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lung_cancer_prediction_ml_model_analysis.ipynb
4987 lines (4987 with data), 529.0 kB
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"<a href=\"https://colab.research.google.com/github/Aditya-567/Lung-AND-Oral-Cancer-ML-Model/blob/lextrone/lung_cancer_prediction_ml_model_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"source": [
"# Lung Cancer Prediction\n",
"\n"
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"id": "GP93lbtBZbPV"
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"Lung cancer prediction using machine learning classification models using Scikit-learn library in Python is a code implementation that aims to develop a predictive model for detecting lung cancer in patients. The code uses different machine learning algorithms, including **logistic regression, decision tree, k-nearest neighbour, Gaussian naive Bayes, multinomial naive Bayes, support vector classifier, random forest** to predict the likelihood of lung cancer based on a range of variables. The dataset used in the code includes various columns such as gender, age, smoking, yellow fingers, anxiety, peer pressure, chronic disease, fatigue, allergy, wheezing, alcohol consuming, coughing, shortness of breath, swallowing difficulty, chest pain, and lung cancer. By analysing these variables and using machine learning algorithms to identify patterns and correlations, the predictive models can provide accurate assessments of a patient's risk of developing lung cancer"
],
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"#Importing Libraries\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"#For ignoring warning\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
],
"metadata": {
"id": "otKLZ-Q3agYe"
},
"execution_count": null,
"outputs": []
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"df=pd.read_csv('/content/sample_data/survey lung cancer.csv')\n",
"df"
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" GENDER AGE SMOKING YELLOW_FINGERS ANXIETY PEER_PRESSURE \\\n",
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{
"cell_type": "markdown",
"source": [
"**Note: In this dataset, YES=2 & NO=1**"
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"(309, 16)"
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"execution_count": 4
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{
"cell_type": "code",
"source": [
"#Checking for Duplicates\n",
"df.duplicated().sum()"
],
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{
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"33"
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"metadata": {},
"execution_count": 5
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{
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"#Removing Duplicates\n",
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"outputs": []
},
{
"cell_type": "code",
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"#Checking for null values\n",
"df.isnull().sum()"
],
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},
"id": "V96H8vobbt5N",
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{
"output_type": "execute_result",
"data": {
"text/plain": [
"GENDER 0\n",
"AGE 0\n",
"SMOKING 0\n",
"YELLOW_FINGERS 0\n",
"ANXIETY 0\n",
"PEER_PRESSURE 0\n",
"CHRONIC DISEASE 0\n",
"FATIGUE 0\n",
"ALLERGY 0\n",
"WHEEZING 0\n",
"ALCOHOL CONSUMING 0\n",
"COUGHING 0\n",
"SHORTNESS OF BREATH 0\n",
"SWALLOWING DIFFICULTY 0\n",
"CHEST PAIN 0\n",
"LUNG_CANCER 0\n",
"dtype: int64"
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},
"metadata": {},
"execution_count": 7
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{
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"<class 'pandas.core.frame.DataFrame'>\n",
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" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 GENDER 276 non-null object\n",
" 1 AGE 276 non-null int64 \n",
" 2 SMOKING 276 non-null int64 \n",
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" 6 CHRONIC DISEASE 276 non-null int64 \n",
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" 8 ALLERGY 276 non-null int64 \n",
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" 10 ALCOHOL CONSUMING 276 non-null int64 \n",
" 11 COUGHING 276 non-null int64 \n",
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" 13 SWALLOWING DIFFICULTY 276 non-null int64 \n",
" 14 CHEST PAIN 276 non-null int64 \n",
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"dtypes: int64(14), object(2)\n",
"memory usage: 36.7+ KB\n"
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" AGE SMOKING YELLOW_FINGERS ANXIETY PEER_PRESSURE \\\n",
"count 276.000000 276.000000 276.000000 276.000000 276.000000 \n",
"mean 62.909420 1.543478 1.576087 1.496377 1.507246 \n",
"std 8.379355 0.499011 0.495075 0.500895 0.500856 \n",
"min 21.000000 1.000000 1.000000 1.000000 1.000000 \n",
"25% 57.750000 1.000000 1.000000 1.000000 1.000000 \n",
"50% 62.500000 2.000000 2.000000 1.000000 2.000000 \n",
"75% 69.000000 2.000000 2.000000 2.000000 2.000000 \n",
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"\n",
" CHRONIC DISEASE FATIGUE ALLERGY WHEEZING ALCOHOL CONSUMING \\\n",
"count 276.000000 276.000000 276.000000 276.000000 276.000000 \n",
"mean 1.521739 1.663043 1.547101 1.547101 1.550725 \n",
"std 0.500435 0.473529 0.498681 0.498681 0.498324 \n",
"min 1.000000 1.000000 1.000000 1.000000 1.000000 \n",
"25% 1.000000 1.000000 1.000000 1.000000 1.000000 \n",
"50% 2.000000 2.000000 2.000000 2.000000 2.000000 \n",
"75% 2.000000 2.000000 2.000000 2.000000 2.000000 \n",
"max 2.000000 2.000000 2.000000 2.000000 2.000000 \n",
"\n",
" COUGHING SHORTNESS OF BREATH SWALLOWING DIFFICULTY CHEST PAIN \n",
"count 276.000000 276.000000 276.000000 276.000000 \n",
"mean 1.576087 1.630435 1.467391 1.557971 \n",
"std 0.495075 0.483564 0.499842 0.497530 \n",
"min 1.000000 1.000000 1.000000 1.000000 \n",
"25% 1.000000 1.000000 1.000000 1.000000 \n",
