Experiment no.8 - Logistic regression divyani.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4cba8c80",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "792877a5",
   "metadata": {},
   "source": [
    "Exp no.: 8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb7a3230",
   "metadata": {},
   "source": [
    "Aim: Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d4f682e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Name: Divyani Sadafale\n",
    "#Roll no.: 50\n",
    "#Sec:B\n",
    "#Aim:SVM Classifier\n",
    "#Year:3rd Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa282a88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a02bbef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\hp\\\\Downloads'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa3a435c",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('C:\\\\Users\\\\HP\\\\Desktop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a4a9228",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('framingham.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e289bc1",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>currentSmoker</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>195.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>26.97</td>\n",
       "      <td>80.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>28.73</td>\n",
       "      <td>95.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>127.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>25.34</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>28.58</td>\n",
       "      <td>65.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>23.10</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   male  age  education  currentSmoker  cigsPerDay  BPMeds  prevalentStroke  \\\n",
       "0     1   39        4.0              0         0.0     0.0                0   \n",
       "1     0   46        2.0              0         0.0     0.0                0   \n",
       "2     1   48        1.0              1        20.0     0.0                0   \n",
       "3     0   61        3.0              1        30.0     0.0                0   \n",
       "4     0   46        3.0              1        23.0     0.0                0   \n",
       "\n",
       "   prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  \\\n",
       "0             0         0    195.0  106.0   70.0  26.97       80.0     77.0   \n",
       "1             0         0    250.0  121.0   81.0  28.73       95.0     76.0   \n",
       "2             0         0    245.0  127.5   80.0  25.34       75.0     70.0   \n",
       "3             1         0    225.0  150.0   95.0  28.58       65.0    103.0   \n",
       "4             0         0    285.0  130.0   84.0  23.10       85.0     85.0   \n",
       "\n",
       "   TenYearCHD  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           1  \n",
       "4           0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f892ea95",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>currentSmoker</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4233</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>25.97</td>\n",
       "      <td>66.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4234</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>126.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.71</td>\n",
       "      <td>65.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4235</th>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4236</th>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>126.5</td>\n",
       "      <td>87.0</td>\n",
       "      <td>19.16</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4237</th>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>133.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>21.47</td>\n",
       "      <td>80.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      male  age  education  currentSmoker  cigsPerDay  BPMeds  \\\n",
       "4233     1   50        1.0              1         1.0     0.0   \n",
       "4234     1   51        3.0              1        43.0     0.0   \n",
       "4235     0   48        2.0              1        20.0     NaN   \n",
       "4236     0   44        1.0              1        15.0     0.0   \n",
       "4237     0   52        2.0              0         0.0     0.0   \n",
       "\n",
       "      prevalentStroke  prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  \\\n",
       "4233                0             1         0    313.0  179.0   92.0  25.97   \n",
       "4234                0             0         0    207.0  126.5   80.0  19.71   \n",
       "4235                0             0         0    248.0  131.0   72.0  22.00   \n",
       "4236                0             0         0    210.0  126.5   87.0  19.16   \n",
       "4237                0             0         0    269.0  133.5   83.0  21.47   \n",
       "\n",
       "      heartRate  glucose  TenYearCHD  \n",
       "4233       66.0     86.0           1  \n",
       "4234       65.0     68.0           0  \n",
       "4235       84.0     86.0           0  \n",
       "4236       86.0      NaN           0  \n",
       "4237       80.0    107.0           0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e0068f9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of       male  age  education  currentSmoker  cigsPerDay  BPMeds  \\\n",
       "0        1   39        4.0              0         0.0     0.0   \n",
       "1        0   46        2.0              0         0.0     0.0   \n",
       "2        1   48        1.0              1        20.0     0.0   \n",
       "3        0   61        3.0              1        30.0     0.0   \n",
       "4        0   46        3.0              1        23.0     0.0   \n",
       "...    ...  ...        ...            ...         ...     ...   \n",
       "4233     1   50        1.0              1         1.0     0.0   \n",
