy: 0.9868355824841641\n","train Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.99      1.00      1.00      2587\n","           1       0.98      0.99      0.98      2580\n","           2       0.99      0.99      0.99      2585\n","           3       1.00      1.00      1.00      2600\n","           4       1.00      1.00      1.00      2614\n","           5       0.98      0.96      0.97      2604\n","           6       0.97      0.98      0.97      2585\n","\n","    accuracy                           0.99     18155\n","   macro avg       0.99      0.99      0.99     18155\n","weighted avg       0.99      0.99      0.99     18155\n","\n","**************************************************************************\n","Testing accuracy: 0.9103326723948006\n","Test Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.94      0.96      0.95       655\n","           1       0.88      0.89      0.88       662\n","           2       0.89      0.91      0.90       657\n","           3       0.97      0.96      0.96       642\n","           4       1.00      1.00      1.00       628\n","           5       0.85      0.81      0.83       638\n","           6       0.84      0.86      0.85       657\n","\n","    accuracy                           0.91      4539\n","   macro avg       0.91      0.91      0.91      4539\n","weighted avg       0.91      0.91      0.91      4539\n","\n"]}],"source":["lgbm = LGBMClassifier(**param,verbose=100)\n","model_prediction(lgbm)"]},{"cell_type":"markdown","metadata":{"id":"DUgslxVl5CWw"},"source":["## CatBoost"]},{"cell_type":"code","execution_count":61,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KIl0w8Kw5CWw","outputId":"be1b70d0-dc2a-4953-f0b6-016675795ce1","executionInfo":{"status":"ok","timestamp":1710718003198,"user_tz":-120,"elapsed":34278,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Learning rate set to 0.091734\n","0:\tlearn: 1.6763648\ttotal: 44ms\tremaining: 44s\n","300:\tlearn: 0.2308577\ttotal: 10.2s\tremaining: 23.6s\n","600:\tlearn: 0.1782085\ttotal: 20s\tremaining: 13.3s\n","900:\tlearn: 0.1435249\ttotal: 30.6s\tremaining: 3.36s\n","999:\tlearn: 0.1350299\ttotal: 33.9s\tremaining: 0us\n","Training accuracy: 0.9620490223079041\n","train Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.97      0.99      0.98      2587\n","           1       0.96      0.95      0.96      2580\n","           2       0.96      0.95      0.96      2585\n","           3       0.98      0.99      0.99      2600\n","           4       1.00      1.00      1.00      2614\n","           5       0.94      0.92      0.93      2604\n","           6       0.92      0.94      0.93      2585\n","\n","    accuracy                           0.96     18155\n","   macro avg       0.96      0.96      0.96     18155\n","weighted avg       0.96      0.96      0.96     18155\n","\n","**************************************************************************\n","Testing accuracy: 0.905045164133069\n","Test Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.94      0.96      0.95       655\n","           1       0.89      0.88      0.88       662\n","           2       0.89      0.88      0.89       657\n","           3       0.96      0.96      0.96       642\n","           4       1.00      1.00      1.00       628\n","           5       0.85      0.81      0.83       638\n","           6       0.82      0.86      0.84       657\n","\n","    accuracy                           0.91      4539\n","   macro avg       0.91      0.91      0.91      4539\n","weighted avg       0.91      0.91      0.90      4539\n","\n"]}],"source":["CATB=CatBoostClassifier(verbose=300)\n","model_prediction(CATB)"]},{"cell_type":"markdown","metadata":{"id":"L5ITpIghBVnO"},"source":["## Voting Classifier"]},{"cell_type":"code","execution_count":62,"metadata":{"executionInfo":{"elapsed":32,"status":"ok","timestamp":1710718003199,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"wyOoMitX5CWw"},"outputs":[],"source":["voting_clf = VotingClassifier([('lr',LogisticRegression()),\n","                               ('gbc',GradientBoostingClassifier()),\n","                               ('dt',DecisionTreeClassifier(max_depth=30, max_features='sqrt', min_samples_leaf=5, min_samples_split=5)),\n","                               ('random',RandomForestClassifier(max_depth=30, min_samples_leaf=5, min_samples_split=5)),\n","                               ('xgboost', XGBClassifier(learning_rate=0.5, max_depth=100)),\n","                               ('gradientboost', GradientBoostingClassifier(learning_rate= 0.1, max_depth=5)),\n","                               ('lightgbm', LGBMClassifier(objective='multiclass', metric='multi_logloss', verbosity=-1, boosting_type='gbdt',\n","                                                           random_state=42, num_class=7, learning_rate=0.03, n_estimators=500,\n","                                                           