"50% 2.000000 2.000000 1.000000 2.000000 \n",
"75% 2.000000 2.000000 2.000000 2.000000 \n",
"max 2.000000 2.000000 2.000000 2.000000 "
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" <td>0.500856</td>\n",
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],
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"type": "dataframe",
"summary": "{\n \"name\": \"df\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"AGE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 83.05535348820949,\n \"min\": 8.37935460896089,\n \"max\": 276.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 62.90942028985507,\n 62.5,\n 276.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SMOKING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 97.07514993020912,\n \"min\": 0.4990108793478454,\n \"max\": 276.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.5434782608695652,\n 2.0,\n 0.4990108793478454\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"YELLOW_FINGERS\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 97.073713131455,\n \"min\": 0.4950745542130488,\n \"max\": 276.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.576086956521739,\n 2.0,\n 0.4950745542130488\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ANXIETY\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 97.12772107886539,\n \"min\": 0.5008951144486481,\n \"max\": 276.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.4963768115942029,\n 2.0,\n 0.5008951144486481\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PEER_PRESSURE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 97.07687921314712,\n \"min\": 0.5008556578364916,\n \"max\": 276.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.5072463768115942,\n 2.0,\n 0.5008556578364916\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"CHRONIC DISEASE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 97.07617086010801,\n \"min\": 0.5004345937369794,\n \"max\": 276.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.5217391304347827,\n 2.0,\n 0.5004345937369794\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"FATIGUE \",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 97.0704624150259,\n \"min\": 0.4735285091859388,\n \"max\": 276.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.6630434782608696,\n 2.0,\n 0.4735285091859388\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ALLERGY \",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 97.07498465257831,\n \"min\": 0.49868073648713007,\n \"max\": 276.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.5471014492753623,\n 2.0,\n 0.49868073648713007\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"WHEEZING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 97.07498465257831,\n \"min\": 0.49868073648713007,\n \"max\": 276.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.5471014492753623,\n 2.0,\n 0.49868073648713007\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ALCOHOL CONSUMING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 97.07482077528157,\n \"min\": 0.4983239365292922,\n \"max\": 276.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.5507246376811594,\n 2.0,\n 0.4983239365292922\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"COUGHING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 97.073713131455,\n \"min\": 0.4950745542130488,\n \"max\": 276.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.576086956521739,\n 2.0,\n 0.4950745542130488\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SHORTNESS OF BREATH\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 97.07157861616834,\n \"min\": 0.48356384490040366,\n \"max\": 276.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.6304347826086956,\n 2.0,\n 0.48356384490040366\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SWALLOWING DIFFICULTY\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 97.12923098421734,\n \"min\": 0.49984187222880955,\n \"max\": 276.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.4673913043478262,\n 2.0,\n 0.49984187222880955\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"CHEST PAIN\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 97.07449723420716,\n \"min\": 0.4975301316343141,\n \"max\": 276.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.5579710144927537,\n 2.0,\n 0.4975301316343141\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"source": [
"**In this dataset, GENDER & LUNG_CANCER attributes are in object data type. So, let's convert them to numerical values using LabelEncoder from sklearn. LabelEncoder is a utility class to help normalize labels such that they contain only values between 0 and n_classes-1. It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels. Also let's make every other attributes as YES=1 & NO=0.**"
],
"metadata": {
"id": "e33O2Dqwb-IM"
}
},
{
"cell_type": "code",
"source": [
"from sklearn import preprocessing\n",
"le=preprocessing.LabelEncoder()\n",
"df['GENDER']=le.fit_transform(df['GENDER'])\n",
"df['LUNG_CANCER']=le.fit_transform(df['LUNG_CANCER'])\n",
"df['SMOKING']=le.fit_transform(df['SMOKING'])\n",
"df['YELLOW_FINGERS']=le.fit_transform(df['YELLOW_FINGERS'])\n",
"df['ANXIETY']=le.fit_transform(df['ANXIETY'])\n",
"df['PEER_PRESSURE']=le.fit_transform(df['PEER_PRESSURE'])\n",
"df['CHRONIC DISEASE']=le.fit_transform(df['CHRONIC DISEASE'])\n",
"df['FATIGUE ']=le.fit_transform(df['FATIGUE '])\n",
"df['ALLERGY ']=le.fit_transform(df['ALLERGY '])\n",
"df['WHEEZING']=le.fit_transform(df['WHEEZING'])\n",
"df['ALCOHOL CONSUMING']=le.fit_transform(df['ALCOHOL CONSUMING'])\n",
"df['COUGHING']=le.fit_transform(df['COUGHING'])\n",
"df['SHORTNESS OF BREATH']=le.fit_transform(df['SHORTNESS OF BREATH'])\n",
"df['SWALLOWING DIFFICULTY']=le.fit_transform(df['SWALLOWING DIFFICULTY'])\n",
"df['CHEST PAIN']=le.fit_transform(df['CHEST PAIN'])\n",
"df['LUNG_CANCER']=le.fit_transform(df['LUNG_CANCER'])"
],
"metadata": {
