PCA_load_digits.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ab775e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "72ec7420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = load_digits()\n",
    "dataset.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a89f7e9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.],\n",
       "       [ 0.,  0., 13., 15., 10., 15.,  5.,  0.],\n",
       "       [ 0.,  3., 15.,  2.,  0., 11.,  8.,  0.],\n",
       "       [ 0.,  4., 12.,  0.,  0.,  8.,  8.,  0.],\n",
       "       [ 0.,  5.,  8.,  0.,  0.,  9.,  8.,  0.],\n",
       "       [ 0.,  4., 11.,  0.,  1., 12.,  7.,  0.],\n",
       "       [ 0.,  2., 14.,  5., 10., 12.,  0.,  0.],\n",
       "       [ 0.,  0.,  6., 13., 10.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.data[0].reshape(8,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bedb5972",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e5de953df0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.gray()\n",
    "plt.matshow(dataset.data[8].reshape(8,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "08ff7972",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(dataset.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "52d59d28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.target[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ce58df16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>pixel_1_1</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel_6_6</th>\n",
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       "      <th>pixel_7_3</th>\n",
       "      <th>pixel_7_4</th>\n",
       "      <th>pixel_7_5</th>\n",
       "      <th>pixel_7_6</th>\n",
       "      <th>pixel_7_7</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pixel_0_0  pixel_0_1  pixel_0_2  pixel_0_3  pixel_0_4  pixel_0_5  \\\n",
       "0        0.0        0.0        5.0       13.0        9.0        1.0   \n",
       "1        0.0        0.0        0.0       12.0       13.0        5.0   \n",
       "2        0.0        0.0        0.0        4.0       15.0       12.0   \n",
       "3        0.0        0.0        7.0       15.0       13.0        1.0   \n",
       "4        0.0        0.0        0.0        1.0       11.0        0.0   \n",
       "\n",
       "   pixel_0_6  pixel_0_7  pixel_1_0  pixel_1_1  ...  pixel_6_6  pixel_6_7  \\\n",
       "0        0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "1        0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "2        0.0        0.0        0.0        0.0  ...        5.0        0.0   \n",
       "3        0.0        0.0        0.0        8.0  ...        9.0        0.0   \n",
       "4        0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "\n",
       "   pixel_7_0  pixel_7_1  pixel_7_2  pixel_7_3  pixel_7_4  pixel_7_5  \\\n",
       "0        0.0        0.0        6.0       13.0       10.0        0.0   \n",
       "1        0.0        0.0        0.0       11.0       16.0       10.0   \n",
       "2        0.0        0.0        0.0        3.0       11.0       16.0   \n",
       "3        0.0        0.0        7.0       13.0       13.0        9.0   \n",
       "4        0.0        0.0        0.0        2.0       16.0        4.0   \n",
       "\n",
       "   pixel_7_6  pixel_7_7  \n",
       "0        0.0        0.0  \n",
       "1        0.0        0.0  \n",
       "2        9.0        0.0  \n",
       "3        0.0        0.0  \n",
       "4        0.0        0.0  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(dataset.data, columns=dataset.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1bbc5398",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df\n",
    "y = dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7613d75a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.    , 0.    , 0.3125, ..., 0.    , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.    , ..., 0.625 , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.    , ..., 1.    , 0.5625, 0.    ],\n",
       "       ...,\n",
       "       [0.    , 0.    , 0.0625, ..., 0.375 , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.125 , ..., 0.75  , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.625 , ..., 0.75  , 0.0625, 0.    ]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ea4e778e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_test, X_train, y_test, y_train = train_test_split(X_scaled, y, test_size=0.2, random_state=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fc916ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9331941544885177"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6169a262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 27)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from sklearn.decomposition import PCA\n",
    "# pca = PCA(0.94)\n",
    "# X_pca = pca.fit_transform(X)\n",
    "# X_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d52f03a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train_pca, X_test_pca, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "eff01d38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777777777777777"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "# model_pca = RandomForestClassifier()\n",
    "# model_pca.fit(X_train_pca, y_train)\n",
    "# model_pca.score(X_test_pca, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6acb6abf",
   "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>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>160</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>98</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('heart.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1f2f836d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(918, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "21d892d6",
   "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>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  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d1876ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
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       "  <tbody>\n",
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       "      <th>76</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>118</td>\n",
