{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Task to predict whether person has ckd or notckd??\n",
    "\n",
    "## ckd-chronic kidney disease\n",
    "## notckd-->> not crornic kidney disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
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       "\n",
       "   ca  thal  target  \n",
       "0   2     3       0  \n",
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       "3   1     3       0  \n",
       "4   3     2       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'C:/Users/ganes/projects/chronicproj/djangoMLDeployment/src/heart.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1025, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1. Features:\n",
    "    age - age\n",
    "    bp - blood pressure\n",
    "    sg - specific gravity\n",
    "    al - albumin\n",
    "    su - sugar\n",
    "    rbc - red blood cells\n",
    "    pc - pus cell\n",
    "    pcc - pus cell clumps\n",
    "    ba - bacteria\n",
    "    bgr - blood glucose random\n",
    "    bu - blood urea\n",
    "    sc - serum creatinine\n",
    "    sod - sodium\n",
    "    pot - potassium\n",
    "    hemo - haemoglobin\n",
    "    pcv - packed cell volume\n",
    "    wc - white blood cell count\n",
    "    rc - red blood cell count\n",
    "    htn - hypertension\n",
    "    dm - diabetes mellitus\n",
    "    cad - coronary artery disease\n",
    "    appet - appetite\n",
    "    pe - pedal edema\n",
    "    ane - anemia\n",
    "    classification - class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### rename column names to make it more user-friendly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"columns=pd.read_csv('C:/Users/ganes/projects/chronic/drive-download-20230312T132507Z-001/data_description.txt',sep='-')\\ncolumns=columns.reset_index()\\ncolumns.columns=['cols','abb_col_names']\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''columns=pd.read_csv('C:/Users/ganes/projects/chronic/drive-download-20230312T132507Z-001/data_description.txt',sep='-')\n",
    "columns=columns.reset_index()\n",
    "columns.columns=['cols','abb_col_names']'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
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       "\n",
       "   ca  thal  target  \n",
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.columns=columns['abb_col_names'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1025 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0      52    1   0       125   212    0        1      168      0      1.0   \n",
       "1      53    1   0       140   203    1        0      155      1      3.1   \n",
       "2      70    1   0       145   174    0        1      125      1      2.6   \n",
       "3      61    1   0       148   203    0        1      161      0      0.0   \n",
       "4      62    0   0       138   294    1        1      106      0      1.9   \n",
       "...   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "1020   59    1   1       140   221    0        1      164      1      0.0   \n",
       "1021   60    1   0       125   258    0        0      141      1      2.8   \n",
       "1022   47    1   0       110   275    0        0      118      1      1.0   \n",
       "1023   50    0   0       110   254    0        0      159      0      0.0   \n",
       "1024   54    1   0       120   188    0        1      113      0      1.4   \n",
       "\n",
       "      slope  ca  thal  target  \n",
       "0         2   2     3       0  \n",
       "1         0   0     3       0  \n",
       "2         0   0     3       0  \n",
       "3         2   1     3       0  \n",
       "4         1   3     2       0  \n",
       "...     ...  ..   ...     ...  \n",
       "1020      2   0     2       1  \n",
       "1021      1   1     3       0  \n",
       "1022      1   1     2       0  \n",
       "1023      2   0     2       1  \n",
       "1024      1   1     3       0  \n",
       "\n",
       "[1025 rows x 14 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age           int64\n",
       "sex           int64\n",
       "cp            int64\n",
       "trestbps      int64\n",
       "chol          int64\n",
       "fbs           int64\n",
       "restecg       int64\n",
       "thalach       int64\n",
       "exang         int64\n",
       "oldpeak     float64\n",
       "slope         int64\n",
       "ca            int64\n",
       "thal          int64\n",
       "target        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    As it can be seen, red_blood_cell_count, packed_cell_volume and white_blood_cell_count are object type. We need to \n",
    "    change to numerical dtype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#features=['red blood cell count','packed cell volume','white blood cell count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"def convert_dtype(df,feature):\\n    df[feature] = pd.to_numeric(df[feature], errors='coerce')\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''def convert_dtype(df,feature):\n",
    "    df[feature] = pd.to_numeric(df[feature], errors='coerce')'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'for feature in features:\\n    convert_dtype(df,feature)'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''for feature in features:\n",
    "    convert_dtype(df,feature)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age           int64\n",
       "sex           int64\n",
       "cp            int64\n",
       "trestbps      int64\n",
       "chol          int64\n",
       "fbs           int64\n",
       "restecg       int64\n",
       "thalach       int64\n",
       "exang         int64\n",
       "oldpeak     float64\n",
       "slope         int64\n",
       "ca            int64\n",
       "thal          int64\n",