lambda_l1=0.009, lambda_l2=0.04, max_depth=50, colsample_bytree=0.4,\n","                                                           subsample=0.95, min_child_samples=26)),\n","                               ('catboost', CatBoostClassifier(verbose=300))], voting='soft')"]},{"cell_type":"code","execution_count":63,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":281502,"status":"ok","timestamp":1710718284671,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"00VgzNhTEVxi","outputId":"6bf8d4bf-99c1-4dc3-ba9b-fd081a9d510c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Learning rate set to 0.089857\n","0:\tlearn: 1.6841980\ttotal: 39.3ms\tremaining: 39.2s\n","300:\tlearn: 0.2207886\ttotal: 9.76s\tremaining: 22.7s\n","600:\tlearn: 0.1621978\ttotal: 18.9s\tremaining: 12.5s\n","900:\tlearn: 0.1267584\ttotal: 27.7s\tremaining: 3.04s\n","999:\tlearn: 0.1173876\ttotal: 30.2s\tremaining: 0us\n","Learning rate set to 0.089857\n","0:\tlearn: 1.6806934\ttotal: 35.7ms\tremaining: 35.6s\n","300:\tlearn: 0.2284804\ttotal: 8.81s\tremaining: 20.5s\n","600:\tlearn: 0.1687097\ttotal: 16.4s\tremaining: 10.9s\n","900:\tlearn: 0.1319316\ttotal: 25.1s\tremaining: 2.76s\n","999:\tlearn: 0.1226581\ttotal: 27.7s\tremaining: 0us\n","Learning rate set to 0.089857\n","0:\tlearn: 1.6731894\ttotal: 35.6ms\tremaining: 35.6s\n","300:\tlearn: 0.2246395\ttotal: 7.66s\tremaining: 17.8s\n","600:\tlearn: 0.1640053\ttotal: 16.4s\tremaining: 10.9s\n","900:\tlearn: 0.1266834\ttotal: 25.1s\tremaining: 2.76s\n","999:\tlearn: 0.1167551\ttotal: 27.7s\tremaining: 0us\n"]},{"output_type":"execute_result","data":{"text/plain":["array([0.90416391, 0.91093853, 0.90365229])"]},"metadata":{},"execution_count":63}],"source":["score = cross_val_score(voting_clf, X_train, y_train, cv=3, scoring='accuracy')\n","score"]},{"cell_type":"code","execution_count":64,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":214},"executionInfo":{"elapsed":125672,"status":"ok","timestamp":1710718410335,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"ldY-y4ZiEWOj","outputId":"925d7a0b-c415-4f66-f0a7-84895c4ee992"},"outputs":[{"output_type":"stream","name":"stdout","text":["Learning rate set to 0.091734\n","0:\tlearn: 1.6763648\ttotal: 76.2ms\tremaining: 1m 16s\n","300:\tlearn: 0.2308577\ttotal: 10.2s\tremaining: 23.7s\n","600:\tlearn: 0.1782085\ttotal: 20.5s\tremaining: 13.6s\n","900:\tlearn: 0.1435249\ttotal: 31.1s\tremaining: 3.41s\n","999:\tlearn: 0.1350299\ttotal: 34.2s\tremaining: 0us\n"]},{"output_type":"execute_result","data":{"text/plain":["VotingClassifier(estimators=[('lr', LogisticRegression()),\n","                             ('gbc', GradientBoostingClassifier()),\n","                             ('dt',\n","                              DecisionTreeClassifier(max_depth=30,\n","                                                     max_features='sqrt',\n","                                                     min_samples_leaf=5,\n","                                                     min_samples_split=5)),\n","                             ('random',\n","                              RandomForestClassifier(max_depth=30,\n","                                                     min_samples_leaf=5,\n","                                                     min_samples_split=5)),\n","                             ('xgboost',\n","                              XGBClassifier(base_score=None, booster=None,\n","                                            callbacks=No...\n","                              GradientBoostingClassifier(max_depth=5)),\n","                             ('lightgbm',\n","                              LGBMClassifier(colsample_bytree=0.4,\n","                                             lambda_l1=0.009, lambda_l2=0.04,\n","                                             learning_rate=0.03, max_depth=50,\n","                                             metric='multi_logloss',\n","                                             min_child_samples=26,\n","                                             n_estimators=500, num_class=7,\n","                                             objective='multiclass',\n","                                             random_state=42, subsample=0.95,\n","                                             verbosity=-1)),\n","                             ('catboost',\n","                              <catboost.core.CatBoostClassifier object at 0x7e3d31c8ba90>)],\n","                 voting='soft')"],"text/html":["<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 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1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;, LogisticRegression()),\n","                             (&#x27;gbc&#x27;, GradientBoostingClassifier()),\n","                             (&#x27;dt&#x27;,\n","                              DecisionTreeClassifier(max_depth=30,\n","                                                     max_features=&#x27;sqrt&#x27;,\n","                                                     min_samples_leaf=5,\n","                                                     min_samples_split=5)),\n","                             (&#x27;random&#x27;,\n","                              RandomForestClassifier(max_depth=30,\n","                                                     min_samples_leaf=5,\n","                                                     min_samples_split=5)),\n","                             (&#x27;xgboost&#x27;,\n","                              XGBClassifier(base_score=None, booster=None,\n","                                            callbacks=No...