lecture19hw.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we have heart.csv file and now we have to predict that what is the chance of heart attack ..\n",
    "Here PCA(Principal Component Analysis) will be used.."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PCA:\n",
    "It is a process of figuring out most important features or principal components that has the most impact on the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>40</td>\n",
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       "      <td>140</td>\n",
       "      <td>289</td>\n",
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       "      <td>Normal</td>\n",
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       "      <td>ATA</td>\n",
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       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>108</td>\n",
       "      <td>Y</td>\n",
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       "      <td>54</td>\n",
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      ],
      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "3   48   F           ASY        138          214          0     Normal    108   \n",
       "4   54   M           NAP        150          195          0     Normal    122   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0              N      0.0       Up             0  \n",
       "1              N      1.0     Flat             1  \n",
       "2              N      0.0       Up             0  \n",
       "3              Y      1.5     Flat             1  \n",
       "4              N      0.0       Up             0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"heart.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.510893</td>\n",
       "      <td>132.396514</td>\n",
       "      <td>198.799564</td>\n",
       "      <td>0.233115</td>\n",
       "      <td>136.809368</td>\n",
       "      <td>0.887364</td>\n",
       "      <td>0.553377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.432617</td>\n",
       "      <td>18.514154</td>\n",
       "      <td>109.384145</td>\n",
       "      <td>0.423046</td>\n",
       "      <td>25.460334</td>\n",
       "      <td>1.066570</td>\n",
       "      <td>0.497414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-2.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age   RestingBP  Cholesterol   FastingBS       MaxHR  \\\n",
       "count  918.000000  918.000000   918.000000  918.000000  918.000000   \n",
       "mean    53.510893  132.396514   198.799564    0.233115  136.809368   \n",
       "std      9.432617   18.514154   109.384145    0.423046   25.460334   \n",
       "min     28.000000    0.000000     0.000000    0.000000   60.000000   \n",
       "25%     47.000000  120.000000   173.250000    0.000000  120.000000   \n",
       "50%     54.000000  130.000000   223.000000    0.000000  138.000000   \n",
       "75%     60.000000  140.000000   267.000000    0.000000  156.000000   \n",
       "max     77.000000  200.000000   603.000000    1.000000  202.000000   \n",
       "\n",
       "          Oldpeak  HeartDisease  \n",
       "count  918.000000    918.000000  \n",
       "mean     0.887364      0.553377  \n",
       "std      1.066570      0.497414  \n",
       "min     -2.600000      0.000000  \n",
       "25%      0.000000      0.000000  \n",
       "50%      0.600000      1.000000  \n",
       "75%      1.500000      1.000000  \n",
       "max      6.200000      1.000000  "
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(\"HeartDisease\",axis='columns')\n",
    "y=df.HeartDisease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>ExerciseAngina</th>\n",
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       "      <th>0</th>\n",
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       "      <td>NAP</td>\n",
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       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>156</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
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       "      <td>N</td>\n",
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      ],
      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0              N      0.0       Up             0  \n",
       "1              N      1.0     Flat             1  \n",
       "2              N      0.0       Up             0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(915, 12)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df[df.Cholesterol<=(df.Cholesterol.mean()+3*df.Cholesterol.std())]\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "Empty DataFrame\n",
       "Columns: [Age, Sex, ChestPainType, RestingBP, Cholesterol, FastingBS, RestingECG, MaxHR, ExerciseAngina, Oldpeak, ST_Slope, HeartDisease]\n",
       "Index: []"
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   "source": [
    "df[df.MaxHR>(df.MaxHR.mean()+3*df.MaxHR.std())]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
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       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>145</td>\n",
       "      <td>N</td>\n",
       "      <td>6.2</td>\n",
       "      <td>Down</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>58</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>114</td>\n",
       "      <td>318</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>140</td>\n",
       "      <td>N</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Down</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "166   50   M           ASY        140          231          0         ST   \n",
       "702   59   M            TA        178          270          0        LVH   \n",
       "771   55   M           ASY        140          217          0     Normal   \n",
       "791   51   M           ASY        140          298          0     Normal   \n",
       "850   62   F           ASY        160          164          0        LVH   \n",