       "target        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Let's drop the id column. id column is seems to be an unique identifier for each row so we are dropping that it won't \n",
    "    help us to find any insights from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.drop([\"id\"],axis=1,inplace=True) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract Numerical & Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_cat_num(df):\n",
    "    cat_col=[col for col in df.columns if df[col].dtype=='object']\n",
    "    num_col=[col for col in df.columns if df[col].dtype!='object']\n",
    "    return cat_col,num_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_col,num_col=extract_cat_num(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'sex',\n",
       " 'cp',\n",
       " 'trestbps',\n",
       " 'chol',\n",
       " 'fbs',\n",
       " 'restecg',\n",
       " 'thalach',\n",
       " 'exang',\n",
       " 'oldpeak',\n",
       " 'slope',\n",
       " 'ca',\n",
       " 'thal',\n",
       " 'target']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "### total unique categories in our categorical features to check if any dirtiness in data or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cat_col:\n",
    "    print('{} has {} values '.format(col,df[col].unique()))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "## ckd-chronic kidney disease\n",
    "## notckd-->> not crornic kidney disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    So we need to correct 2 features and the target variable which contain certain discrepancy in some values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ndf['diabetes mellitus'].replace(to_replace = {'\\tno':'no','\\tyes':'yes',' yes':'yes'},inplace=True)\\n\\ndf['coronary artery disease'] = df['coronary artery disease'].replace(to_replace = '\\tno', value='no')\\n\\ndf['class'] = df['class'].replace(to_replace = 'ckd\\t', value = 'ckd')\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace incorrect values\n",
    "'''\n",
    "df['diabetes mellitus'].replace(to_replace = {'\\tno':'no','\\tyes':'yes',' yes':'yes'},inplace=True)\n",
    "\n",
    "df['coronary artery disease'] = df['coronary artery disease'].replace(to_replace = '\\tno', value='no')\n",
    "\n",
    "df['class'] = df['class'].replace(to_replace = 'ckd\\t', value = 'ckd')'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "##df['diabetes mellitus'] = [float(str(i).replace(\",\", \"\")) for i in df['diabetes mellitus']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cat_col:\n",
    "    print('{} has {} values  '.format(col, df[col].unique()))\n",
    "    print('\\n')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Looks good now apart from the NaNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(num_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Checking features distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3000x2000 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,20))\n",
    "for i,feature in enumerate(num_col):\n",
    "    plt.subplot(5,3,i+1)\n",
    "    df[feature].hist()\n",
    "    plt.title(feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Observations:\n",
    "        1.age looks a bit left skewed\n",
    "        2.Blood gluscose random is right skewed\n",
    "        3.Blood Urea is also a bit right skewed\n",
    "        4.Rest of the features are lightly skewed"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, let's check the label distribution for categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cat_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x2000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "for i,feature in enumerate(cat_col):\n",
    "    plt.subplot(4,3,i+1)\n",
    "    df[feature].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    A few features have imbalanced categories. Stratified folds will be necessary while cross validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sns.countplot(x=\\'class\\',data=df)\\nplt.xlabel(\"classification\")\\nplt.ylabel(\"Count\")\\nplt.title(\"target Class\")'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''sns.countplot(x='class',data=df)\n",
    "plt.xlabel(\"classification\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"target Class\")'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "## ckd-chronic kidney disease\n",
    "## notckd-->> not crornic kidney disease"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  7. Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "corr_df = df.corr()\n",
    "sns.heatmap(corr_df,annot=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Positive Correlation:\n",
    "    \n",
    "    Specific gravity -> Red blood cell count, Packed cell volume and Hemoglobin\n",
    "    Sugar -> Blood glucose random\n",
    "    Blood Urea -> Serum creatinine\n",
    "    Hemoglobin -> Red Blood cell count <- packed cell volume\n",
    "    \n",
    "    \n",
    "    Negative Correlation:\n",
    "    Albumin, Blood urea -> Red blood cell count, packed cell volume, Hemoglobin\n",
    "    Serum creatinine -> Sodium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.groupby(['red blood cells','class'])['red blood cell count'].agg(['count','mean','median','min','max'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's check for Positive correlation and its impact on classes¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#px.violin(df,y='red blood cell count',x=\"class\", color=\"class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#px.scatter(df,'haemoglobin','packed cell volume')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'grid=sns.FacetGrid(df, hue=\"class\",aspect=2)\\ngrid.map(sns.kdeplot, \\'red blood cell count\\')\\ngrid.add_legend()'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### analysing distribution of 'red_blood_cell_count' in both Labels \n",