"id": "2H1O13m0cEAH"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#Let's check what's happened now\n",
"df"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "ZVObwCq_cTE0",
"outputId": "f24c7583-97df-4882-edac-b6f4a1dff6e3"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" GENDER AGE SMOKING YELLOW_FINGERS ANXIETY PEER_PRESSURE \\\n",
"0 1 69 0 1 1 0 \n",
"1 1 74 1 0 0 0 \n",
"2 0 59 0 0 0 1 \n",
"3 1 63 1 1 1 0 \n",
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".. ... ... ... ... ... ... \n",
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" CHRONIC DISEASE FATIGUE ALLERGY WHEEZING ALCOHOL CONSUMING \\\n",
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"0 1 \n",
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"[276 rows x 16 columns]"
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"type": "dataframe",
"variable_name": "df",
"summary": "{\n \"name\": \"df\",\n \"rows\": 276,\n \"fields\": [\n {\n \"column\": \"GENDER\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"AGE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 8,\n \"min\": 21,\n \"max\": 87,\n \"num_unique_values\": 39,\n \"samples\": [\n 81,\n 39\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SMOKING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"YELLOW_FINGERS\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ANXIETY\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PEER_PRESSURE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"CHRONIC DISEASE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"FATIGUE \",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ALLERGY \",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"WHEEZING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ALCOHOL CONSUMING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"COUGHING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SHORTNESS OF BREATH\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SWALLOWING DIFFICULTY\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"CHEST PAIN\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"LUNG_CANCER\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"source": [
"**Note: Male=1 & Female=0. Also for other variables, YES=1 & NO=0**"
],
"metadata": {
"id": "yWw4Pl6JcY4n"
}
},
{
"cell_type": "code",
"source": [
"df.info()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Hnf_n6LqcclC",
"outputId": "f3309cd4-24f8-4a6c-b3f6-472b2e77ba24"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 276 entries, 0 to 283\n",
"Data columns (total 16 columns):\n",
" # Column Non-Null Count Dtype\n",
"--- ------ -------------- -----\n",
" 0 GENDER 276 non-null int64\n",
" 1 AGE 276 non-null int64\n",
" 2 SMOKING 276 non-null int64\n",
" 3 YELLOW_FINGERS 276 non-null int64\n",
" 4 ANXIETY 276 non-null int64\n",
" 5 PEER_PRESSURE 276 non-null int64\n",
" 6 CHRONIC DISEASE 276 non-null int64\n",
" 7 FATIGUE 276 non-null int64\n",
" 8 ALLERGY 276 non-null int64\n",
" 9 WHEEZING 276 non-null int64\n",
" 10 ALCOHOL CONSUMING 276 non-null int64\n",
" 11 COUGHING 276 non-null int64\n",
" 12 SHORTNESS OF BREATH 276 non-null int64\n",
" 13 SWALLOWING DIFFICULTY 276 non-null int64\n",
" 14 CHEST PAIN 276 non-null int64\n",
" 15 LUNG_CANCER 276 non-null int64\n",
"dtypes: int64(16)\n",
"memory usage: 36.7 KB\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**That is, Target Distribution is imbalanced.**"
],
"metadata": {
"id": "Qm3R1V2Lcqpz"
}
},
{
"cell_type": "code",
"source": [
"df['LUNG_CANCER'].value_counts()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-kmfmRoBctWR",
"outputId": "c524fab4-f126-425e-8b14-2f75f90bdc9a"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1 238\n",
"0 38\n",
"Name: LUNG_CANCER, dtype: int64"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "markdown",
"source": [
"**We will handle this imbalance before applyig algorithm.**"
],
"metadata": {
"id": "TnpgLqVMczTf"
}
},
{
"cell_type": "markdown",
"source": [
"**From the visualizations from 1 colab, it is clear that in the given dataset, the features GENDER, AGE, SMOKING and SHORTNESS OF BREATH don't have that much relationship with LUNG CANCER. So let's drop those features to make this dataset more clean.**"
],
"metadata": {
"id": "irOaPmKrd6G3"
}
},
{
"cell_type": "code",
"source": [
"df_new=df.drop(columns=['GENDER','AGE', 'SMOKING', 'SHORTNESS OF BREATH'])\n",
"df_new"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "BaAs_R6zd7xT",
"outputId": "47f1258d-c133-47cb-8fae-da698a290dae"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" YELLOW_FINGERS ANXIETY PEER_PRESSURE CHRONIC DISEASE FATIGUE \\\n",
"0 1 1 0 0 1 \n",
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"2 0 0 1 0 1 \n",
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" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
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" <tr>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>279</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>280</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>281</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>282</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
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" <tr>\n",
" <th>283</th>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
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"</table>\n",
"<p>276 rows × 12 columns</p>\n",
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" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
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" docLink.innerHTML = docLinkHtml;\n",
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" }\n",
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"<div id=\"df-49fa1b26-c24d-4daa-8dda-a2aeda9404dd\">\n",
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" .colab-df-quickchart {\n",
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" border: none;\n",
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" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
