project_YunjinNA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Artificial Intelligence : Project Assignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Disease Data\n",
    "\n",
    "Import python modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "load the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>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",
       "    </tr>\n",
       "    <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('../input/heart.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data information:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data description (Attribute Information)\n",
    "1. age - Age of the patient (Continuous)\n",
    "2. sex - Sex of the patient (Categorical – 2 values : Male, Female)\n",
    "3. cp - Chest pain type (Categorical - 4 values : types, 1:typical angina Type 2:atypical angina Type 3:non-anginal pain Type 4:asymptomatic)\n",
    "4. trestbps - Resting blood pressure in mm Hg on admission to the hospital (Continuous)\n",
    "5. chol - Serum cholestoral in mg/dl (Continuous)\n",
    "6. fbs - Fasting blood sugar > 120 mg/dl (Categorical - 2 values : True, False)\n",
    "7. restecg - Resting electrocardiographic results (Categorical - 3 values : N(Normal), L1(Level 1), L2(Level 2))\n",
    "8. thalach - Maximum heart rate achieved (Continuous)\n",
    "9. exang - Exercise induced angina (Categorical - 2 values : Yes, No)\n",
    "10. oldpeak - ST depression induced by exercise relative to rest (Continuous)\n",
    "11. slope - The slope of the peak exercise ST segment (Categorical - 3 values : Up, Flat, Down)\n",
    "12. ca - Number of major vessels (0-3) colored by flourosopy (Categorical - 4 values : 0, 1, 2, 3)\n",
    "13. thal - The heart status as retrieved from Thallium test (Categorical - 3 values- N (normal), FD(fixed defect), RD (reversible defect)\n",
    "14. target - The predicted attribute, not used in data analysis directly (Categorical – 1 : diseased, 0 : not heart disease))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(303, 14)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "age           int64\n",
       "sex           int64\n",
       "cp            int64\n",
       "trestbps      int64\n",
       "chol          int64\n",
       "fbs           int64\n",
       "restecg       int64\n",
       "thalach       int64\n",
       "exang         int64\n",
       "oldpeak     float64\n",
       "slope         int64\n",
       "ca            int64\n",
       "thal          int64\n",
       "target        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data.shape)\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(figsize=(10,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 15))\n",
    "sns.heatmap(data.corr(), annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train, test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.target.values\n",
    "x_temp = data.drop(['target'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = (x_temp -np.min(x_temp))/(np.max(x_temp)-np.min(x_temp)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=109)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape:  (212, 13)\n",
      "Y_train shape:  (212,)\n"
     ]
    }
   ],
   "source": [
    "print('X_train shape: ',X_train.shape)\n",
    "print('Y_train shape: ',y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN (Supervised Learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(metric='euclidean', n_neighbors=2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "knn = KNeighborsClassifier(n_neighbors = 2, metric = 'euclidean')\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training - to define best value of $k$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score : 85.71%\n"
     ]
    }
   ],
   "source": [
    "score = []\n",
    "for i in range(1, 20):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i, metric='euclidean')\n",
    "    knn.fit(X_train, y_train)\n",
    "    y_pred = knn.predict(X_test)\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    score.append(np.sum(np.diag(cm))/np.sum(cm))\n",
    "        \n",
    "plt.plot(range(1,20), score)\n",
    "plt.xticks(np.arange(1,20,1))\n",
    "plt.xlabel('K value')\n",
    "plt.ylabel('Score')\n",
    "plt.show()\n",
    "\n",
    "acc = max(score)\n",
    "accuracy_['KNN']=acc\n",
    "print('Score : {:.2%}'.format(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the model is trained, use it to predict the values for test subset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20965e307c8>"
      ]
     },
     "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": [
    "plt.scatter(\n",
    "    x='trestbps', \n",
    "    y='thalach',\n",
    "    c='target',\n",
    "    cmap = 'coolwarm',\n",
    "    alpha=0.7,\n",
    "    data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM (Supervised Learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score : 89.01%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "svm = SVC(kernel = 'linear', random_state = 777)\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "y_pred = svm.predict(X_test)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "accuracy_['SVM']=acc\n",
    "print('Score : {:.2%}'.format(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes (Supervised Learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score : 85.71%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb = GaussianNB()\n",
    "nb.fit(X_train, y_train)\n",
    "\n",
    "y_pred = nb.predict(X_test)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "accuracy_['NB'] = acc\n",
    "print('Score : {:.2%}'.format(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree (Supervised Learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score : 73.63%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtree = DecisionTreeClassifier()\n",
    "dtree.fit(X_train, y_train)\n",
    "\n",
    "y_pred = dtree.predict(X_test)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "accuracy_['Decision Tree'] = acc\n",
    "print('Score : {:.2%}'.format(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest (Supervised Learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score : 89.01%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators=1000, random_state=777)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rf.predict(X_test)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "accuracy_['Random Forest'] = acc\n",