    "\n",
    "'''grid=sns.FacetGrid(df, hue=\"class\",aspect=2)\n",
    "grid.map(sns.kdeplot, 'red blood cell count')\n",
    "grid.add_legend()'''"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Both distributions are quite different, distribution CKD is quite normal and evenly distributed but not CKD distribution is a little bit left-skewed but quite close to a normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'def violin(col):\\n    fig = px.violin(df, y=col, x=\"class\", color=\"class\", box=True)\\n    return fig.show()\\n\\ndef scatters(col1,col2):\\n    fig = px.scatter(df, x=col1, y=col2, color=\"class\")\\n    return fig.show()'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining violin and scatter plot & kde_plot functions\n",
    "'''def violin(col):\n",
    "    fig = px.violin(df, y=col, x=\"class\", color=\"class\", box=True)\n",
    "    return fig.show()\n",
    "\n",
    "def scatters(col1,col2):\n",
    "    fig = px.scatter(df, x=col1, y=col2, color=\"class\")\n",
    "    return fig.show()'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'def kde_plot(feature):\\n    grid = sns.FacetGrid(df, hue=\"class\",aspect=2)\\n    grid.map(sns.kdeplot, feature)\\n    grid.add_legend()'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''def kde_plot(feature):\n",
    "    grid = sns.FacetGrid(df, hue=\"class\",aspect=2)\n",
    "    grid.map(sns.kdeplot, feature)\n",
    "    grid.add_legend()'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"kde_plot('red blood cell count')\""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''kde_plot('red blood cell count')'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#kde_plot('haemoglobin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scatters('red blood cell count', 'packed cell volume')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scatters('red blood cell count', 'haemoglobin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scatters('haemoglobin','packed cell volume')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1.RBC count range ~2 to <4.5 and Hemoglobin between 3 to <13 are mostly classified as positive for chronic kidney  \n",
    "    disease(i.e ckd).\n",
    "    2.RBC count range >4.5 to ~6.1 and Hemoglobin between >13 to 17.8 are classified as negative for chronic kidney \n",
    "    disease(i.e nockd)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#violin('red blood cell count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#violin('packed cell volume')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Now let's check for negative correlation and its impact on classes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Albumin, Blood urea -> Red blood cell count, packed cell volume, Haemoglobin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scatters('red blood cell count','albumin')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Clearly, albumin levels of above 0 affect ckd largely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scatters('packed cell volume','blood urea')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Packed cell volume >= 40 largely affects to be non ckd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fig = px.bar(df, x=\"specific gravity\", y=\"packed cell volume\",\\n             color=\\'class\\', barmode=\\'group\\',\\n             height=400)\\nfig.show()'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''fig = px.bar(df, x=\"specific gravity\", y=\"packed cell volume\",\n",
    "             color='class', barmode='group',\n",
    "             height=400)\n",
    "fig.show()'''"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Clearly, specific gravity >=1.02 affects non ckd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
       "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   2     3       0  \n",
       "1   0     3       0  \n",
       "2   0     3       0  \n",
       "3   1     3       0  \n",
       "4   3     2       0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['red blood cells'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "### filling missing with Random value"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Value Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['red blood cells'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['red blood cells'].dropna().sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_sample=data['red blood cells'].dropna().sample(data['red blood cells'].isnull().sum())\n",
    "#random_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_sample.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data[data['red blood cells'].isnull()].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_sample.index=data[data['red blood cells'].isnull()].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_sample.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.loc[data['red blood cells'].isnull(),'red blood cells']=random_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
       "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   2     3       0  \n",
       "1   0     3       0  \n",
       "2   0     3       0  \n",
       "3   1     3       0  \n",
       "4   3     2       0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['red blood cells'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "### lets create a function so that I can easily do it for all features\n",
    "def Random_value_imputation(feature):\n",
    "    random_sample=data[feature].dropna().sample(data[feature].isnull().sum())               \n",
    "    random_sample.index=data[data[feature].isnull()].index\n",