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" }\n",
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" border-right-color: var(--fill-color);\n",
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"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
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" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
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" const charts = await google.colab.kernel.invokeFunction(\n",
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df_new",
"summary": "{\n \"name\": \"df_new\",\n \"rows\": 276,\n \"fields\": [\n {\n \"column\": \"YELLOW_FINGERS\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ANXIETY\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PEER_PRESSURE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"CHRONIC DISEASE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"FATIGUE \",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ALLERGY \",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"WHEEZING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ALCOHOL CONSUMING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"COUGHING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SWALLOWING DIFFICULTY\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"CHEST PAIN\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"LUNG_CANCER\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 29
}
]
},
{
"cell_type": "markdown",
"source": [
"**CORRELATION**"
],
"metadata": {
"id": "4GjBr9hPeCtL"
}
},
{
"cell_type": "code",
"source": [
"#Finding Correlation\n",
"cn=df_new.corr()\n",
"cn\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 426
},
"id": "XFavNSXWeFVx",
"outputId": "f8c1d694-d8cf-4c66-d4fe-3f1c5c2cfe62"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" YELLOW_FINGERS ANXIETY PEER_PRESSURE \\\n",
"YELLOW_FINGERS 1.000000 0.558344 0.313067 \n",
"ANXIETY 0.558344 1.000000 0.210278 \n",
"PEER_PRESSURE 0.313067 0.210278 1.000000 \n",
"CHRONIC DISEASE 0.015316 -0.006938 0.042893 \n",
"FATIGUE -0.099644 -0.181474 0.094661 \n",
"ALLERGY -0.147130 -0.159451 -0.066887 \n",
"WHEEZING -0.058756 -0.174009 -0.037769 \n",
"ALCOHOL CONSUMING -0.273643 -0.152228 -0.132603 \n",
"COUGHING 0.020803 -0.218843 -0.068224 \n",
"SWALLOWING DIFFICULTY 0.333349 0.478820 0.327764 \n",
"CHEST PAIN -0.099169 -0.123182 -0.074655 \n",
"LUNG_CANCER 0.189192 0.144322 0.195086 \n",
"\n",
" CHRONIC DISEASE FATIGUE ALLERGY WHEEZING \\\n",
"YELLOW_FINGERS 0.015316 -0.099644 -0.147130 -0.058756 \n",
"ANXIETY -0.006938 -0.181474 -0.159451 -0.174009 \n",
"PEER_PRESSURE 0.042893 0.094661 -0.066887 -0.037769 \n",
"CHRONIC DISEASE 1.000000 -0.099411 0.134309 -0.040546 \n",
"FATIGUE -0.099411 1.000000 -0.001841 0.152151 \n",
"ALLERGY 0.134309 -0.001841 1.000000 0.166517 \n",
"WHEEZING -0.040546 0.152151 0.166517 1.000000 \n",
"ALCOHOL CONSUMING 0.010144 -0.181573 0.378125 0.261061 \n",
"COUGHING -0.160813 0.148538 0.206367 0.353657 \n",
"SWALLOWING DIFFICULTY 0.068263 -0.115727 -0.037581 0.108304 \n",
"CHEST PAIN -0.048895 0.013757 0.245440 0.142846 \n",
"LUNG_CANCER 0.143692 0.160078 0.333552 0.249054 \n",
"\n",
" ALCOHOL CONSUMING COUGHING SWALLOWING DIFFICULTY \\\n",
"YELLOW_FINGERS -0.273643 0.020803 0.333349 \n",
"ANXIETY -0.152228 -0.218843 0.478820 \n",
"PEER_PRESSURE -0.132603 -0.068224 0.327764 \n",
"CHRONIC DISEASE 0.010144 -0.160813 0.068263 \n",
"FATIGUE -0.181573 0.148538 -0.115727 \n",
"ALLERGY 0.378125 0.206367 -0.037581 \n",
"WHEEZING 0.261061 0.353657 0.108304 \n",
"ALCOHOL CONSUMING 1.000000 0.198023 -0.000635 \n",
"COUGHING 0.198023 1.000000 -0.136885 \n",
"SWALLOWING DIFFICULTY -0.000635 -0.136885 1.000000 \n",
"CHEST PAIN 0.310767 0.077988 0.102674 \n",
"LUNG_CANCER 0.294422 0.253027 0.268940 \n",
"\n",
" CHEST PAIN LUNG_CANCER \n",
"YELLOW_FINGERS -0.099169 0.189192 \n",
"ANXIETY -0.123182 0.144322 \n",
"PEER_PRESSURE -0.074655 0.195086 \n",
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"type": "dataframe",
"variable_name": "cn",
"summary": "{\n \"name\": \"cn\",\n \"rows\": 12,\n \"fields\": [\n {\n \"column\": \"YELLOW_FINGERS\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.35946589942210144,\n \"min\": -0.2736434085826755,\n \"max\": 1.0,\n \"num_unique_values\": 12,\n \"samples\": [\n -0.09916946287962446,\n 0.3333486404628587,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ANXIETY\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.38415057613591813,\n \"min\": -0.21884297760854748,\n \"max\": 1.0,\n \"num_unique_values\": 12,\n \"samples\": [\n -0.12318220152098101,\n 0.47882019458453057,\n 0.5583444650857541\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PEER_PRESSURE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3105924126810151,\n \"min\": -0.13260305400147793,\n \"max\": 1.0,\n \"num_unique_values\": 12,\n \"samples\": [\n -0.07465535224630711,\n 0.3277642703979553,\n 0.3130673471106882\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"CHRONIC DISEASE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.30031300697271335,\n \"min\": -0.16081334372902287,\n \"max\": 1.0,\n \"num_unique_values\": 12,\n \"samples\": [\n -0.048894891169872355,\n 0.06826266941167272,\n 0.015315556545621249\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"FATIGUE \",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.31811387476671066,\n \"min\": -0.18157283780590083,\n \"max\": 1.0,\n \"num_unique_values\": 12,\n \"samples\": [\n 0.013757129080677472,\n -0.11572680085185086,\n -0.09964378670642009\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ALLERGY \",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.31695977100754086,\n \"min\": -0.15945090309028645,\n \"max\": 1.0,\n \"num_unique_values\": 12,\n \"samples\": [\n 0.24544047640085628,\n -0.03758136914593552,\n -0.14713018902222144\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"WHEEZING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.30145821346162405,\n \"min\": -0.17400877581702118,\n \"max\": 1.0,\n \"num_unique_values\": 12,\n \"samples\": [\n 0.14284614485467398,\n 0.10830411445854016,\n -0.058756016725957906\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ALCOHOL CONSUMING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.348778279664811,\n \"min\": -0.2736434085826755,\n \"max\": 1.0,\n \"num_unique_values\": 12,\n \"samples\": [\n 0.31076668250896533,\n -0.0006347388592016574,\n -0.2736434085826755\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"COUGHING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3251850307187637,\n \"min\": -0.21884297760854748,\n \"max\": 1.0,\n \"num_unique_values\": 12,\n \"samples\": [\n 0.07798763585679196,\n -0.1368853784746863,\n 0.02080309627479439\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SWALLOWING DIFFICULTY\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3164731787979486,\n \"min\": -0.1368853784746863,\n \"max\": 1.0,\n \"num_unique_values\": 12,\n \"samples\": [\n 0.10267388007306033,\n 1.0,\n 0.3333486404628587\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"CHEST PAIN\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3033109108506233,\n \"min\": -0.12318220152098101,\n \"max\": 1.0,\n \"num_unique_values\": 12,\n \"samples\": [\n 1.0,\n 0.10267388007306033,\n -0.09916946287962446\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"LUNG_CANCER\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2329638319616281,\n \"min\": 0.14369194056052245,\n \"max\": 1.0,\n \"num_unique_values\": 12,\n \"samples\": [\n 0.19485572223181838,\n 0.26894009396050644,\n 0.1891920088200089\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"source": [
"#Correlation\n",
"cmap=sns.diverging_palette(260,-10,s=50, l=75, n=6,\n",
"as_cmap=True)\n",
"plt.subplots(figsize=(12,12))\n",
"sns.heatmap(cn,cmap=cmap,annot=True, square=True)\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "2wG5j8j2eZme",
"outputId": "89ba1d9a-eec3-4eef-ce1e-765a2f50be45"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x1200 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"kot = cn[cn>=.40]\n",
"plt.figure(figsize=(12,8))\n",
"sns.heatmap(kot, cmap=\"Blues\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 862
},
"id": "RDGlEp_segiB",
"outputId": "8d0a3f0d-7b9a-4c17-d955-ab5359687aa7"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Axes: >"
]
},
"metadata": {},
"execution_count": 32
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x800 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# **Feature Engineering**"
],
"metadata": {
"id": "6DhXjHXIe6cz"
}
},
{
"cell_type": "markdown",
"source": [
"# Feature Engineering is the process of creating new features using existing features.\n",
"\n",
"\n",
"\n",
"The correlation matrix shows that ANXIETY and YELLOW_FINGERS are correlated more than 50%. So, lets create a new feature combining them."
],
"metadata": {
"id": "xNRs678IfAOF"
}
},
{
"cell_type": "code",
"source": [
"df_new['ANXYELFIN']=df_new['ANXIETY']*df_new['YELLOW_FINGERS']\n",
"df_new"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "CSB7QbD9e80x",
"outputId": "230c9b48-82e1-4708-d4a8-2ebf77f3fa90"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" YELLOW_FINGERS ANXIETY PEER_PRESSURE CHRONIC DISEASE FATIGUE \\\n",
"0 1 1 0 0 1 \n",
"1 0 0 0 1 1 \n",
"2 0 0 1 0 1 \n",
"3 1 1 0 0 0 \n",
"4 1 0 0 0 0 \n",
".. ... ... ... ... ... \n",
"279 1 1 1 0 0 \n",
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"type": "dataframe",
"variable_name": "df_new",
"summary": "{\n \"name\": \"df_new\",\n \"rows\": 276,\n \"fields\": [\n {\n \"column\": \"YELLOW_FINGERS\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ANXIETY\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PEER_PRESSURE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"CHRONIC DISEASE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"FATIGUE \",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ALLERGY \",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"WHEEZING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ALCOHOL CONSUMING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"COUGHING\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SWALLOWING DIFFICULTY\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"CHEST PAIN\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"LUNG_CANCER\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ANXYELFIN\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 33
}
]
},
{
"cell_type": "code",
"source": [
"#Splitting independent and dependent variables\n",
"X = df_new.drop('LUNG_CANCER', axis = 1)\n",
"y = df_new['LUNG_CANCER']"
],
"metadata": {
"id": "P0wp0BCifI93"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# **Target Distribution Imbalance Handling**"
],
"metadata": {
"id": "EKtfYfvrfTgt"
}
},
{
"cell_type": "code",
"source": [
"from imblearn.over_sampling import ADASYN\n",
"adasyn = ADASYN(random_state=42)\n",
"X, y = adasyn.fit_resample(X, y)"
],
"metadata": {
"id": "FvgimCi2fVw8"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"len(X)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d9cfuwd2fa5Z",
"outputId": "4622ab4b-1a2d-479a-fda0-24a1b2ed0eaa"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"477"
]
},
"metadata": {},
"execution_count": 37
}
]
},
{
"cell_type": "markdown",
"source": [
"# **Logistic Regression**"
],
"metadata": {
"id": "S3cPFgtyfjD5"
}
},
{
"cell_type": "code",
"source": [
"#Splitting data for training and testing\n",
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test= train_test_split(X, y, test_size= 0.25, random_state=0)"
],
"metadata": {
"id": "Ox1yJTX9flpk"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#Fitting training data to the model\n",
"from sklearn.linear_model import LogisticRegression\n",
"lr_model=LogisticRegression(random_state=0)\n",
