Heart Diseases Identifier .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c97debfb",
   "metadata": {},
   "source": [
    "## 1. Importing the Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "e553634f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "631322fc",
   "metadata": {},
   "source": [
    "## 2. Importing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "d26ad0c1",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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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",
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       "      <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",
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       "      <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": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"D:/Top_mentor/Dec/5th_dec/Assignments/Assignments/Healthcare_Analysis_Project_Capstone1/heart.csv\")\n",
    "## show 5 rows\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f403d255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a8f4b8",
   "metadata": {},
   "source": [
    "## 3. Taking Care of Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ea1f2b5",
   "metadata": {
    "collapsed": true
   },
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()\n",
    "## No nullvalues in data set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b1fb246",
   "metadata": {},
   "source": [
    "## 4. Taking Care of Duplicate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9740372d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a4c5c5a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "203d8854",
   "metadata": {},
   "source": [
    "## 5. Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b6ff16e",
   "metadata": {},
   "outputs": [],
   "source": [
    "cate_val = []\n",
    "cont_val = []\n",
    "\n",
    "for column in data.columns:\n",
    "    if data[column].nunique() <= 10:\n",
    "        cate_val.append(column)\n",
    "    else: \n",
    "        cont_val.append(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd3b03b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "categorical column ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal', 'target']\n"
     ]
    }
   ],
   "source": [
    "print('categorical column',cate_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "969d9829",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical column ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']\n"
     ]
    }
   ],
   "source": [
    "print('Numerical column',cont_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b35d0bf",
   "metadata": {},
   "source": [
    "## 6. Encoding Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf3e21bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['cp'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea0165a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Both columns rae removed due to contain 0 and 1 value, not required encoding \n",
    "cate_val.remove('sex')\n",
    "cate_val.remove('target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a10c0a7",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>sex</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>thalach</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>target</th>\n",
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       "      <th>cp_2</th>\n",
       "      <th>cp_3</th>\n",
       "      <th>...</th>\n",
       "      <th>exang_1</th>\n",
       "      <th>slope_1</th>\n",
       "      <th>slope_2</th>\n",
       "      <th>ca_1</th>\n",
       "      <th>ca_2</th>\n",
       "      <th>ca_3</th>\n",
       "      <th>ca_4</th>\n",
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       "      <td>0</td>\n",
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       "      <td>3.5</td>\n",
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       "      <td>204</td>\n",
       "      <td>172</td>\n",
       "      <td>1.4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>178</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>163</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  trestbps  chol  thalach  oldpeak  target  cp_1  cp_2  cp_3  ...  \\\n",
       "0   63    1       145   233      150      2.3       1     0     0     1  ...   \n",
       "1   37    1       130   250      187      3.5       1     0     1     0  ...   \n",
       "2   41    0       130   204      172      1.4       1     1     0     0  ...   \n",
       "3   56    1       120   236      178      0.8       1     1     0     0  ...   \n",
       "4   57    0       120   354      163      0.6       1     0     0     0  ...   \n",
       "\n",
       "   exang_1  slope_1  slope_2  ca_1  ca_2  ca_3  ca_4  thal_1  thal_2  thal_3  \n",
       "0        0        0        0     0     0     0     0       1       0       0  \n",
       "1        0        0        0     0     0     0     0       0       1       0  \n",
       "2        0        0        1     0     0     0     0       0       1       0  \n",
       "3        0        0        1     0     0     0     0       0       1       0  \n",
       "4        1        0        1     0     0     0     0       0       1       0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## encode by dumy variables \n",
    "data = pd.get_dummies(data,columns = cate_val, drop_first= True ) ## drop_first= True  use to drop main columns\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b05300",
   "metadata": {},
   "source": [
    "## 7. Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e39e9d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a4dbe072",
   "metadata": {
    "collapsed": true
   },
   "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>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>thalach</th>\n",
       "      <th>oldpeak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.949794</td>\n",
       "      <td>0.764066</td>\n",
       "      <td>-0.261285</td>\n",
       "      <td>0.018826</td>\n",
       "      <td>1.084022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.928548</td>\n",
       "      <td>-0.091401</td>\n",
       "      <td>0.067741</td>\n",
       "      <td>1.636979</td>\n",
       "      <td>2.118926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.485726</td>\n",
       "      <td>-0.091401</td>\n",
       "      <td>-0.822564</td>\n",
       "      <td>0.980971</td>\n",
       "      <td>0.307844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.174856</td>\n",
       "      <td>-0.661712</td>\n",
       "      <td>-0.203222</td>\n",
       "      <td>1.243374</td>\n",
       "      <td>-0.209608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.285561</td>\n",
       "      <td>-0.661712</td>\n",