    "    data.loc[data[feature].isnull(),feature]=random_sample\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cat_col].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "### as rest of the features has less missing values,so I can fill it using mode concept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_mode(feature):\n",
    "    mode=data[feature].mode()[0]\n",
    "    data[feature]=data[feature].fillna(mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cat_col:\n",
    "    impute_mode(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cat_col].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[num_col].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "### lets fill missing values in Numerical features using Random value Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in num_col:\n",
    "    Random_value_imputation(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[num_col].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### feature Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cat_col:\n",
    "    print('{} has {} categories'.format(col, data[col].nunique()))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### as we have just 2 categories in each feature then we can consider Label Encoder as it will not cause Curse of Dimensionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cat_col:\n",
    "    data[col]=le.fit_transform(data[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
       "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   2     3       0  \n",
       "1   0     3       0  \n",
       "2   0     3       0  \n",
       "3   1     3       0  \n",
       "4   3     2       0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#SelectKBest-to select k best features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#chi2-Internally this class is going to check that whether p-value is less than 0.05 or not\n",
    "#based on that,it will actually order all the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest#Also known as Information Gain\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_col=[col for col in data.columns if col!='target']\n",
    "dep_col='target'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data[ind_col]\n",
    "y=data[dep_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ordered_rank_features=SelectKBest(score_func=chi2,k=13)\n",
    "ordered_feature=ordered_rank_features.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SelectKBest(k=13, score_func=&lt;function chi2 at 0x000001FAAA44B250&gt;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=13, score_func=&lt;function chi2 at 0x000001FAAA44B250&gt;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SelectKBest(k=13, score_func=<function chi2 at 0x000001FAAA44B250>)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 81.42536762,  24.37365008, 217.82392171,  45.97406854,\n",
       "       110.72336371,   1.47754962,   9.73934262, 650.00849349,\n",
       "       130.47092725, 253.65346109,  33.67394807, 210.62591949,\n",
       "        19.37346461])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To get scores(rank) of feature,what we can do we can use scores function\n",
    "ordered_feature.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81.425368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.373650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>217.823922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.974069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110.723364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.477550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.739343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>650.008493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>130.470927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>253.653461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>33.673948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>210.625919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19.373465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Score\n",
       "0    81.425368\n",
       "1    24.373650\n",
       "2   217.823922\n",
       "3    45.974069\n",
       "4   110.723364\n",
       "5     1.477550\n",
       "6     9.739343\n",
       "7   650.008493\n",
       "8   130.470927\n",
       "9   253.653461\n",
       "10   33.673948\n",
       "11  210.625919\n",
       "12   19.373465"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datascores=pd.DataFrame(ordered_feature.scores_,columns=[\"Score\"])\n",
    "datascores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>trestbps</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fbs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>restecg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>thalach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>exang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>oldpeak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>thal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0\n",
       "0        age\n",
       "1        sex\n",
       "2         cp\n",
       "3   trestbps\n",
       "4       chol\n",
       "5        fbs\n",
       "6    restecg\n",
       "7    thalach\n",
       "8      exang\n",
       "9    oldpeak\n",
       "10     slope\n",
       "11        ca\n",
       "12      thal"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfcolumns=pd.DataFrame(X.columns)\n",
    "dfcolumns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_rank=pd.concat([dfcolumns,datascores],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>81.425368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sex</td>\n",
       "      <td>24.373650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cp</td>\n",
       "      <td>217.823922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>trestbps</td>\n",
       "      <td>45.974069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chol</td>\n",
