hear_disease_predictor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe29a74a",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b9266b1c",
   "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.metrics import classification_report\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6f60ed81",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>52</td>\n",
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       "      <td>125</td>\n",
       "      <td>212</td>\n",
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       "      <td>168</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
       "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   2     3       0  \n",
       "1   0     3       0  \n",
       "2   0     3       0  \n",
       "3   1     3       0  \n",
       "4   3     2       0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('heart.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ededc776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1025, 14)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9e9c4f70",
   "metadata": {},
   "outputs": [
    {
     "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fc473678",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>1020</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>1023</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>1024</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "<p>1025 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age    sex     cp  trestbps   chol    fbs  restecg  thalach  exang  \\\n",
       "0     False  False  False     False  False  False    False    False  False   \n",
       "1     False  False  False     False  False  False    False    False  False   \n",
       "2     False  False  False     False  False  False    False    False  False   \n",
       "3     False  False  False     False  False  False    False    False  False   \n",
       "4     False  False  False     False  False  False    False    False  False   \n",
       "...     ...    ...    ...       ...    ...    ...      ...      ...    ...   \n",
       "1020  False  False  False     False  False  False    False    False  False   \n",
       "1021  False  False  False     False  False  False    False    False  False   \n",
       "1022  False  False  False     False  False  False    False    False  False   \n",
       "1023  False  False  False     False  False  False    False    False  False   \n",
       "1024  False  False  False     False  False  False    False    False  False   \n",
       "\n",
       "      oldpeak  slope     ca   thal  target  \n",
       "0       False  False  False  False   False  \n",
       "1       False  False  False  False   False  \n",
       "2       False  False  False  False   False  \n",
       "3       False  False  False  False   False  \n",
       "4       False  False  False  False   False  \n",
       "...       ...    ...    ...    ...     ...  \n",
       "1020    False  False  False  False   False  \n",
       "1021    False  False  False  False   False  \n",
       "1022    False  False  False  False   False  \n",
       "1023    False  False  False  False   False  \n",
       "1024    False  False  False  False   False  \n",
       "\n",
       "[1025 rows x 14 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "196f3484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8f832027",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.00000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
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       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.434146</td>\n",
       "      <td>0.695610</td>\n",
       "      <td>0.942439</td>\n",
       "      <td>131.611707</td>\n",
       "      <td>246.00000</td>\n",
       "      <td>0.149268</td>\n",
       "      <td>0.529756</td>\n",
       "      <td>149.114146</td>\n",
       "      <td>0.336585</td>\n",
       "      <td>1.071512</td>\n",
       "      <td>1.385366</td>\n",
       "      <td>0.754146</td>\n",
       "      <td>2.323902</td>\n",
       "      <td>0.513171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.072290</td>\n",
       "      <td>0.460373</td>\n",
       "      <td>1.029641</td>\n",
       "      <td>17.516718</td>\n",
       "      <td>51.59251</td>\n",
       "      <td>0.356527</td>\n",
       "      <td>0.527878</td>\n",
       "      <td>23.005724</td>\n",
       "      <td>0.472772</td>\n",
       "      <td>1.175053</td>\n",
       "      <td>0.617755</td>\n",
       "      <td>1.030798</td>\n",
       "      <td>0.620660</td>\n",
       "      <td>0.500070</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>126.00000</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>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>132.000000</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>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>152.000000</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>275.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.800000</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>564.00000</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  \\\n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.00000   \n",
       "mean     54.434146     0.695610     0.942439   131.611707   246.00000   \n",
       "std       9.072290     0.460373     1.029641    17.516718    51.59251   \n",
       "min      29.000000     0.000000     0.000000    94.000000   126.00000   \n",
       "25%      48.000000     0.000000     0.000000   120.000000   211.00000   \n",
       "50%      56.000000     1.000000     1.000000   130.000000   240.00000   \n",
       "75%      61.000000     1.000000     2.000000   140.000000   275.00000   \n",
       "max      77.000000     1.000000     3.000000   200.000000   564.00000   \n",
       "\n",
       "               fbs      restecg      thalach        exang      oldpeak  \\\n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.000000   \n",
       "mean      0.149268     0.529756   149.114146     0.336585     1.071512   \n",
       "std       0.356527     0.527878    23.005724     0.472772     1.175053   \n",
       "min       0.000000     0.000000    71.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000   132.000000     0.000000     0.000000   \n",
       "50%       0.000000     1.000000   152.000000     0.000000     0.800000   \n",
       "75%       0.000000     1.000000   166.000000     1.000000     1.800000   \n",