       "      <td>2.080602</td>\n",
       "      <td>0.587366</td>\n",
       "      <td>-0.382092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>0.285561</td>\n",
       "      <td>0.478910</td>\n",
       "      <td>-0.106449</td>\n",
       "      <td>-1.161988</td>\n",
       "      <td>-0.727060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>-1.042904</td>\n",
       "      <td>-1.232023</td>\n",
       "      <td>0.338703</td>\n",
       "      <td>-0.768384</td>\n",
       "      <td>0.135360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>1.503322</td>\n",
       "      <td>0.707035</td>\n",
       "      <td>-1.035462</td>\n",
       "      <td>-0.374779</td>\n",
       "      <td>2.032684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>0.285561</td>\n",
       "      <td>-0.091401</td>\n",
       "      <td>-2.235438</td>\n",
       "      <td>-1.511859</td>\n",
       "      <td>0.135360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>0.285561</td>\n",
       "      <td>-0.091401</td>\n",
       "      <td>-0.203222</td>\n",
       "      <td>1.068439</td>\n",
       "      <td>-0.899544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>302 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  trestbps      chol   thalach   oldpeak\n",
       "0    0.949794  0.764066 -0.261285  0.018826  1.084022\n",
       "1   -1.928548 -0.091401  0.067741  1.636979  2.118926\n",
       "2   -1.485726 -0.091401 -0.822564  0.980971  0.307844\n",
       "3    0.174856 -0.661712 -0.203222  1.243374 -0.209608\n",
       "4    0.285561 -0.661712  2.080602  0.587366 -0.382092\n",
       "..        ...       ...       ...       ...       ...\n",
       "298  0.285561  0.478910 -0.106449 -1.161988 -0.727060\n",
       "299 -1.042904 -1.232023  0.338703 -0.768384  0.135360\n",
       "300  1.503322  0.707035 -1.035462 -0.374779  2.032684\n",
       "301  0.285561 -0.091401 -2.235438 -1.511859  0.135360\n",
       "302  0.285561 -0.091401 -0.203222  1.068439 -0.899544\n",
       "\n",
       "[302 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st = StandardScaler()\n",
    "data[cont_val] = st.fit_transform(data[cont_val])\n",
    "data[cont_val]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccea688f",
   "metadata": {},
   "source": [
    "## 8. Splitting The Dataset Into The Training Set And Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "88a1f2ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.drop('target',axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "14c2b373",
   "metadata": {},
   "outputs": [],
   "source": [
    "y= data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "85b8760a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "59d079d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2, random_state=1254)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d892e9be",
   "metadata": {},
   "source": [
    "## 9. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "035997cb",
   "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>LogisticRegression()</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1b51b32e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Model Accuracy 0.8688524590163934\n"
     ]
    }
   ],
   "source": [
    "## Prediction\n",
    "log_pred = log.predict(X_test)\n",
    "\n",
    "## Accuuracy check \n",
    "from sklearn.metrics import accuracy_score\n",
    "log_accuracy = accuracy_score(y_test,log_pred)\n",
    "print('Logistic Regression Model Accuracy', log_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9f1206f",
   "metadata": {},
   "source": [
    "## 10. SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aa12ddda",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "clf = svm.SVC() ## Support vector classifier\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1f67cf42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVCs Model Accuracy 0.819672131147541\n"
     ]
    }
   ],
   "source": [
    "svm_pred = clf.predict(X_test)\n",
    "svc_accuracy = accuracy_score(y_test,svm_pred)\n",
    "print('SVCs Model Accuracy', svc_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7cdc284",
   "metadata": {},
   "source": [
    "## 11. Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f84c8e6f",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[20  7]\n",
      " [ 5 29]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.74      0.77        27\n",
      "           1       0.81      0.85      0.83        34\n",
      "\n",
      "    accuracy                           0.80        61\n",
      "   macro avg       0.80      0.80      0.80        61\n",
      "weighted avg       0.80      0.80      0.80        61\n",
      "\n",
      "Accuracy score of Decision Tree model= 0.8032786885245902\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import metrics\n",
    "\n",
    "## Model fitting\n",
    "dt = DecisionTreeClassifier(criterion=\"entropy\")\n",
    "dt.fit(X_train, y_train)\n",
    "dt_pred = dt.predict(X_test)\n",
    "\n",
    "## Accuracy \n",
    "print(metrics.confusion_matrix(y_test, dt_pred))\n",
    "print(classification_report(y_test, dt_pred))\n",
    "dt_accuracy = accuracy_score(y_test, dt_pred)\n",
    "print('Accuracy score of Decision Tree model=',dt_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c14191d",
   "metadata": {},
   "source": [
    "## 12. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "573083f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of RandomForest model= 0.8360655737704918\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "regressor = RandomForestClassifier()\n",
    "regressor.fit(X_train,y_train)\n",
    "\n",
    "rf_pred = regressor.predict(X_test)\n",
    "rfc_accuracy = accuracy_score(y_test, rf_pred)\n",
    "print('Accuracy score of RandomForest model=',rfc_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "927e1194",
   "metadata": {},
   "source": [
    "## 13. Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8c82330d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "46bcd8d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "gbc = GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5af72eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5b4ff6e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "gbc_pred = gbc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5f693670",
   "metadata": {},
   "outputs": [],
   "source": [
    "gbc_accuracy = accuracy_score(y_test,gbc_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "381be492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of Gradient Boosting Classifier model= 0.8360655737704918\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy score of Gradient Boosting Classifier model=',gbc_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb6df3a5",