       "      <td>110.723364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fbs</td>\n",
       "      <td>1.477550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>restecg</td>\n",
       "      <td>9.739343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>thalach</td>\n",
       "      <td>650.008493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>exang</td>\n",
       "      <td>130.470927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>oldpeak</td>\n",
       "      <td>253.653461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>slope</td>\n",
       "      <td>33.673948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ca</td>\n",
       "      <td>210.625919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>thal</td>\n",
       "      <td>19.373465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0       Score\n",
       "0        age   81.425368\n",
       "1        sex   24.373650\n",
       "2         cp  217.823922\n",
       "3   trestbps   45.974069\n",
       "4       chol  110.723364\n",
       "5        fbs    1.477550\n",
       "6    restecg    9.739343\n",
       "7    thalach  650.008493\n",
       "8      exang  130.470927\n",
       "9    oldpeak  253.653461\n",
       "10     slope   33.673948\n",
       "11        ca  210.625919\n",
       "12      thal   19.373465"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Higher the score is,more important feature is "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>81.425368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sex</td>\n",
       "      <td>24.373650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cp</td>\n",
       "      <td>217.823922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>trestbps</td>\n",
       "      <td>45.974069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chol</td>\n",
       "      <td>110.723364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fbs</td>\n",
       "      <td>1.477550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>restecg</td>\n",
       "      <td>9.739343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>thalach</td>\n",
       "      <td>650.008493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>exang</td>\n",
       "      <td>130.470927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>oldpeak</td>\n",
       "      <td>253.653461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>slope</td>\n",
       "      <td>33.673948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ca</td>\n",
       "      <td>210.625919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>thal</td>\n",
       "      <td>19.373465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Features       Score\n",
       "0        age   81.425368\n",
       "1        sex   24.373650\n",
       "2         cp  217.823922\n",
       "3   trestbps   45.974069\n",
       "4       chol  110.723364\n",
       "5        fbs    1.477550\n",
       "6    restecg    9.739343\n",
       "7    thalach  650.008493\n",
       "8      exang  130.470927\n",
       "9    oldpeak  253.653461\n",
       "10     slope   33.673948\n",
       "11        ca  210.625919\n",
       "12      thal   19.373465"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_rank.columns=['Features','Score']\n",
    "features_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>thalach</td>\n",
       "      <td>650.008493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>oldpeak</td>\n",
       "      <td>253.653461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cp</td>\n",
       "      <td>217.823922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ca</td>\n",
       "      <td>210.625919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>exang</td>\n",
       "      <td>130.470927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chol</td>\n",
       "      <td>110.723364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>81.425368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>trestbps</td>\n",
       "      <td>45.974069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>slope</td>\n",
       "      <td>33.673948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sex</td>\n",
       "      <td>24.373650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Features       Score\n",
       "7    thalach  650.008493\n",
       "9    oldpeak  253.653461\n",
       "2         cp  217.823922\n",
       "11        ca  210.625919\n",
       "8      exang  130.470927\n",
       "4       chol  110.723364\n",
       "0        age   81.425368\n",
       "3   trestbps   45.974069\n",
       "10     slope   33.673948\n",
       "1        sex   24.373650"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fetch largest 10 values of Score column\n",
    "features_rank.nlargest(10,'Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_new=features_rank.nlargest(10,'Score')['Features'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc=np.array(x_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      thalach  oldpeak  cp  ca  exang  chol  age  trestbps  slope  sex\n",
      "0         168      1.0   0   2      0   212   52       125      2    1\n",
      "1         155      3.1   0   0      1   203   53       140      0    1\n",
      "2         125      2.6   0   0      1   174   70       145      0    1\n",
      "3         161      0.0   0   1      0   203   61       148      2    1\n",
      "4         106      1.9   0   3      0   294   62       138      1    0\n",
      "...       ...      ...  ..  ..    ...   ...  ...       ...    ...  ...\n",
      "1020      164      0.0   1   0      1   221   59       140      2    1\n",
      "1021      141      2.8   0   1      1   258   60       125      1    1\n",
      "1022      118      1.0   0   1      1   275   47       110      1    1\n",
      "1023      159      0.0   0   0      0   254   50       110      2    0\n",