       "      <td>529</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>130</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>130</td>\n",
       "      <td>603</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>125</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>67</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>115</td>\n",
       "      <td>564</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>160</td>\n",
       "      <td>N</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Flat</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "76    32   M           ASY        118          529          0     Normal   \n",
       "149   54   M           ASY        130          603          1     Normal   \n",
       "616   67   F           NAP        115          564          0        LVH   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "76     130              N      0.0     Flat             1  \n",
       "149    125              Y      1.0     Flat             1  \n",
       "616    160              N      1.6     Flat             0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Cholesterol>(df.Cholesterol.mean()+3*df.Cholesterol.std())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "78c5517b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(918, 12)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ee7e9637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(915, 12)"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "id": "e14ccd14",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Age, Sex, ChestPainType, RestingBP, Cholesterol, FastingBS, RestingECG, MaxHR, ExerciseAngina, Oldpeak, ST_Slope, HeartDisease]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.MaxHR>(df.MaxHR.mean()+3*df.MaxHR.std())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f9bd7745",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Age, Sex, ChestPainType, RestingBP, Cholesterol, FastingBS, RestingECG, MaxHR, ExerciseAngina, Oldpeak, ST_Slope, HeartDisease]\n",
       "Index: []"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[df.FastingBS>(df.FastingBS.mean()+3*df.FastingBS.std())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc8bcca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>166</th>\n",
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       "      <th>702</th>\n",
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       "      <td>178</td>\n",
       "      <td>270</td>\n",
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       "      <td>145</td>\n",
       "      <td>N</td>\n",
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       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>55</td>\n",
       "      <td>M</td>\n",
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       "      <td>140</td>\n",
       "      <td>217</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>111</td>\n",
       "      <td>Y</td>\n",
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       "    <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",
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       "      <th>850</th>\n",
       "      <td>62</td>\n",
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       "      <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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Oldpeak>(df.Oldpeak.mean()+3*df.Oldpeak.std())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a3175236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(909, 12)"
      ]
     },
     "execution_count": 18,
     "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,
   "id": "72621323",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>109</th>\n",
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       "      <th>241</th>\n",
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       "      <td>198</td>\n",
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       "      <td>142</td>\n",
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       "      <th>365</th>\n",
       "      <td>64</td>\n",
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       "      <td>200</td>\n",
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       "      <td>140</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>61</td>\n",
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       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ST</td>\n",
       "      <td>70</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>61</td>\n",
       "      <td>M</td>\n",
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       "      <td>190</td>\n",
       "      <td>287</td>\n",
       "      <td>1</td>\n",
       "      <td>LVH</td>\n",
       "      <td>150</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Down</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>56</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>200</td>\n",
       "      <td>288</td>\n",
       "      <td>1</td>\n",
       "      <td>LVH</td>\n",
       "      <td>133</td>\n",
       "      <td>Y</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Down</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>192</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>195</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "109   39   M           ATA        190          241          0     Normal   \n",
       "241   54   M           ASY        200          198          0     Normal   \n",
       "365   64   F           ASY        200            0          0     Normal   \n",
       "399   61   M           NAP        200            0          1         ST   \n",
       "592   61   M           ASY        190          287          1        LVH   \n",
       "732   56   F           ASY        200          288          1        LVH   \n",
       "759   54   M           ATA        192          283          0        LVH   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "109    106              N      0.0       Up             0  \n",
       "241    142              Y      2.0     Flat             1  \n",
       "365    140              Y      1.0     Flat             1  \n",