       "4234     1   51        3.0              1        43.0     0.0   \n",
       "4235     0   48        2.0              1        20.0     NaN   \n",
       "4236     0   44        1.0              1        15.0     0.0   \n",
       "4237     0   52        2.0              0         0.0     0.0   \n",
       "\n",
       "      prevalentStroke  prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  \\\n",
       "0                   0             0         0    195.0  106.0   70.0  26.97   \n",
       "1                   0             0         0    250.0  121.0   81.0  28.73   \n",
       "2                   0             0         0    245.0  127.5   80.0  25.34   \n",
       "3                   0             1         0    225.0  150.0   95.0  28.58   \n",
       "4                   0             0         0    285.0  130.0   84.0  23.10   \n",
       "...               ...           ...       ...      ...    ...    ...    ...   \n",
       "4233                0             1         0    313.0  179.0   92.0  25.97   \n",
       "4234                0             0         0    207.0  126.5   80.0  19.71   \n",
       "4235                0             0         0    248.0  131.0   72.0  22.00   \n",
       "4236                0             0         0    210.0  126.5   87.0  19.16   \n",
       "4237                0             0         0    269.0  133.5   83.0  21.47   \n",
       "\n",
       "      heartRate  glucose  TenYearCHD  \n",
       "0          80.0     77.0           0  \n",
       "1          95.0     76.0           0  \n",
       "2          75.0     70.0           0  \n",
       "3          65.0    103.0           1  \n",
       "4          85.0     85.0           0  \n",
       "...         ...      ...         ...  \n",
       "4233       66.0     86.0           1  \n",
       "4234       65.0     68.0           0  \n",
       "4235       84.0     86.0           0  \n",
       "4236       86.0      NaN           0  \n",
       "4237       80.0    107.0           0  \n",
       "\n",
       "[4238 rows x 16 columns]>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e05394a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>currentSmoker</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4238.000000</td>\n",
       "      <td>4238.000000</td>\n",
       "      <td>4133.000000</td>\n",
       "      <td>4238.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4185.000000</td>\n",
       "      <td>4238.000000</td>\n",
       "      <td>4238.000000</td>\n",
       "      <td>4238.000000</td>\n",
       "      <td>4188.000000</td>\n",
       "      <td>4238.000000</td>\n",
       "      <td>4238.000000</td>\n",
       "      <td>4219.000000</td>\n",
       "      <td>4237.000000</td>\n",
       "      <td>3850.000000</td>\n",
       "      <td>4238.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.429212</td>\n",
       "      <td>49.584946</td>\n",
       "      <td>1.978950</td>\n",
       "      <td>0.494101</td>\n",
       "      <td>9.003089</td>\n",
       "      <td>0.029630</td>\n",
       "      <td>0.005899</td>\n",
       "      <td>0.310524</td>\n",
       "      <td>0.025720</td>\n",
       "      <td>236.721585</td>\n",
       "      <td>132.352407</td>\n",
       "      <td>82.893464</td>\n",
       "      <td>25.802008</td>\n",
       "      <td>75.878924</td>\n",
       "      <td>81.966753</td>\n",
       "      <td>0.151958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.495022</td>\n",
       "      <td>8.572160</td>\n",
       "      <td>1.019791</td>\n",
       "      <td>0.500024</td>\n",
       "      <td>11.920094</td>\n",
       "      <td>0.169584</td>\n",
       "      <td>0.076587</td>\n",
       "      <td>0.462763</td>\n",
       "      <td>0.158316</td>\n",
       "      <td>44.590334</td>\n",
       "      <td>22.038097</td>\n",
       "      <td>11.910850</td>\n",
       "      <td>4.080111</td>\n",
       "      <td>12.026596</td>\n",
       "      <td>23.959998</td>\n",
       "      <td>0.359023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>83.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>15.540000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>23.070000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>89.875000</td>\n",
       "      <td>28.040000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>696.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>142.500000</td>\n",
       "      <td>56.800000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              male          age    education  currentSmoker   cigsPerDay  \\\n",
       "count  4238.000000  4238.000000  4133.000000    4238.000000  4209.000000   \n",
       "mean      0.429212    49.584946     1.978950       0.494101     9.003089   \n",
       "std       0.495022     8.572160     1.019791       0.500024    11.920094   \n",
       "min       0.000000    32.000000     1.000000       0.000000     0.000000   \n",
       "25%       0.000000    42.000000     1.000000       0.000000     0.000000   \n",
       "50%       0.000000    49.000000     2.000000       0.000000     0.000000   \n",
       "75%       1.000000    56.000000     3.000000       1.000000    20.000000   \n",
       "max       1.000000    70.000000     4.000000       1.000000    70.000000   \n",
       "\n",
       "            BPMeds  prevalentStroke  prevalentHyp     diabetes      totChol  \\\n",
       "count  4185.000000      4238.000000   4238.000000  4238.000000  4188.000000   \n",
       "mean      0.029630         0.005899      0.310524     0.025720   236.721585   \n",
       "std       0.169584         0.076587      0.462763     0.158316    44.590334   \n",
       "min       0.000000         0.000000      0.000000     0.000000   107.000000   \n",
       "25%       0.000000         0.000000      0.000000     0.000000   206.000000   \n",
       "50%       0.000000         0.000000      0.000000     0.000000   234.000000   \n",