       "900   58   M           ASY        114          318          0         ST   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "166    140              Y      5.0     Flat             1  \n",
       "702    145              N      4.2     Down             0  \n",
       "771    111              Y      5.6     Down             1  \n",
       "791    122              Y      4.2     Flat             1  \n",
       "850    145              N      6.2     Down             1  \n",
       "900    140              N      4.4     Down             1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Oldpeak>(df.Oldpeak.mean()+3*df.Oldpeak.std())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>50</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>140</td>\n",
       "      <td>231</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>140</td>\n",
       "      <td>Y</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>59</td>\n",
       "      <td>M</td>\n",
       "      <td>TA</td>\n",
       "      <td>178</td>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>145</td>\n",
       "      <td>N</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Down</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>55</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>140</td>\n",
       "      <td>217</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>111</td>\n",
       "      <td>Y</td>\n",
       "      <td>5.6</td>\n",
       "      <td>Down</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791</th>\n",
       "      <td>51</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>140</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>Y</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>62</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>160</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>145</td>\n",
       "      <td>N</td>\n",
       "      <td>6.2</td>\n",
       "      <td>Down</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>58</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>114</td>\n",
       "      <td>318</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>140</td>\n",
       "      <td>N</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Down</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "166   50   M           ASY        140          231          0         ST   \n",
       "702   59   M            TA        178          270          0        LVH   \n",
       "771   55   M           ASY        140          217          0     Normal   \n",
       "791   51   M           ASY        140          298          0     Normal   \n",
       "850   62   F           ASY        160          164          0        LVH   \n",
       "900   58   M           ASY        114          318          0         ST   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "166    140              Y      5.0     Flat             1  \n",
       "702    145              N      4.2     Down             0  \n",
       "771    111              Y      5.6     Down             1  \n",
       "791    122              Y      4.2     Flat             1  \n",
       "850    145              N      6.2     Down             1  \n",
       "900    140              N      4.4     Down             1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Oldpeak>(df.Oldpeak.mean()+3*df.Oldpeak.std())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(909, 12)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1[df1.Oldpeak<=(df1.Oldpeak.mean()+3*df1.Oldpeak.std())]\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(902, 12)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2[df2.RestingBP<=(df2.RestingBP.mean()+3*df2.RestingBP.std())]\n",
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ATA', 'NAP', 'ASY', 'TA'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ChestPainType.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Normal', 'ST', 'LVH'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.RestingECG.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['N', 'Y'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ExerciseAngina.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,x_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'M'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[12], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlinear_model\u001b[39;00m \u001b[39mimport\u001b[39;00m LogisticRegressionCV\n\u001b[1;32m      2\u001b[0m model\u001b[39m=\u001b[39mLogisticRegressionCV()\n\u001b[0;32m----> 3\u001b[0m model\u001b[39m.\u001b[39;49mfit(X_train,y_train)\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1792\u001b[0m, in \u001b[0;36mLogisticRegressionCV.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1785\u001b[0m         warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m   1786\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39ml1_ratios parameter is only used when penalty \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1787\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mis \u001b[39m\u001b[39m'\u001b[39m\u001b[39melasticnet\u001b[39m\u001b[39m'\u001b[39m\u001b[39m. Got (penalty=\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpenalty)\n\u001b[1;32m   1788\u001b[0m         )\n\u001b[1;32m   1790\u001b[0m     l1_ratios_ \u001b[39m=\u001b[39m [\u001b[39mNone\u001b[39;00m]\n\u001b[0;32m-> 1792\u001b[0m X, y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(\n\u001b[1;32m   1793\u001b[0m     X,\n\u001b[1;32m   1794\u001b[0m     y,\n\u001b[1;32m   1795\u001b[0m     accept_sparse\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mcsr\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m   1796\u001b[0m     dtype\u001b[39m=\u001b[39;49mnp\u001b[39m.