"lr_model.fit(X_train, y_train)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
},
"id": "xHygEApDfn6h",
"outputId": "80721f4f-b941-438a-cb8a-f12cd2040aa0"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LogisticRegression(random_state=0)"
],
"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>LogisticRegression(random_state=0)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=0)</pre></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 49
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"import time\n",
"\n",
"# Create an instance of the LogisticRegression model\n",
"lr_model = LogisticRegression(random_state=0)\n",
"\n",
"# Start the timer\n",
"start_time = time.time()\n",
"\n",
"# Fit the model to your training data\n",
"lr_model.fit(X_train, y_train)\n",
"\n",
"# End the timer\n",
"end_time = time.time()\n",
"\n",
"# Calculate the training time\n",
"training_time = end_time - start_time\n",
"\n",
"# Print the training time\n",
"print(\"Training time:\", training_time, \"seconds\")\n"
],
"metadata": {
"id": "Yxdy2DGrzQzT",
"outputId": "5468159a-bcaf-470b-ca5a-e3ad3135b888",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training time: 0.02142047882080078 seconds\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#Predicting result using testing data\n",
"y_lr_pred= lr_model.predict(X_test)\n",
"y_lr_pred"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uKEUHf51fwwm",
"outputId": "3088698b-f216-402d-ba3b-7bab72289066"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
" 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
" 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
" 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n",
" 1, 0, 0, 0, 0, 1, 0, 1, 1, 0])"
]
},
"metadata": {},
"execution_count": 39
}
]
},
{
"cell_type": "code",
"source": [
"#Model accuracy\n",
"from sklearn.metrics import classification_report, accuracy_score, f1_score\n",
"lr_cr=classification_report(y_test, y_lr_pred)\n",
"print(lr_cr)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "m7NPY0cAf0Jq",
"outputId": "1167a78f-1df0-4ff4-d1d4-f9906bcf16a6"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.96 1.00 0.98 64\n",
" 1 1.00 0.95 0.97 56\n",
"\n",
" accuracy 0.97 120\n",
" macro avg 0.98 0.97 0.97 120\n",
"weighted avg 0.98 0.97 0.97 120\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**This model is almost 97% accurate.**"
],
"metadata": {
"id": "lYym9IRwf3pr"
}
},
{
"cell_type": "markdown",
"source": [
"# **Decision Tree**"
],
"metadata": {
"id": "-5HeVmEtesKu"
}
},
{
"cell_type": "code",
"source": [
"#Fitting training data to the model\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"dt_model= DecisionTreeClassifier(criterion='entropy', random_state=0)\n",
"dt_model.fit(X_train, y_train)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
},
"id": "RQ-PmT2xeu5L",
"outputId": "3e846984-5064-4eb5-b453-e9199f341014"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DecisionTreeClassifier(criterion='entropy', random_state=0)"
],
"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>DecisionTreeClassifier(criterion='entropy', random_state=0)</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\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion='entropy', random_state=0)</pre></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 41
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.metrics import classification_report\n",
"import time\n",
"\n",
"# Create an instance of the DecisionTreeClassifier\n",
"dt_classifier = DecisionTreeClassifier()\n",
"\n",
"# Start the timer\n",
"start_time = time.time()\n",
"\n",
"# Fit the model to your training data\n",
"dt_classifier.fit(X_train, y_train)\n",
"\n",
"# End the timer\n",
"end_time = time.time()\n",
"\n",
"# Calculate the training time\n",
"training_time = end_time - start_time\n",
"\n",
"# Print the training time\n",
"print(\"Training time:\", training_time, \"seconds\")\n"
],
"metadata": {
"id": "MSoMImavyz8x",
"outputId": "c14dcaa9-1697-4cfa-d9f7-cb94fbfab0b9",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training time: 0.04231142997741699 seconds\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#Predicting result using testing data\n",
"y_dt_pred= dt_model.predict(X_test)\n",
"y_dt_pred"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OSbU74N4f987",
"outputId": "b4f60dc1-cccf-4176-bf0a-10b81a37d9d3"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
" 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
" 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
" 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
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" 1, 0, 0, 0, 0, 1, 0, 1, 1, 0])"
]
},
"metadata": {},
"execution_count": 42
}
]
},
{
"cell_type": "code",
"source": [
"#Model accuracy\n",
"dt_cr=classification_report(y_test, y_dt_pred)\n",
"print(dt_cr)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hyT9TFe9gCX3",
"outputId": "4a7d36dd-8eda-4bf3-c419-89c34418e73b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.93 0.97 0.95 64\n",
" 1 0.96 0.91 0.94 56\n",
"\n",
" accuracy 0.94 120\n",
" macro avg 0.94 0.94 0.94 120\n",
"weighted avg 0.94 0.94 0.94 120\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**This model is 94% accurate.**"
],
"metadata": {
"id": "i6DPP32XgIXu"
}
},
{
"cell_type": "markdown",
"source": [
"# **K Nearest Neighbor**"
],
"metadata": {
"id": "UMc1I86nqtRP"
}
},
{
"cell_type": "code",
"source": [
"#Fitting K-NN classifier to the training set\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"knn_model= KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2 )\n",
"knn_model.fit(X_train, y_train)"
],
"metadata": {
"id": "ObLVuNgPqycY",
"outputId": "3d1f5a53-7fe8-475f-887e-543e8593e0d8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
}
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"KNeighborsClassifier()"
],
"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>KNeighborsClassifier()</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 44