       "75%       0.000000         0.000000      1.000000     0.000000   263.000000   \n",
       "max       1.000000         1.000000      1.000000     1.000000   696.000000   \n",
       "\n",
       "             sysBP        diaBP          BMI    heartRate      glucose  \\\n",
       "count  4238.000000  4238.000000  4219.000000  4237.000000  3850.000000   \n",
       "mean    132.352407    82.893464    25.802008    75.878924    81.966753   \n",
       "std      22.038097    11.910850     4.080111    12.026596    23.959998   \n",
       "min      83.500000    48.000000    15.540000    44.000000    40.000000   \n",
       "25%     117.000000    75.000000    23.070000    68.000000    71.000000   \n",
       "50%     128.000000    82.000000    25.400000    75.000000    78.000000   \n",
       "75%     144.000000    89.875000    28.040000    83.000000    87.000000   \n",
       "max     295.000000   142.500000    56.800000   143.000000   394.000000   \n",
       "\n",
       "        TenYearCHD  \n",
       "count  4238.000000  \n",
       "mean      0.151958  \n",
       "std       0.359023  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       0.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7159f4fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male                 0\n",
       "age                  0\n",
       "education          105\n",
       "currentSmoker        0\n",
       "cigsPerDay          29\n",
       "BPMeds              53\n",
       "prevalentStroke      0\n",
       "prevalentHyp         0\n",
       "diabetes             0\n",
       "totChol             50\n",
       "sysBP                0\n",
       "diaBP                0\n",
       "BMI                 19\n",
       "heartRate            1\n",
       "glucose            388\n",
       "TenYearCHD           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "67cb08ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['glucose'].fillna(value = df['glucose'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "db0d6118",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['education'].fillna(value = df['education'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7066e84d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['heartRate'].fillna(value = df['heartRate'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "66ef568a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BMI'].fillna(value = df['BMI'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3bd688d",
   "metadata": {},
   "source": [
    "df['cigsPerDay'].fillna(value = df['cigsPerDay'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ec4d3fde",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['totChol'].fillna(value = df['totChol'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "494970fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BPMeds'].fillna(value = df['BPMeds'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8676a99f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male                0\n",
       "age                 0\n",
       "education           0\n",
       "currentSmoker       0\n",
       "cigsPerDay         29\n",
       "BPMeds              0\n",
       "prevalentStroke     0\n",
       "prevalentHyp        0\n",
       "diabetes            0\n",
       "totChol             0\n",
       "sysBP               0\n",
       "diaBP               0\n",
       "BMI                 0\n",
       "heartRate           0\n",
       "glucose             0\n",
       "TenYearCHD          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ad7e15f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male                0\n",
       "age                 0\n",
       "education           0\n",
       "currentSmoker       0\n",
       "cigsPerDay         29\n",
       "BPMeds              0\n",
       "prevalentStroke     0\n",
       "prevalentHyp        0\n",
       "diabetes            0\n",
       "totChol             0\n",
       "sysBP               0\n",
       "diaBP               0\n",
       "BMI                 0\n",
       "heartRate           0\n",
       "glucose             0\n",
       "TenYearCHD          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a8d4db4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the dependent and independent variables.\n",
    "x = df.drop(\"TenYearCHD\",axis=1)\n",
    "y = df['TenYearCHD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dde69f44",
   "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>male</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>currentSmoker</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>26.97</td>\n",
       "      <td>80.0</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>28.73</td>\n",
       "      <td>95.0</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>127.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>25.34</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>28.58</td>\n",
       "      <td>65.0</td>\n",
       "      <td>103.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>23.10</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.000000</td>\n",
       "    </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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4233</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>25.97</td>\n",
       "      <td>66.0</td>\n",
       "      <td>86.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4234</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>126.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.71</td>\n",
       "      <td>65.0</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4235</th>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.02963</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>86.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4236</th>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>126.5</td>\n",