\u001b[39;49mfloat64,\n\u001b[1;32m   1797\u001b[0m     order\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mC\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m   1798\u001b[0m     accept_large_sparse\u001b[39m=\u001b[39;49msolver \u001b[39mnot\u001b[39;49;00m \u001b[39min\u001b[39;49;00m [\u001b[39m\"\u001b[39;49m\u001b[39mliblinear\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39msag\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39msaga\u001b[39;49m\u001b[39m\"\u001b[39;49m],\n\u001b[1;32m   1799\u001b[0m )\n\u001b[1;32m   1800\u001b[0m check_classification_targets(y)\n\u001b[1;32m   1802\u001b[0m class_weight \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclass_weight\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/sklearn/base.py:554\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    552\u001b[0m         y \u001b[39m=\u001b[39m check_array(y, input_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_y_params)\n\u001b[1;32m    553\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 554\u001b[0m         X, y \u001b[39m=\u001b[39m check_X_y(X, y, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcheck_params)\n\u001b[1;32m    555\u001b[0m     out \u001b[39m=\u001b[39m X, y\n\u001b[1;32m    557\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m check_params\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mensure_2d\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mTrue\u001b[39;00m):\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/sklearn/utils/validation.py:1104\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[0;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[1;32m   1099\u001b[0m         estimator_name \u001b[39m=\u001b[39m _check_estimator_name(estimator)\n\u001b[1;32m   1100\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m   1101\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mestimator_name\u001b[39m}\u001b[39;00m\u001b[39m requires y to be passed, but the target y is None\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1102\u001b[0m     )\n\u001b[0;32m-> 1104\u001b[0m X \u001b[39m=\u001b[39m check_array(\n\u001b[1;32m   1105\u001b[0m     X,\n\u001b[1;32m   1106\u001b[0m     accept_sparse\u001b[39m=\u001b[39;49maccept_sparse,\n\u001b[1;32m   1107\u001b[0m     accept_large_sparse\u001b[39m=\u001b[39;49maccept_large_sparse,\n\u001b[1;32m   1108\u001b[0m     dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[1;32m   1109\u001b[0m     order\u001b[39m=\u001b[39;49morder,\n\u001b[1;32m   1110\u001b[0m     copy\u001b[39m=\u001b[39;49mcopy,\n\u001b[1;32m   1111\u001b[0m     force_all_finite\u001b[39m=\u001b[39;49mforce_all_finite,\n\u001b[1;32m   1112\u001b[0m     ensure_2d\u001b[39m=\u001b[39;49mensure_2d,\n\u001b[1;32m   1113\u001b[0m     allow_nd\u001b[39m=\u001b[39;49mallow_nd,\n\u001b[1;32m   1114\u001b[0m     ensure_min_samples\u001b[39m=\u001b[39;49mensure_min_samples,\n\u001b[1;32m   1115\u001b[0m     ensure_min_features\u001b[39m=\u001b[39;49mensure_min_features,\n\u001b[1;32m   1116\u001b[0m     estimator\u001b[39m=\u001b[39;49mestimator,\n\u001b[1;32m   1117\u001b[0m     input_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mX\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m   1118\u001b[0m )\n\u001b[1;32m   1120\u001b[0m y \u001b[39m=\u001b[39m _check_y(y, multi_output\u001b[39m=\u001b[39mmulti_output, y_numeric\u001b[39m=\u001b[39my_numeric, estimator\u001b[39m=\u001b[39mestimator)\n\u001b[1;32m   1122\u001b[0m check_consistent_length(X, y)\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/sklearn/utils/validation.py:877\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;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[1;32m    875\u001b[0m         array \u001b[39m=\u001b[39m xp\u001b[39m.\u001b[39mastype(array, dtype, copy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m    876\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 877\u001b[0m         array \u001b[39m=\u001b[39m _asarray_with_order(array, order\u001b[39m=\u001b[39;49morder, dtype\u001b[39m=\u001b[39;49mdtype, xp\u001b[39m=\u001b[39;49mxp)\n\u001b[1;32m    878\u001b[0m \u001b[39mexcept\u001b[39;00m ComplexWarning \u001b[39mas\u001b[39;00m complex_warning:\n\u001b[1;32m    879\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    880\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mComplex data not supported\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{}\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(array)\n\u001b[1;32m    881\u001b[0m     ) \u001b[39mfrom\u001b[39;00m \u001b[39mcomplex_warning\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/sklearn/utils/_array_api.py:185\u001b[0m, in \u001b[0;36m_asarray_with_order\u001b[0;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[1;32m    182\u001b[0m     xp, _ \u001b[39m=\u001b[39m get_namespace(array)\n\u001b[1;32m    183\u001b[0m \u001b[39mif\u001b[39;00m xp\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m \u001b[39min\u001b[39;00m {\u001b[39m\"\u001b[39m\u001b[39mnumpy\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnumpy.array_api\u001b[39m\u001b[39m\"\u001b[39m}:\n\u001b[1;32m    184\u001b[0m     \u001b[39m# Use NumPy API to support order\u001b[39;00m\n\u001b[0;32m--> 185\u001b[0m     array \u001b[39m=\u001b[39m numpy\u001b[39m.\u001b[39;49masarray(array, order\u001b[39m=\u001b[39;49morder, dtype\u001b[39m=\u001b[39;49mdtype)\n\u001b[1;32m    186\u001b[0m     \u001b[39mreturn\u001b[39;00m xp\u001b[39m.\u001b[39masarray(array, copy\u001b[39m=\u001b[39mcopy)\n\u001b[1;32m    187\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/core/generic.py:2070\u001b[0m, in \u001b[0;36mNDFrame.__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m   2069\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__array__\u001b[39m(\u001b[39mself\u001b[39m, dtype: npt\u001b[39m.\u001b[39mDTypeLike \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m np\u001b[39m.\u001b[39mndarray:\n\u001b[0;32m-> 2070\u001b[0m     \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39;49masarray(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_values, dtype\u001b[39m=\u001b[39;49mdtype)\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'M'"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "model=LogisticRegressionCV()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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