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"import time\n",
"\n",
"# Create an instance of the KNeighborsClassifier\n",
"knn_model = KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2)\n",
"\n",
"# Start the timer\n",
"start_time = time.time()\n",
"\n",
"# Fit the model to your training data\n",
"knn_model.fit(X_train, y_train)\n",
"\n",
"# End the timer\n",
"end_time = time.time()\n",
"\n",
"# Calculate the training time\n",
"training_time = end_time - start_time\n",
"\n",
"# Print the training time\n",
"print(\"Training time:\", training_time, \"seconds\")\n"
],
"metadata": {
"id": "gJ_KisYDzyrN",
"outputId": "40b3dbb1-5bd0-4b67-f920-86b6c7b9e993",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training time: 0.012855768203735352 seconds\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#Predicting result using testing data\n",
"y_knn_pred= knn_model.predict(X_test)\n",
"y_knn_pred"
],
"metadata": {
"id": "jUmeoQQvsMSE",
"outputId": "ff17fc6c-6aa5-4004-d058-34d2b0227206",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
" 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
" 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
" 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n",
" 1, 0, 0, 0, 0, 1, 0, 1, 1, 0])"
]
},
"metadata": {},
"execution_count": 45
}
]
},
{
"cell_type": "code",
"source": [
"#Model accuracy\n",
"knn_cr=classification_report(y_test, y_knn_pred)\n",
"print(knn_cr)"
],
"metadata": {
"id": "w1jKcqMRsT5_",
"outputId": "6bdf7f1e-fba6-4d32-b4d9-7b775a099e45",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.93 1.00 0.96 64\n",
" 1 1.00 0.91 0.95 56\n",
"\n",
" accuracy 0.96 120\n",
" macro avg 0.96 0.96 0.96 120\n",
"weighted avg 0.96 0.96 0.96 120\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**This model is 96% accurate.**"
],
"metadata": {
"id": "hP2TeFeOsXnt"
}
},
{
"cell_type": "markdown",
"source": [
"# **Gaussian Naive Bayes**"
],
"metadata": {
"id": "sC6WzdRDsc-g"
}
},
{
"cell_type": "code",
"source": [
"#Fitting Gaussian Naive Bayes classifier to the training set\n",
"from sklearn.naive_bayes import GaussianNB\n",
"gnb_model = GaussianNB()\n",
"gnb_model.fit(X_train, y_train)"
],
"metadata": {
"id": "vxydCz8lsh3h",
"outputId": "175e2e54-f39c-4d38-cb74-65decc6fdc93",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 74
}
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"GaussianNB()"
],
"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>GaussianNB()</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 49
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.naive_bayes import GaussianNB\n",
"import time\n",
"\n",
"# Create an instance of the Gaussian Naive Bayes classifier\n",
"gnb_model = GaussianNB()\n",
"\n",
"# Start the timer\n",
"start_time = time.time()\n",
"\n",
"# Fit the model to your training data\n",
"gnb_model.fit(X_train, y_train)\n",
"\n",
"# End the timer\n",
"end_time = time.time()\n",
"\n",
"# Calculate the training time\n",
"training_time = end_time - start_time\n",
"\n",
"# Print the training time\n",
"print(\"Training time:\", training_time, \"seconds\")\n"
],
"metadata": {
"id": "s2AdyOwH0But",
"outputId": "56c30c7a-9699-4a1e-fac3-a595a7b8a311",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training time: 0.004689693450927734 seconds\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#Predicting result using testing data\n",
"y_gnb_pred= gnb_model.predict(X_test)\n",
"y_gnb_pred"
],
"metadata": {
"id": "ylTHjsSmslCg",
"outputId": "5ab8f828-1b08-4392-bba8-8e78f5454616",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
" 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
" 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1,\n",
" 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n",
" 1, 1, 1, 0, 0, 1, 0, 1, 1, 0])"
]
},
"metadata": {},
"execution_count": 50
}
]
},
{
"cell_type": "code",
"source": [
"#Model accuracy\n",
"gnb_cr=classification_report(y_test, y_gnb_pred)\n",
"print(gnb_cr)"
],
"metadata": {
"id": "1z1QnuO1sn-o",
"outputId": "218a3734-5ce3-4c1e-e435-6302abf7955d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.95 0.89 0.92 64\n",
" 1 0.88 0.95 0.91 56\n",
"\n",
" accuracy 0.92 120\n",
" macro avg 0.92 0.92 0.92 120\n",
"weighted avg 0.92 0.92 0.92 120\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**This model is 92% accurate.**"
],
"metadata": {
"id": "WYXWa_qNssTe"
}
},
{
"cell_type": "markdown",
"source": [
"# **Multinomial Naive Bayes**"
],
"metadata": {
"id": "vVbbV6ehswt0"
}
},
{
"cell_type": "code",
"source": [
"#Fitting Multinomial Naive Bayes classifier to the training set\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"mnb_model = MultinomialNB()\n",
"mnb_model.fit(X_train, y_train)"
],
"metadata": {
"id": "lnTvXP6As1_n",
"outputId": "5ba5a50f-116b-4cd2-fb90-9988f2927bb9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 74
}
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"MultinomialNB()"
],
"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>MultinomialNB()</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.naive_bayes import MultinomialNB\n",
"import time\n",
"\n",
"# Create an instance of the MultinomialNB classifier\n",
"mnb_model = MultinomialNB()\n",
"\n",
"# Start the timer\n",
"start_time = time.time()\n",
"\n",
"# Fit the model to your training data\n",
"mnb_model.fit(X_train, y_train)\n",
"\n",
"# End the timer\n",
"end_time = time.time()\n",
"\n",
"# Calculate the training time\n",
"training_time = end_time - start_time\n",
"\n",
"# Print the training time\n",
"print(\"Training time:\", training_time, \"seconds\")\n"
],
"metadata": {
"id": "tJAiCxnQ0WPq",
"outputId": "2baa934b-8466-4fcb-8d63-c4ac60a219a3",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training time: 0.01954054832458496 seconds\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#Predicting result using testing data\n",