       "399     70              N      0.0     Flat             1  \n",
       "592    150              Y      2.0     Down             1  \n",
       "732    133              Y      4.0     Down             1  \n",
       "759    195              N      0.0       Up             1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.RestingBP>(df.RestingBP.mean()+3*df.RestingBP.std())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0dc80082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(902, 12)"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "id": "e74fde6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ATA', 'NAP', 'ASY', 'TA'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ChestPainType.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "da0c6ff2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Normal', 'ST', 'LVH'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.RestingECG.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "337daad4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['N', 'Y'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ExerciseAngina.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "16ccbea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Up', 'Flat', 'Down'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ST_Slope.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f5485b0a",
   "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>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>2</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>1</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS  RestingECG  \\\n",
       "0   40   M           ATA        140          289          0           1   \n",
       "1   49   F           NAP        160          180          0           1   \n",
       "2   37   M           ATA        130          283          0           2   \n",
       "3   48   F           ASY        138          214          0           1   \n",
       "4   54   M           NAP        150          195          0           1   \n",
       "\n",
       "   MaxHR  ExerciseAngina  Oldpeak  ST_Slope  HeartDisease  \n",
       "0    172               0      0.0         3             0  \n",
       "1    156               0      1.0         2             1  \n",
       "2     98               0      0.0         3             0  \n",
       "3    108               1      1.5         2             1  \n",
       "4    122               0      0.0         3             0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3.copy()\n",
    "df4.ExerciseAngina.replace(\n",
    "    {\n",
    "        'N': 0,\n",
    "        'Y': 1\n",
    "    },\n",
    "    inplace=True)\n",
    "\n",
    "df4.ST_Slope.replace(\n",
    "    {\n",
    "        'Down': 1,\n",
    "        'Flat': 2,\n",
    "        'Up': 3\n",
    "    },\n",
    "    inplace=True\n",
    ")\n",
    "\n",
    "df4.RestingECG.replace(\n",
    "    {\n",
    "        'Normal': 1,\n",
    "        'ST': 2,\n",
    "        'LVH': 3\n",
    "    },\n",
    "    inplace=True)\n",
    "\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8df8676e",
   "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>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",
       "      <th>Sex_M</th>\n",
       "      <th>ChestPainType_ATA</th>\n",
       "      <th>ChestPainType_NAP</th>\n",
       "      <th>ChestPainType_TA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  RestingBP  Cholesterol  FastingBS  RestingECG  MaxHR  ExerciseAngina  \\\n",
       "0   40        140          289          0           1    172               0   \n",
       "1   49        160          180          0           1    156               0   \n",
       "2   37        130          283          0           2     98               0   \n",
       "3   48        138          214          0           1    108               1   \n",
       "4   54        150          195          0           1    122               0   \n",
       "\n",
       "   Oldpeak  ST_Slope  HeartDisease  Sex_M  ChestPainType_ATA  \\\n",
       "0      0.0         3             0      1                  1   \n",
       "1      1.0         2             1      0                  0   \n",
       "2      0.0         3             0      1                  1   \n",
       "3      1.5         2             1      0                  0   \n",
       "4      0.0         3             0      1                  0   \n",
       "\n",
       "   ChestPainType_NAP  ChestPainType_TA  \n",
       "0                  0                 0  \n",
       "1                  1                 0  \n",
       "2                  0                 0  \n",
       "3                  0                 0  \n",
       "4                  1                 0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = pd.get_dummies(df4, drop_first=True)\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3bee4d9a",
   "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>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>Sex_M</th>\n",
       "      <th>ChestPainType_ATA</th>\n",
       "      <th>ChestPainType_NAP</th>\n",
       "      <th>ChestPainType_TA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  RestingBP  Cholesterol  FastingBS  RestingECG  MaxHR  ExerciseAngina  \\\n",
       "0   40        140          289          0           1    172               0   \n",
       "1   49        160          180          0           1    156               0   \n",
       "2   37        130          283          0           2     98               0   \n",
       "3   48        138          214          0           1    108               1   \n",
       "4   54        150          195          0           1    122               0   \n",
       "\n",
       "   Oldpeak  ST_Slope  Sex_M  ChestPainType_ATA  ChestPainType_NAP  \\\n",
       "0      0.0         3      1                  1                  0   \n",
       "1      1.0         2      0                  0                  1   \n",
       "2      0.0         3      1                  1                  0   \n",