\n","                              GradientBoostingClassifier(max_depth=5)),\n","                             (&#x27;lightgbm&#x27;,\n","                              LGBMClassifier(colsample_bytree=0.4,\n","                                             lambda_l1=0.009, lambda_l2=0.04,\n","                                             learning_rate=0.03, max_depth=50,\n","                                             metric=&#x27;multi_logloss&#x27;,\n","                                             min_child_samples=26,\n","                                             n_estimators=500, num_class=7,\n","                                             objective=&#x27;multiclass&#x27;,\n","                                             random_state=42, subsample=0.95,\n","                                             verbosity=-1)),\n","                             (&#x27;catboost&#x27;,\n","                              &lt;catboost.core.CatBoostClassifier object at 0x7e3d31c8ba90&gt;)],\n","                 voting=&#x27;soft&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;, LogisticRegression()),\n","                             (&#x27;gbc&#x27;, GradientBoostingClassifier()),\n","                             (&#x27;dt&#x27;,\n","                              DecisionTreeClassifier(max_depth=30,\n","                                                     max_features=&#x27;sqrt&#x27;,\n","                                                     min_samples_leaf=5,\n","                                                     min_samples_split=5)),\n","                             (&#x27;random&#x27;,\n","                              RandomForestClassifier(max_depth=30,\n","                                                     min_samples_leaf=5,\n","                                                     min_samples_split=5)),\n","                             (&#x27;xgboost&#x27;,\n","                              XGBClassifier(base_score=None, booster=None,\n","                                            callbacks=No...\n","                              GradientBoostingClassifier(max_depth=5)),\n","                             (&#x27;lightgbm&#x27;,\n","                              LGBMClassifier(colsample_bytree=0.4,\n","                                             lambda_l1=0.009, lambda_l2=0.04,\n","                                             learning_rate=0.03, max_depth=50,\n","                                             metric=&#x27;multi_logloss&#x27;,\n","                                             min_child_samples=26,\n","                                             n_estimators=500, num_class=7,\n","                                             objective=&#x27;multiclass&#x27;,\n","                                             random_state=42, subsample=0.95,\n","                                             verbosity=-1)),\n","                             (&#x27;catboost&#x27;,\n","                              &lt;catboost.core.CatBoostClassifier object at 0x7e3d31c8ba90&gt;)],\n","                 voting=&#x27;soft&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>gbc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>dt</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=30, max_features=&#x27;sqrt&#x27;, min_samples_leaf=5,\n","                       min_samples_split=5)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>random</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=30, min_samples_leaf=5, min_samples_split=5)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>xgboost</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n","              colsample_bylevel=None, colsample_bynode=None,\n","              colsample_bytree=None, device=None, early_stopping_rounds=None,\n","              enable_categorical=False, eval_metric=None, feature_types=None,\n","              gamma=None, grow_policy=None, importance_type=None,\n","              interaction_constraints=None, learning_rate=0.5, max_bin=None,\n","              max_cat_threshold=None, max_cat_to_onehot=None,\n","              max_delta_step=None, max_depth=100, max_leaves=None,\n","              min_child_weight=None, missing=nan, monotone_constraints=None,\n","              multi_strategy=None, n_estimators=None, n_jobs=None,\n","              