      "1024      113      1.4   0   1      0   188   54       120      1    1\n",
      "\n",
      "[1025 rows x 10 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "thalach       int64\n",
       "oldpeak     float64\n",
       "cp            int64\n",
       "ca            int64\n",
       "exang         int64\n",
       "chol          int64\n",
       "age           int64\n",
       "trestbps      int64\n",
       "slope         int64\n",
       "sex           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_columns=data[sc]\n",
    "print(selected_columns)\n",
    "selected_columns.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(selected_columns,y,train_size=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 10)\n",
      "(257, 10)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "thalach  oldpeak  cp  ca  exang  chol  age  trestbps  slope  sex\n",
       "202      0.0      1   0   0      204   29   130       2      1      4\n",
       "131      1.8      2   0   1      309   64   125       1      1      4\n",
       "173      0.0      2   4   0      175   38   138       2      1      4\n",
       "118      1.0      0   1   1      275   47   110       1      1      4\n",
       "124      1.0      0   3   0      289   57   165       1      1      4\n",
       "                                                                   ..\n",
       "125      0.0      3   0   0      273   59   160       2      1      1\n",
       "161      0.0      0   1   1      255   52   128       2      1      1\n",
       "178      1.2      3   0   0      298   52   152       1      1      1\n",
       "161      0.5      0   0   0      234   59   135       1      1      1\n",
       "71       1.0      0   0   0      237   67   120       1      1      1\n",
       "Length: 299, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    398\n",
       "0    370\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## check whether dataset is imbalance or not\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Lets find best model using Hyperparameter optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       0\n",
      "1       0\n",
      "2       0\n",
      "3       0\n",
      "4       0\n",
      "       ..\n",
      "1020    1\n",
      "1021    0\n",
      "1022    0\n",
      "1023    1\n",
      "1024    0\n",
      "Name: target, Length: 1025, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nparams={\\n \"learning_rate\"    : [0.05, 0.20, 0.25 ] ,\\n \"max_depth\"        : [ 5, 8, 10, 12],\\n \"min_child_weight\" : [ 1, 3, 5, 7 ],\\n \"gamma\"            : [ 0.0, 0.1, 0.2 , 0.4 ],\\n \"colsample_bytree\" : [ 0.3, 0.4, 0.7 ]\\n    \\n}'"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Hyper Parameter Optimization with respect to XGBoost\n",
    "'''\n",
    "params={\n",
    " \"learning_rate\"    : [0.05, 0.20, 0.25 ] ,\n",
    " \"max_depth\"        : [ 5, 8, 10, 12],\n",
    " \"min_child_weight\" : [ 1, 3, 5, 7 ],\n",
    " \"gamma\"            : [ 0.0, 0.1, 0.2 , 0.4 ],\n",
    " \"colsample_bytree\" : [ 0.3, 0.4, 0.7 ]\n",
    "    \n",
    "}'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'from xgboost import XGBClassifier\\nclassifier=XGBClassifier()'"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''from xgboost import XGBClassifier\n",
    "classifier=XGBClassifier()'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_search=RandomizedSearchCV(classifier,param_distributions=params,n_iter=5,scoring='roc_auc',n_jobs=-1,cv=5,verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"classifier=RandomForestClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\\n              colsample_bynode=1, colsample_bytree=0.4, gamma=0.0, gpu_id=-1,\\n              importance_type='gain', interaction_constraints='',\\n              learning_rate=0.25, max_delta_step=0, max_depth=5,\\n              min_child_weight=1, monotone_constraints='()',\\n              n_estimators=100, n_jobs=2, num_parallel_tree=1,\\n              objective='binary:logistic', random_state=0, reg_alpha=0,\\n              reg_lambda=1, scale_pos_weight=1, subsample=1,\\n              tree_method='exact', use_label_encoder=False,\\n              validate_parameters=1, verbosity=None)\\n\""
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''classifier=RandomForestClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "              colsample_bynode=1, colsample_bytree=0.4, gamma=0.0, gpu_id=-1,\n",
    "              importance_type='gain', interaction_constraints='',\n",
    "              learning_rate=0.25, max_delta_step=0, max_depth=5,\n",
    "              min_child_weight=1, monotone_constraints='()',\n",
    "              n_estimators=100, n_jobs=2, num_parallel_tree=1,\n",
    "              objective='binary:logistic', random_state=0, reg_alpha=0,\n",
    "              reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
    "              tree_method='exact', use_label_encoder=False,\n",
    "              validate_parameters=1, verbosity=None)\n",
    "'''\n",
    "## we have got this model on the basis of cross valudation & hyper-parameter optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[129   0]\n",
      " [  0 128]]\n"
     ]
    }
   ],
   "source": [
    "confusion = confusion_matrix(y_test, y_pred)\n",
    "print('Confusion Matrix:')\n",
    "print(confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1faaa553e20>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### to make confusion matrix user-friendly\n",
    "plt.imshow(confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./../savedModels/model2.joblib']"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(model,'./../savedModels/model2.joblib')"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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