.L21HW.ipynb

{
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       "  <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",
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       "      <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",
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       "      <td>283</td>\n",
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       "      <td>ST</td>\n",
       "      <td>98</td>\n",
       "      <td>N</td>\n",
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       "      <td>Up</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",
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       "      <td>1</td>\n",
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       "      <td>141</td>\n",
       "      <td>N</td>\n",
       "      <td>3.4</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>57</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>115</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>57</td>\n",
       "      <td>F</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>174</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>Up</td>\n",
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       "<p>918 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "0     40   M           ATA        140          289          0     Normal   \n",
       "1     49   F           NAP        160          180          0     Normal   \n",
       "2     37   M           ATA        130          283          0         ST   \n",
       "3     48   F           ASY        138          214          0     Normal   \n",
       "4     54   M           NAP        150          195          0     Normal   \n",
       "..   ...  ..           ...        ...          ...        ...        ...   \n",
       "913   45   M            TA        110          264          0     Normal   \n",
       "914   68   M           ASY        144          193          1     Normal   \n",
       "915   57   M           ASY        130          131          0     Normal   \n",
       "916   57   F           ATA        130          236          0        LVH   \n",
       "917   38   M           NAP        138          175          0     Normal   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0      172              N      0.0       Up             0  \n",
       "1      156              N      1.0     Flat             1  \n",
       "2       98              N      0.0       Up             0  \n",
       "3      108              Y      1.5     Flat             1  \n",
       "4      122              N      0.0       Up             0  \n",
       "..     ...            ...      ...      ...           ...  \n",
       "913    132              N      1.2     Flat             1  \n",
       "914    141              N      3.4     Flat             1  \n",
       "915    115              Y      1.2     Flat             1  \n",
       "916    174              N      0.0     Flat             1  \n",
       "917    173              N      0.0       Up             0  \n",
       "\n",
       "[918 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"heart.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pushk\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\preprocessing\\_label.py:114: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\pushk\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\preprocessing\\_label.py:114: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\pushk\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\preprocessing\\_label.py:114: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\pushk\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\preprocessing\\_label.py:114: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\pushk\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\preprocessing\\_label.py:114: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
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      ],
      "text/plain": [
       "     Age  Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  RestingECG  \\\n",
       "0     40    2              1        140          289          0           1   \n",
       "1     49    1              2        160          180          0           1   \n",
       "2     37    2              1        130          283          0           2   \n",
       "3     48    1              0        138          214          0           1   \n",
       "4     54    2              2        150          195          0           1   \n",
       "..   ...  ...            ...        ...          ...        ...         ...   \n",
       "913   45    1              3        110          264          0           1   \n",
       "914   68    1              0        144          193          1           1   \n",
       "915   57    1              0        130          131          0           1   \n",
       "916   57    1              1        130          236          0           0   \n",
       "917   38    2              2        138          175          0           1   \n",
       "\n",
       "     MaxHR  ExerciseAngina  Oldpeak  ST_Slope  HeartDisease  \n",
       "0      172               2      0.0         2             0  \n",
       "1      156               1      1.0         1             1  \n",
       "2       98               2      0.0         2             0  \n",
       "3      108               1      1.5         1             1  \n",
       "4      122               2      0.0         2             0  \n",
       "..     ...             ...      ...       ...           ...  \n",
       "913    132               1      1.2         1             1  \n",
       "914    141               1      3.4         1             1  \n",
       "915    115               1      1.2         1             1  \n",
       "916    174               1      0.0         1             1  \n",
       "917    173               2      0.0         2             0  \n",
       "\n",
       "[918 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ChestPainType.unique()\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "df['ChestPainType'] = le.fit_transform(df[['ChestPainType']])\n",
    "df['RestingECG'] = le.fit_transform(df[['RestingECG']])\n",
    "df['ST_Slope'] = le.fit_transform(df[['ST_Slope']])\n",
    "df['Sex'] = le.fit_transform(df[['ST_Slope']])\n",
    "df['ExerciseAngina'] = le.fit_transform(df[['ST_Slope']])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      0\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "913    1\n",
       "914    1\n",
       "915    1\n",
       "916    1\n",
       "917    0\n",
       "Name: HeartDisease, Length: 918, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "y = df['HeartDisease']\n",
    "X = df.drop(['HeartDisease'], axis='columns')\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7652173913043478"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y)\n",
    "modelsvc = SVC()\n",
    "modelsvc.fit(X_train,y_train)\n",
    "modelsvc.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6873129009265859"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "bag_model = BaggingClassifier(\n",
    "    estimator=SVC(), \n",
    "    n_estimators=100,\n",
    "    max_samples=0.8,\n",
    "    oob_score=True,\n",
    "    random_state=0\n",
    ")\n",
    "scores = cross_val_score(bag_model, X, y, cv=5)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8173913043478261"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.790775718698028"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_model = BaggingClassifier(\n",
    "    estimator=DecisionTreeClassifier(), \n",
    "    n_estimators=100,\n",
    "    max_samples=0.8,\n",
    "    oob_score=True,\n",
    "    random_state=0\n",
    ")\n",
    "scores1 = cross_val_score(bag_model, X, y, cv=5)\n",
    "scores1.mean()"
   ]
  }
 ],
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