    "print('Score : {:.2%}'.format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   15.2s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  3.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(), n_iter=100,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'max_depth': [10, 20, 30, 40, 50, 60,\n",
       "                                                      70, 80, 90, 100, 110,\n",
       "                                                      None],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [200, 400, 600, 800,\n",
       "                                                         1000, 1200, 1400, 1600,\n",
       "                                                         1800, 2000]},\n",
       "                   random_state=42, verbose=2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "rf = RandomForestClassifier()\n",
    "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n",
    "max_features = ['auto', 'sqrt']\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "min_samples_split = [2, 5, 10]\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "\n",
    "bootstrap = [True, False]\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, \n",
    "                               cv = 3, verbose=2, random_state=42, n_jobs = -1)\n",
    "rf_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 600,\n",
       " 'min_samples_split': 5,\n",
       " 'min_samples_leaf': 2,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 70,\n",
       " 'bootstrap': False}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  90.11%\n"
     ]
    }
   ],
   "source": [
    "best_rf = RandomForestClassifier(bootstrap=False,\n",
    "                                 max_depth=70,\n",
    "                                 max_features='auto',\n",
    "                                 min_samples_leaf=2,\n",
    "                                 min_samples_split=5,\n",
    "                                 n_estimators=600)\n",
    "best_rf.fit(X_train, y_train)\n",
    "y_pred = best_rf.predict(X_test)\n",
    "scores = accuracy_score(y_test, y_pred)\n",
    "accuracy_['rf_grid search'] = scores\n",
    "print('Accuracy: ','{:2.2%}'.format(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential \n",
    "from keras.layers import Dense \n",
    "from keras.optimizers import adam\n",
    "from keras.layers import Dropout\n",
    "from keras.utils.np_utils import to_categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y data (target) to categorical type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = to_categorical(y_train, num_classes=None)\n",
    "Y_test = to_categorical(y_test, num_classes=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def neural_network():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(16, input_dim=13, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Dense(8, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Dense(2, activation='softmax'))\n",
    "    \n",
    "    model.compile(loss='categorical_crossentropy', \n",
    "                  optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 16)                224       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 8)                 136       \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 8)                 0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2)                 18        \n",
      "=================================================================\n",
      "Total params: 378\n",
      "Trainable params: 378\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = neural_network()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 212 samples, validate on 91 samples\n",
      "Epoch 1/10\n",
      "212/212 [==============================] - 0s 1ms/step - loss: 0.6926 - accuracy: 0.4953 - val_loss: 0.6882 - val_accuracy: 0.6923\n",
      "Epoch 2/10\n",
      "212/212 [==============================] - 0s 159us/step - loss: 0.6870 - accuracy: 0.6698 - val_loss: 0.6806 - val_accuracy: 0.7912\n",
      "Epoch 3/10\n",
      "212/212 [==============================] - 0s 123us/step - loss: 0.6764 - accuracy: 0.7311 - val_loss: 0.6587 - val_accuracy: 0.8462\n",
      "Epoch 4/10\n",
      "212/212 [==============================] - 0s 123us/step - loss: 0.6553 - accuracy: 0.7311 - val_loss: 0.6285 - val_accuracy: 0.8681\n",
      "Epoch 5/10\n",
      "212/212 [==============================] - 0s 136us/step - loss: 0.6201 - accuracy: 0.7453 - val_loss: 0.5903 - val_accuracy: 0.8571\n",
      "Epoch 6/10\n",
      "212/212 [==============================] - 0s 112us/step - loss: 0.5877 - accuracy: 0.8066 - val_loss: 0.5453 - val_accuracy: 0.8462\n",
      "Epoch 7/10\n",
      "212/212 [==============================] - 0s 111us/step - loss: 0.5582 - accuracy: 0.7642 - val_loss: 0.5049 - val_accuracy: 0.8462\n",
      "Epoch 8/10\n",
      "212/212 [==============================] - 0s 121us/step - loss: 0.5188 - accuracy: 0.7972 - val_loss: 0.4684 - val_accuracy: 0.8462\n",
      "Epoch 9/10\n",
      "212/212 [==============================] - 0s 125us/step - loss: 0.4953 - accuracy: 0.7925 - val_loss: 0.4387 - val_accuracy: 0.8681\n",
      "Epoch 10/10\n",
      "212/212 [==============================] - 0s 143us/step - loss: 0.4813 - accuracy: 0.7642 - val_loss: 0.4195 - val_accuracy: 0.8681\n",
      "91/91 [==============================] - 0s 39us/step\n",
      "accuracy: 86.81%\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=10, batch_size=10)\n",
    "\n",
    "scores = model.evaluate(X_test, Y_test)\n",
    "accuracy_['Neural Network'] = scores[1]\n",
    "print(\"accuracy: %.2f%%\" % (scores[1]*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.yticks(np.arange(0, 100, 10))\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.xlabel('Methods')\n",
    "sns.barplot(x=list(accuracy_.keys()), y=list(accuracy_.values()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random forest method which apply the best parameters by grid search has the best accuracy.\n",
    "\n",
    "Conclusion : Random Forest Grid Search > Random Forest = SVM > Neural Net > NB = KNN > Decision Tree"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}