       "max       1.000000     2.000000   202.000000     1.000000     6.200000   \n",
       "\n",
       "             slope           ca         thal       target  \n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  \n",
       "mean      1.385366     0.754146     2.323902     0.513171  \n",
       "std       0.617755     1.030798     0.620660     0.500070  \n",
       "min       0.000000     0.000000     0.000000     0.000000  \n",
       "25%       1.000000     0.000000     2.000000     0.000000  \n",
       "50%       1.000000     0.000000     2.000000     1.000000  \n",
       "75%       2.000000     1.000000     3.000000     1.000000  \n",
       "max       2.000000     4.000000     3.000000     1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "400bd999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5027ef8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    526\n",
       "0    499\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f6fe3b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,:-1]\n",
    "y=df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "47884f4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state= 99)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c1698b6",
   "metadata": {},
   "source": [
    "# Descision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e2f80689",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy is:  91.82879377431907\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.88      0.91       128\n",
      "           1       0.89      0.95      0.92       129\n",
      "\n",
      "    accuracy                           0.92       257\n",
      "   macro avg       0.92      0.92      0.92       257\n",
      "weighted avg       0.92      0.92      0.92       257\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "DecisionTree = DecisionTreeClassifier(criterion=\"entropy\",random_state=2,max_depth=5)\n",
    "\n",
    "DecisionTree.fit(X_train,y_train)\n",
    "\n",
    "predicted_values = DecisionTree.predict(X_test)\n",
    "x = metrics.accuracy_score(y_test, predicted_values)\n",
    "print(\"DecisionTrees's Accuracy is: \", x*100)\n",
    "\n",
    "print(classification_report(y_test,predicted_values))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ed9b70",
   "metadata": {},
   "source": [
    "# Guassian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4eb26c1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes's Accuracy is:  0.8210116731517509\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.73      0.80       128\n",
      "           1       0.77      0.91      0.84       129\n",
      "\n",
      "    accuracy                           0.82       257\n",
      "   macro avg       0.83      0.82      0.82       257\n",
      "weighted avg       0.83      0.82      0.82       257\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "NaiveBayes = GaussianNB()\n",
    "\n",
    "NaiveBayes.fit(X_train,y_train)\n",
    "\n",
    "predicted_values = NaiveBayes.predict(X_test)\n",
    "x = metrics.accuracy_score(y_test, predicted_values)\n",
    "\n",
    "print(\"Naive Bayes's Accuracy is: \", x*100)\n",
    "\n",
    "print(classification_report(y_test,predicted_values))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "086a7824",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e10e4cb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM's Accuracy is:  0.9066147859922179\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'Ytest' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[28], line 16\u001b[0m\n\u001b[0;32m     12\u001b[0m x \u001b[38;5;241m=\u001b[39m metrics\u001b[38;5;241m.\u001b[39maccuracy_score(y_test, predicted_values)\n\u001b[0;32m     14\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSVM\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms Accuracy is: \u001b[39m\u001b[38;5;124m\"\u001b[39m, x)\n\u001b[1;32m---> 16\u001b[0m \u001b[38;5;28mprint\u001b[39m(classification_report(Ytest,predicted_values))\n",
      "\u001b[1;31mNameError\u001b[0m: name 'Ytest' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "norm = MinMaxScaler().fit(X_train)\n",
    "X_train_norm = norm.transform(X_train)\n",
    "X_test_norm = norm.transform(X_test)\n",
    "\n",
    "SVM = SVC(kernel='poly', degree=3, C=1)\n",
    "SVM.fit(X_train_norm, y_train)\n",
    "predicted_values = SVM.predict(X_test_norm)\n",
    "accuracy = accuracy_score(y_test, predicted_values)\n",
    "\n",
    "print(\"SVM's Accuracy is: \", accuracy * 100)\n",
    "\n",
    "print(classification_report(y_test, predicted_values))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bb795e5",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "18a512c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier(criterion = 'gini', max_depth = 8, min_samples_split = 10, random_state = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4726ba52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=8, min_samples_split=10, random_state=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=8, min_samples_split=10, random_state=5)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=8, min_samples_split=10, random_state=5)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8bb3ca3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08877226, 0.03775317, 0.1567776 , 0.05804852, 0.05636884,\n",
       "       0.00938654, 0.01518357, 0.08071837, 0.06114553, 0.12830501,\n",
       "       0.05535863, 0.12655215, 0.12562981])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "187874bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fdf69384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
       "       1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0,\n",
       "       1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0,\n",
       "       1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e33a59fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[119,   9],\n",
       "       [  1, 128]], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b7ebf173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9610894941634242"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy : \", accuracy*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "02bbc576",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mayekar\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "print(clf.predict(np.array([200,0,0,125,212,0,1,168,0,1.0,2,2,3]).reshape(-1, 13)))"
   ]
  }
 ],
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