"y_mnb_pred= mnb_model.predict(X_test)\n",
"y_mnb_pred"
],
"metadata": {
"id": "3xmiNB7TtAeb",
"outputId": "0c7261e3-b428-4cd5-f4e0-ac273047e7a9",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
" 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0,\n",
" 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1,\n",
" 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n",
" 1, 1, 1, 1, 1, 1, 0, 0, 1, 0])"
]
},
"metadata": {},
"execution_count": 53
}
]
},
{
"cell_type": "code",
"source": [
"#Model accuracy\n",
"mnb_cr=classification_report(y_test, y_mnb_pred)\n",
"print(mnb_cr)"
],
"metadata": {
"id": "G72w2J-itEsq",
"outputId": "d99285f5-e1c8-49a6-de54-301f674970f9",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.89 0.73 0.80 64\n",
" 1 0.75 0.89 0.81 56\n",
"\n",
" accuracy 0.81 120\n",
" macro avg 0.82 0.81 0.81 120\n",
"weighted avg 0.82 0.81 0.81 120\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**This model is 81% accurate.**"
],
"metadata": {
"id": "PpJh6arotIsi"
}
},
{
"cell_type": "markdown",
"source": [
"# **Support Vector Classifier**"
],
"metadata": {
"id": "G9vTVqEZtKp6"
}
},
{
"cell_type": "code",
"source": [
"#Fitting SVC to the training set\n",
"from sklearn.svm import SVC\n",
"svc_model = SVC()\n",
"svc_model.fit(X_train, y_train)"
],
"metadata": {
"id": "MCUGsk0LtQm4",
"outputId": "b0db5f38-57a8-4500-d47b-7b0b324f72d2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 74
}
},
"execution_count": null,
"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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" 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": 55
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.svm import SVC\n",
"import time\n",
"\n",
"# Create an instance of the SVC model\n",
"svc_model = SVC()\n",
"\n",
"# Start the timer\n",
"start_time = time.time()\n",
"\n",
"# Fit the model to your training data\n",
"svc_model.fit(X_train, y_train)\n",
"\n",
"# End the timer\n",
"end_time = time.time()\n",
"\n",
"# Calculate the training time\n",
"training_time = end_time - start_time\n",
"\n",
"# Print the training time\n",
"print(\"Training time:\", training_time, \"seconds\")\n"
],
"metadata": {
"id": "wwCQiqhZ0mre",
"outputId": "8ae1a8c8-21f5-4bc0-870f-1ec9063dfc5f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training time: 0.018738985061645508 seconds\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#Predicting result using testing data\n",
"y_svc_pred= svc_model.predict(X_test)\n",
"y_svc_pred"
],
"metadata": {
"id": "qv1VuS1ztZeM",
"outputId": "54c501ff-c562-4718-c291-bce339aca486",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
" 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
" 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
" 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n",
" 1, 0, 0, 0, 0, 1, 0, 1, 1, 0])"
]
},
"metadata": {},
"execution_count": 56
}
]
},
{
"cell_type": "code",
"source": [
"#Model accuracy\n",
"svc_cr=classification_report(y_test, y_svc_pred)\n",
"print(svc_cr)"
],
"metadata": {
"id": "KVE6Kgh_te_C",
"outputId": "7334e7d0-4344-459d-e713-04e4456bb4aa",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.98 0.98 0.98 64\n",
" 1 0.98 0.98 0.98 56\n",
"\n",
" accuracy 0.98 120\n",
" macro avg 0.98 0.98 0.98 120\n",
"weighted avg 0.98 0.98 0.98 120\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**This model is 98% accurate.**"
],
"metadata": {
"id": "fNX2rEr4tiwF"
}
},
{
"cell_type": "markdown",
"source": [
"# **Random Forest**"
],
"metadata": {
"id": "J1-eTc9Ctk17"
}
},
{
"cell_type": "code",
"source": [
"#Training\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"rf_model = RandomForestClassifier()\n",
"rf_model.fit(X_train, y_train)"
],
"metadata": {
"id": "dCjIDC50tr59",
"outputId": "d88d166f-a785-49e9-da0f-1bbe7eb3b3b4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 74
}
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RandomForestClassifier()"
],
"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>RandomForestClassifier()</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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 58
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"import time\n",
"\n",
"# Create an instance of the RandomForestClassifier\n",
"rf_model = RandomForestClassifier()\n",
"\n",
"# Start the timer\n",
"start_time = time.time()\n",
"\n",
"# Fit the model to your training data\n",
"rf_model.fit(X_train, y_train)\n",
"\n",
"# End the timer\n",
"end_time = time.time()\n",
"\n",
"# Calculate the training time\n",
"training_time = end_time - start_time\n",
"\n",
"# Print the training time\n",
"print(\"Training time:\", training_time, \"seconds\")\n"
],
"metadata": {
"id": "csG1ZdYr0ziV",
"outputId": "0c3ac392-268a-4ce3-ea07-beda745bf58b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training time: 0.5766491889953613 seconds\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#Predicting result using testing data\n",
"y_rf_pred= rf_model.predict(X_test)\n",
"y_rf_pred"
],
"metadata": {
"id": "UL28OMK0tvNz",
"outputId": "2b3162af-076a-40e4-94f5-0c4326dddcc7",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
" 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
" 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
" 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n",
" 1, 0, 0, 0, 0, 1, 0, 1, 1, 0])"
]
},
"metadata": {},
"execution_count": 59
}
]
},
{
"cell_type": "code",
"source": [
"#Model accuracy\n",
"rf_cr=classification_report(y_test, y_rf_pred)\n",
"print(rf_cr)"
],
"metadata": {
"id": "3zgaW278tzVr",
"outputId": "dbbcde0d-297e-4938-fa7e-14fcda10929f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.98 0.97 0.98 64\n",
" 1 0.96 0.98 0.97 56\n",
"\n",
" accuracy 0.97 120\n",
" macro avg 0.97 0.98 0.97 120\n",
"weighted avg 0.98 0.97 0.98 120\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**This model is also 98% accurate.**"
],
"metadata": {
"id": "q8y8fFzAt3rf"
}
}
]
}