       "      <td>87.0</td>\n",
       "      <td>19.16</td>\n",
       "      <td>86.0</td>\n",
       "      <td>81.966753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4237</th>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>133.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>21.47</td>\n",
       "      <td>80.0</td>\n",
       "      <td>107.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4238 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      male  age  education  currentSmoker  cigsPerDay   BPMeds  \\\n",
       "0        1   39        4.0              0         0.0  0.00000   \n",
       "1        0   46        2.0              0         0.0  0.00000   \n",
       "2        1   48        1.0              1        20.0  0.00000   \n",
       "3        0   61        3.0              1        30.0  0.00000   \n",
       "4        0   46        3.0              1        23.0  0.00000   \n",
       "...    ...  ...        ...            ...         ...      ...   \n",
       "4233     1   50        1.0              1         1.0  0.00000   \n",
       "4234     1   51        3.0              1        43.0  0.00000   \n",
       "4235     0   48        2.0              1        20.0  0.02963   \n",
       "4236     0   44        1.0              1        15.0  0.00000   \n",
       "4237     0   52        2.0              0         0.0  0.00000   \n",
       "\n",
       "      prevalentStroke  prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  \\\n",
       "0                   0             0         0    195.0  106.0   70.0  26.97   \n",
       "1                   0             0         0    250.0  121.0   81.0  28.73   \n",
       "2                   0             0         0    245.0  127.5   80.0  25.34   \n",
       "3                   0             1         0    225.0  150.0   95.0  28.58   \n",
       "4                   0             0         0    285.0  130.0   84.0  23.10   \n",
       "...               ...           ...       ...      ...    ...    ...    ...   \n",
       "4233                0             1         0    313.0  179.0   92.0  25.97   \n",
       "4234                0             0         0    207.0  126.5   80.0  19.71   \n",
       "4235                0             0         0    248.0  131.0   72.0  22.00   \n",
       "4236                0             0         0    210.0  126.5   87.0  19.16   \n",
       "4237                0             0         0    269.0  133.5   83.0  21.47   \n",
       "\n",
       "      heartRate     glucose  \n",
       "0          80.0   77.000000  \n",
       "1          95.0   76.000000  \n",
       "2          75.0   70.000000  \n",
       "3          65.0  103.000000  \n",
       "4          85.0   85.000000  \n",
       "...         ...         ...  \n",
       "4233       66.0   86.000000  \n",
       "4234       65.0   68.000000  \n",
       "4235       84.0   86.000000  \n",
       "4236       86.0   81.966753  \n",
       "4237       80.0  107.000000  \n",
       "\n",
       "[4238 rows x 15 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x #checking the features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a7942a",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f2f33ee8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8328511c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3252    0\n",
       "3946    0\n",
       "1261    0\n",
       "2536    0\n",
       "4089    0\n",
       "       ..\n",
       "3444    0\n",
       "466     0\n",
       "3092    0\n",
       "3772    0\n",
       "860     0\n",
       "Name: TenYearCHD, Length: 3390, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c089324",
   "metadata": {},
   "source": [
    "# Logistic Regression Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "07d73d2b",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input X contains NaN.\nLogisticRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[23], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinear_model\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LogisticRegression\n\u001b[1;32m----> 2\u001b[0m model \u001b[38;5;241m=\u001b[39m LogisticRegression()\u001b[38;5;241m.\u001b[39mfit(x_train,y_train)\n\u001b[0;32m      3\u001b[0m model\u001b[38;5;241m.\u001b[39mscore(x_train, y_train)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:1151\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1144\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1146\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1147\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1148\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1149\u001b[0m     )\n\u001b[0;32m   1150\u001b[0m ):\n\u001b[1;32m-> 1151\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1207\u001b[0m, in \u001b[0;36mLogisticRegression.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m   1204\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1205\u001b[0m     _dtype \u001b[38;5;241m=\u001b[39m [np\u001b[38;5;241m.\u001b[39mfloat64, np\u001b[38;5;241m.