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>gradientboost</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(max_depth=5)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lightgbm</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(colsample_bytree=0.4, lambda_l1=0.009, lambda_l2=0.04,\n","               learning_rate=0.03, max_depth=50, metric=&#x27;multi_logloss&#x27;,\n","               min_child_samples=26, n_estimators=500, num_class=7,\n","               objective=&#x27;multiclass&#x27;, random_state=42, subsample=0.95,\n","               verbosity=-1)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>catboost</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CatBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x7e3d31c8ba90&gt;</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":64}],"source":["voting_clf.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":65,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9559,"status":"ok","timestamp":1710718419885,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"9zpjCSypEW0K","outputId":"ab91344c-a3ef-49e5-cd67-a371cd9760e1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9767557146791518"]},"metadata":{},"execution_count":65}],"source":["voting_clf.score(X_train, y_train)"]},{"cell_type":"code","execution_count":66,"metadata":{"executionInfo":{"elapsed":2400,"status":"ok","timestamp":1710718422252,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"wBh6S3rjOrG1"},"outputs":[],"source":["pred = voting_clf.predict(X_test)"]},{"cell_type":"code","execution_count":67,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":28,"status":"ok","timestamp":1710718422252,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"DFjLeBBKEh4Z","outputId":"6769c559-cb5c-4776-d438-1223a3d34ff4"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9076889182639348"]},"metadata":{},"execution_count":67}],"source":["score = accuracy_score(y_test, pred)\n","score"]},{"cell_type":"code","execution_count":68,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23,"status":"ok","timestamp":1710718422252,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"xW2T3DmwDI0N","outputId":"bdecaa1f-7f35-4db0-abc3-81853ae700ba"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9076383461337466"]},"metadata":{},"execution_count":68}],"source":["precision_score(y_test, pred, average='weighted')"]},{"cell_type":"code","execution_count":69,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1710718422253,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"aNrHR-oADIk6","outputId":"b34ec5dc-c4b3-4716-c323-288fd918c788"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9076889182639348"]},"metadata":{},"execution_count":69}],"source":["recall_score(y_test, pred, average='weighted')"]},{"cell_type":"code","execution_count":70,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1710718422253,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"},"user_tz":-120},"id":"_O4w8WuEElpT","outputId":"30b30d00-c30b-4442-c346-ade4669953d8"},"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.94      0.96      0.95       655\n","           1       0.88      0.88      0.88       662\n","           2       0.90      0.90      0.90       657\n","           3       0.96      0.96      0.96       642\n","           4       1.00      1.00      1.00       628\n","           5       0.85      0.79      0.82       638\n","           6       0.82      0.86      0.84       657\n","\n","    accuracy                           0.91      4539\n","   macro avg       0.91      0.91      0.91      4539\n","weighted avg       0.91      0.91      0.91      4539\n","\n"]}],"source":["print(classification_report(y_test, pred))"]},{"cell_type":"markdown","metadata":{"id":"_audYA3-P8js"},"source":["## Pipeline"]},{"cell_type":"markdown","source":["- We have done pipeline and use Voting Classifier with it."],"metadata":{"id":"IodY3Hrk505v"}},{"cell_type":"code","execution_count":29,"metadata":{"id":"qkwWb7kNP-JF","executionInfo":{"status":"ok","timestamp":1710712677194,"user_tz":-120,"elapsed":291,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["# Encode target labels\n","label_encoder = LabelEncoder()\n","y_train_encoded = label_encoder.fit_transform(y_train)\n","y_test_encoded = label_encoder.transform(y_test)\n","\n","# Extracting numeric and object columns\n","numeric_columns = X.select_dtypes(include=['int64', 'float64']).columns\n","object_columns = X.select_dtypes(include=['object']).columns\n","\n","numeric_transformer = Pipeline(steps=[('scaler', StandardScaler())])\n","\n","categorical_transformer = Pipeline(steps=[('encoder', OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1))])\n","\n","# Create preprocessor\n","preprocessor = ColumnTransformer(\n","    transformers=[\n","        ('num', numeric_transformer, numeric_columns),\n","        ('cat', categorical_transformer, object_columns)])\n","\n","# Fit and transform the data\n","transformed_data = preprocessor.fit_transform(X_train)\n","\n","# Specify column names for the transformed DataFrame\n","all_columns = list(numeric_columns) + list(preprocessor.named_transformers_['cat']['encoder'].get_feature_names_out(object_columns))\n","\n","# Creating a DataFrame with transformed data and column names\n","transformed_train = pd.DataFrame(transformed_data, columns=all_columns)"]},{"cell_type":"code","execution_count":30,"metadata":{"id":"bwy-UyvzIo7S","executionInfo":{"status":"ok","timestamp":1710712679819,"user_tz":-120,"elapsed":1,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["def evaluate_model_2(model, X_valid, y_valid):\n","    # Transform validation data\n","    transformed_valid = preprocessor.transform(X_valid)\n","\n","    # Make predictions\n","    preds = model.predict(transformed_valid)\n","\n","    # Calculate metrics\n","    accuracy = accuracy_score(y_valid, preds)\n","    precision = precision_score(y_valid, preds, average='weighted')\n","    recall = recall_score(y_valid, preds, average='weighted')\n","    report = classification_report(y_valid, preds)\n","    cm = confusion_matrix(y_valid, preds)\n","\n","    # Print results\n","    print('Accuracy:', accuracy)\n","    print('Precision:', precision)\n","    print('Recall:', recall)\n","    print('Classification Report:\\n', report)\n","\n","    # Plot confusion matrix\n","    plt.figure(figsize=(8, 6))\n","    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n","    plt.xlabel('Predicted labels')\n","    plt.ylabel('True labels')\n","    plt.title('Confusion Matrix')\n","    plt.show()"]},{"cell_type":"code","execution_count":33,"metadata":{"id":"jGw9lWUmKpvn","executionInfo":{"status":"ok","timestamp":1710712729001,"user_tz":-120,"elapsed":290,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}}},"outputs":[],"source":["votClv_model = VotingClassifier([('lr',LogisticRegression()),\n","                               ('gbc',GradientBoostingClassifier()),\n","                               ('dt',DecisionTreeClassifier(max_depth=30, max_features='sqrt', min_samples_leaf=5, min_samples_split=5)),\n","                               ('random',RandomForestClassifier(max_depth=30, min_samples_leaf=5, min_samples_split=5)),\n","                               ('xgboost', XGBClassifier(learning_rate=0.5, max_depth=100)),\n","                               ('gradientboost', GradientBoostingClassifier(learning_rate= 0.1, max_depth=5)),\n","                               ('lightgbm', LGBMClassifier(objective='multiclass', metric='multi_logloss', verbosity=-1, boosting_type='gbdt',\n","                                                           random_state=42, num_class=7, learning_rate=0.03, n_estimators=500,\n","                                                           lambda_l1=0.009, lambda_l2=0.04, max_depth=50, colsample_bytree=0.4,\n","                                                           subsample=0.95, min_child_samples=26)),\n","                               ('catboost', CatBoostClassifier(verbose=300))], voting='soft')"]},{"cell_type":"code","execution_count":34,"metadata":{"id":"apum4deyLLfW","colab":{"base_uri":"https://localhost:8080/","height":981},"executionInfo":{"status":"ok","timestamp":1710712865527,"user_tz":-120,"elapsed":134752,"user":{"displayName":"Eman Ramzy","userId":"07016101921187405931"}},"outputId":"8a146368-a23f-4811-97a2-640954958a2a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Learning rate set to 0.091734\n","0:\tlearn: 1.6763648\ttotal: 90.9ms\tremaining: 1m 30s\n","300:\tlearn: 0.2308577\ttotal: 10.7s\tremaining: 25s\n","600:\tlearn: 0.1782085\ttotal: 21.3s\tremaining: 14.1s\n","900:\tlearn: 0.1435249\ttotal: 32s\tremaining: 3.51s\n","999:\tlearn: 0.1350299\ttotal: 35.1s\tremaining: 0us\n","Accuracy: 0.9070279797312183\n","Precision: 0.9068963580739051\n","Recall: 0.9070279797312183\n","Classification Report:\n","               precision    recall  f1-score   support\n","\n","           0       0.94      0.97      0.95       655\n","           1       0.89      0.87      0.88       662\n","           2       0.89      0.89      0.89       657\n","           3       0.97      0.96      0.96       642\n","           4       1.00      1.00      1.00       628\n","           5       0.84      0.80      0.82       638\n","           6       0.83      0.86      0.84       657\n","\n","    accuracy                           0.91      4539\n","   macro avg       0.91      0.91      0.91      4539\n","weighted avg       0.91      0.91      0.91      4539\n","\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Fit the VotingClassifier to the training data\n","votClv_model.fit(transformed_train, y_train)\n","# Now you can use the evaluation function\n","evaluate_model_2(votClv_model, X_test, y_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cyCh3tUNLS_M"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
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