Support_Vector_Machine_Practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7c22319b-3e69-465a-8fa6-3bd3345ed958",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import confusion_matrix, classification_report, ConfusionMatrixDisplay\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "899c0f84-f511-45fb-84b3-601db2c0f8a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('heart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e1c8901-442b-42ae-a0a9-76dc7d59f866",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "390c1dd5-b8bc-4155-8f41-b5e738efb617",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8985d863-081d-4841-b640-22335bea8bfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c13bdefe-d88d-4d2d-b19e-046b5c67a835",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age-Cholesterol plot')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#see if there is a relationship between age and cholesterol\n",
    "df.plot(kind='scatter', x='age', y = 'chol', alpha = 0.5, color='red')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Cholesterol')\n",
    "plt.title('Age-Cholesterol plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d120aeb-91fe-4ad5-9494-02e5ee63c3ca",
   "metadata": {},
   "source": [
    "We can confirm that as age increases there is a positive correlation with cholersterol levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "15af4cc5-6c55-42a8-8c35-1247e12c2ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert categorical variables\n",
    "df_new = pd.get_dummies(df, columns = ['cp', 'thal'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8da56f8c-4855-40bb-a5e4-1fbed1d3f6aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#define X and y for model\n",
    "X = df_new.drop('target', axis = 1) #do not include target variable, y, in X\n",
    "y = df_new.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5a145dfc-e371-4d40-83be-5e2758196d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e7c68712-65b5-44fa-b479-ba29eac6aa13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "ml = svm.SVC()\n",
    "\n",
    "para_grid = {'C' : [1, 10, 100, 1000], 'gamma' :[1, 0.1, 0.01, 0.001], 'kernel':['rbf']}\n",
    "\n",
    "grid = GridSearchCV(ml, para_grid, refit=True, verbose=1, cv=5, n_jobs = -1)\n",
    "\n",
    "grid_search = grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e05f7d11-852d-4c05-bd51-d91631cca845",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "#print best parameter\n",
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5c1f9b0d-c08a-4a6a-bed0-2a003f7521a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d813a46c-7771-4923-a095-eb4da94b171b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5946428571428571"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ffcae358-31a2-42e0-8ed6-fb2547715a7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_hat = grid.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "03ad92b1-bc2a-4a02-b137-9c59f9ee58e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#confusion matrix for test case\n",
    "#confusion_matrix(y_test, y_test_hat)\n",
    "#clf = SVC(random_state=0)\n",
    "#clf.fit(X_train, y_train)\n",
    "#SVC(random_state=0)\n",
    "ConfusionMatrixDisplay.from_estimator(grid, X_test, y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "678e8168-7c04-46f0-b4c1-b86e8b1e8f99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.42      0.57        33\n",
      "           1       0.58      0.93      0.71        28\n",
      "\n",
      "    accuracy                           0.66        61\n",
      "   macro avg       0.73      0.68      0.64        61\n",
      "weighted avg       0.74      0.66      0.64        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_test_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f8acb0c-e8df-4f4d-80c8-6ec0d91e76bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We see the model has an overall accuracy of 66%\n",
    "# Precision in \"yes\" (1) outcome is about 58% and recall is about 93%"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 [3.10]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}