\u001b[39mfloat32]\n\u001b[1;32m-> 1207\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_data(\n\u001b[0;32m   1208\u001b[0m     X,\n\u001b[0;32m   1209\u001b[0m     y,\n\u001b[0;32m   1210\u001b[0m     accept_sparse\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsr\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1211\u001b[0m     dtype\u001b[38;5;241m=\u001b[39m_dtype,\n\u001b[0;32m   1212\u001b[0m     order\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mC\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1213\u001b[0m     accept_large_sparse\u001b[38;5;241m=\u001b[39msolver \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mliblinear\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msag\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msaga\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m   1214\u001b[0m )\n\u001b[0;32m   1215\u001b[0m check_classification_targets(y)\n\u001b[0;32m   1216\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclasses_ \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39munique(y)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:621\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[0;32m    619\u001b[0m         y \u001b[38;5;241m=\u001b[39m check_array(y, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[0;32m    620\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 621\u001b[0m         X, y \u001b[38;5;241m=\u001b[39m check_X_y(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n\u001b[0;32m    622\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[0;32m    624\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1147\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m   1142\u001b[0m         estimator_name \u001b[38;5;241m=\u001b[39m _check_estimator_name(estimator)\n\u001b[0;32m   1143\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1144\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires y to be passed, but the target y is None\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1145\u001b[0m     )\n\u001b[1;32m-> 1147\u001b[0m X \u001b[38;5;241m=\u001b[39m check_array(\n\u001b[0;32m   1148\u001b[0m     X,\n\u001b[0;32m   1149\u001b[0m     accept_sparse\u001b[38;5;241m=\u001b[39maccept_sparse,\n\u001b[0;32m   1150\u001b[0m     accept_large_sparse\u001b[38;5;241m=\u001b[39maccept_large_sparse,\n\u001b[0;32m   1151\u001b[0m     dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[0;32m   1152\u001b[0m     order\u001b[38;5;241m=\u001b[39morder,\n\u001b[0;32m   1153\u001b[0m     copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[0;32m   1154\u001b[0m     force_all_finite\u001b[38;5;241m=\u001b[39mforce_all_finite,\n\u001b[0;32m   1155\u001b[0m     ensure_2d\u001b[38;5;241m=\u001b[39mensure_2d,\n\u001b[0;32m   1156\u001b[0m     allow_nd\u001b[38;5;241m=\u001b[39mallow_nd,\n\u001b[0;32m   1157\u001b[0m     ensure_min_samples\u001b[38;5;241m=\u001b[39mensure_min_samples,\n\u001b[0;32m   1158\u001b[0m     ensure_min_features\u001b[38;5;241m=\u001b[39mensure_min_features,\n\u001b[0;32m   1159\u001b[0m     estimator\u001b[38;5;241m=\u001b[39mestimator,\n\u001b[0;32m   1160\u001b[0m     input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1161\u001b[0m )\n\u001b[0;32m   1163\u001b[0m y \u001b[38;5;241m=\u001b[39m _check_y(y, multi_output\u001b[38;5;241m=\u001b[39mmulti_output, y_numeric\u001b[38;5;241m=\u001b[39my_numeric, estimator\u001b[38;5;241m=\u001b[39mestimator)\n\u001b[0;32m   1165\u001b[0m check_consistent_length(X, y)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:959\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    953\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    954\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    955\u001b[0m             \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[0;32m    956\u001b[0m         )\n\u001b[0;32m    958\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[1;32m--> 959\u001b[0m         _assert_all_finite(\n\u001b[0;32m    960\u001b[0m             array,\n\u001b[0;32m    961\u001b[0m             input_name\u001b[38;5;241m=\u001b[39minput_name,\n\u001b[0;32m    962\u001b[0m             estimator_name\u001b[38;5;241m=\u001b[39mestimator_name,\n\u001b[0;32m    963\u001b[0m             allow_nan\u001b[38;5;241m=\u001b[39mforce_all_finite \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow-nan\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    964\u001b[0m         )\n\u001b[0;32m    966\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ensure_min_samples \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    967\u001b[0m     n_samples \u001b[38;5;241m=\u001b[39m _num_samples(array)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:124\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[0;32m    121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m first_pass_isfinite:\n\u001b[0;32m    122\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m--> 124\u001b[0m _assert_all_finite_element_wise(\n\u001b[0;32m    125\u001b[0m     X,\n\u001b[0;32m    126\u001b[0m     xp\u001b[38;5;241m=\u001b[39mxp,\n\u001b[0;32m    127\u001b[0m     allow_nan\u001b[38;5;241m=\u001b[39mallow_nan,\n\u001b[0;32m    128\u001b[0m     msg_dtype\u001b[38;5;241m=\u001b[39mmsg_dtype,\n\u001b[0;32m    129\u001b[0m     estimator_name\u001b[38;5;241m=\u001b[39mestimator_name,\n\u001b[0;32m    130\u001b[0m     input_name\u001b[38;5;241m=\u001b[39minput_name,\n\u001b[0;32m    131\u001b[0m )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:173\u001b[0m, in \u001b[0;36m_assert_all_finite_element_wise\u001b[1;34m(X, xp, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[0;32m    156\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator_name \u001b[38;5;129;01mand\u001b[39;00m input_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m has_nan_error:\n\u001b[0;32m    157\u001b[0m     \u001b[38;5;66;03m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[0;32m    158\u001b[0m     \u001b[38;5;66;03m# scikit-learn.\u001b[39;00m\n\u001b[0;32m    159\u001b[0m     msg_err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    160\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not accept missing values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    161\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    171\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#estimators-that-handle-nan-values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    172\u001b[0m     )\n\u001b[1;32m--> 173\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg_err)\n",
      "\u001b[1;31mValueError\u001b[0m: Input X contains NaN.\nLogisticRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression().fit(x_train,y_train)\n",
    "model.score(x_train, y_train)"
   ]
  }
 ],
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