       "3      1.5         2      0                  0                  0   \n",
       "4      0.0         3      1                  0                  1   \n",
       "\n",
       "   ChestPainType_TA  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df5.drop(\"HeartDisease\",axis = 'columns')\n",
    "y = df5.HeartDisease\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2c14a448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.42896269,  0.46089071,  0.85238015, ...,  2.06757196,\n",
       "        -0.53547478, -0.22914788],\n",
       "       [-0.47545956,  1.5925728 , -0.16132855, ..., -0.4836591 ,\n",
       "         1.86750159, -0.22914788],\n",
       "       [-1.74679706, -0.10495034,  0.79657967, ...,  2.06757196,\n",
       "        -0.53547478, -0.22914788],\n",
       "       ...,\n",
       "       [ 0.37209878, -0.10495034, -0.61703246, ..., -0.4836591 ,\n",
       "        -0.53547478, -0.22914788],\n",
       "       [ 0.37209878, -0.10495034,  0.35947592, ...,  2.06757196,\n",
       "        -0.53547478, -0.22914788],\n",
       "       [-1.64085227,  0.3477225 , -0.20782894, ..., -0.4836591 ,\n",
       "         1.86750159, -0.22914788]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e11b955f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d636c29e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(721, 13)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "46183356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181, 13)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8bed94ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8674033149171271"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model_rf = RandomForestClassifier()\n",
    "model_rf.fit(X_train, y_train)\n",
    "model_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "eb887bd3",
   "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>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>Sex_M</th>\n",
       "      <th>ChestPainType_ATA</th>\n",
       "      <th>ChestPainType_NAP</th>\n",
       "      <th>ChestPainType_TA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>45</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>68</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>57</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>57</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>38</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>902 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  RestingBP  Cholesterol  FastingBS  RestingECG  MaxHR  \\\n",
       "0     40        140          289          0           1    172   \n",
       "1     49        160          180          0           1    156   \n",
       "2     37        130          283          0           2     98   \n",
       "3     48        138          214          0           1    108   \n",
       "4     54        150          195          0           1    122   \n",
       "..   ...        ...          ...        ...         ...    ...   \n",
       "913   45        110          264          0           1    132   \n",
       "914   68        144          193          1           1    141   \n",
       "915   57        130          131          0           1    115   \n",
       "916   57        130          236          0           3    174   \n",
       "917   38        138          175          0           1    173   \n",
       "\n",
       "     ExerciseAngina  Oldpeak  ST_Slope  Sex_M  ChestPainType_ATA  \\\n",
       "0                 0      0.0         3      1                  1   \n",
       "1                 0      1.0         2      0                  0   \n",
       "2                 0      0.0         3      1                  1   \n",
       "3                 1      1.5         2      0                  0   \n",
       "4                 0      0.0         3      1                  0   \n",
       "..              ...      ...       ...    ...                ...   \n",
       "913               0      1.2         2      1                  0   \n",
       "914               0      3.4         2      1                  0   \n",
       "915               1      1.2         2      1                  0   \n",
       "916               0      0.0         2      0                  1   \n",
       "917               0      0.0         3      1                  0   \n",
       "\n",
       "     ChestPainType_NAP  ChestPainType_TA  \n",
       "0                    0                 0  \n",
       "1                    1                 0  \n",
       "2                    0                 0  \n",
       "3                    0                 0  \n",
       "4                    1                 0  \n",
       "..                 ...               ...  \n",
       "913                  0                 1  \n",
       "914                  0                 0  \n",
       "915                  0                 0  \n",
       "916                  0                 0  \n",
       "917                  1                 0  \n",
       "\n",
       "[902 rows x 13 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "894f1a2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 93.82465373, -29.40099458, -11.5858984 ],\n",
       "       [-15.58422331, -14.10909233, -31.680368  ],\n",
       "       [ 83.29606634,  38.6867453 ,  15.39992052],\n",
       "       ...,\n",
       "       [-67.57318721,  17.61319354,   4.35202042],\n",
       "       [ 40.70458237, -33.38750602,  -5.99332666],\n",
       "       [-19.91368346, -37.29085722, -13.31321738]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(0.99)\n",
    "X_pca = pca.fit_transform(X)\n",
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "cfd985da",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_pca, X_test_pca, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "5b698f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6906077348066298"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model_rf = RandomForestClassifier()\n",
    "model_rf.fit(X_train_pca, y_train)\n",
    "model_rf.score(X_test_pca, y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
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