   "metadata": {},
   "source": [
    "## 14. Guassian Naive_bayes model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5cbef440",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f32abeb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 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-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 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-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 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-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 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-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</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-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "df7a833b",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb_pred = gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d4c214ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[21,  6],\n",
       "       [ 3, 31]], dtype=int64)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, gnb_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "21e2b444",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb_accuracy =(21+31)/(21+31+3+6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a27549e8",
   "metadata": {},
   "source": [
    "## 15. Accuracy identification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "734b0151",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_score = pd.DataFrame({'Models':['LR', 'SVM','DT', 'RF','GB','GNB'],'Accuracy': [accuracy_score(y_test,log_pred),accuracy_score(y_test,svm_pred),accuracy_score(y_test, dt_pred),accuracy_score(y_test, rf_pred),accuracy_score(y_test,gbc_pred),gnb_accuracy]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "9fe8e2fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Models</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.868852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.819672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.803279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.836066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GB</td>\n",
       "      <td>0.836066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GNB</td>\n",
       "      <td>0.852459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Models  Accuracy\n",
       "0     LR  0.868852\n",
       "1    SVM  0.819672\n",
       "2     DT  0.803279\n",
       "3     RF  0.836066\n",
       "4     GB  0.836066\n",
       "5    GNB  0.852459"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "53ae3635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Models', ylabel='Accuracy'>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns \n",
    "sns.barplot(x=df_score['Models'],y= df_score['Accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f4cfc7",
   "metadata": {},
   "source": [
    "## 16. Choose best model for prediction \n",
    "1. According to diffrent model LogisticRegression model has provide higher accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "7f8880e7",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Acer\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=data.drop('target',axis= 1)\n",
    "y= data['target']\n",
    "log = LogisticRegression()\n",
    "log.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f443d382",
   "metadata": {},
   "source": [
    "## 17. Prediction on New Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "c12718f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create new data set for prediction \n",
    "new_data =pd.DataFrame({\n",
    "    'age': 65, \n",
    "    'sex':0,\n",
    "    'cp': 0,\n",
    "    'trestbps':125,\n",
    "    'chol':212, \n",
    "    'fbs':0,\n",
    "    'restecg':1,\n",
    "    'thalach':168,\n",
    "    'exang':0,\n",
    "    'oldpeak':1.0,\n",
    "    'slope':2,\n",
    "    'ca':2,\n",
    "    'thal':3,\n",
    "},index=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "68a8646f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Possibility of Heart Disease\n"
     ]
    }
   ],
   "source": [
    "## Predict \n",
    "new_pred = log.predict(new_data)\n",
    "if new_pred[0]==0:\n",
    "    print('No Disease detected')\n",
    "else:\n",
    "    print(\"Possibility of Heart Disease\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66040b94",
   "metadata": {},
   "source": [
    "## 18. Save Model Usign Joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "769662ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "3b7d319a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Heart_Dieases_Predition_Model']"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(log,'Heart_Dieases_Predition_Model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "b2e82268",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load model \n",
    "model_heart = joblib.load('Heart_Dieases_Predition_Model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "1d62e9ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Possibility of Heart Disease\n"
     ]
    }
   ],
   "source": [
    "## Predict again after loading model \n",
    "model_heart.predict(new_data)\n",
    "if new_pred[0]==0:\n",
    "    print('No Disease detected')\n",
    "else:\n",
    "    print(\"Possibility of Heart Disease\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc979920",
   "metadata": {},
   "source": [
    "## 19. Creating GUI (Graphical User Interface)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "1852eda4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter import *\n",
    "import joblib\n",
    "import numpy as np\n",
    "from sklearn import *\n",
    "from PIL import ImageTk, Image\n",
    "\n",
    "\n",
    "\n",
    "def show_entry_fields():\n",
    "    p1=int(e1.get())\n",
    "    p2=int(e2.get())\n",
    "    p3=int(e3.get())\n",
    "    p4=int(e4.get())\n",
    "    p5=int(e5.get())\n",
    "    p6=int(e6.get())\n",
    "    p7=int(e7.get())\n",
    "    p8=int(e8.get())\n",
    "    p9=int(e9.get())\n",
    "    p10=float(e10.get())\n",
    "    p11=int(e11.get())\n",
    "    p12=int(e12.get())\n",
    "    p13=int(e13.get())\n",
    "    model = joblib.load('Heart_Dieases_Predition_Model')\n",
    "    result=model.predict([[p1,p2,p3,p4,p5,p6,p7,p8,p8,p10,p11,p12,p13]])\n",
    "    \n",
    "    if result == 0:\n",
    "        Label(master, text=\"No Heart Disease\").grid(row=31)\n",
    "    else:\n",
    "        Label(master, text=\"Possibility of Heart Disease\").grid(row=31)\n",
    "    \n",
    "    \n",
    "master = Tk()\n",
    "master.title(\"Heart Disease Prediction System\")\n",
    "\n",
    "\n",
    "label = Label(master, text = \"Heart Disease Prediction System\"\n",
    "                          , bg = \"black\", fg = \"white\"). \\\n",
    "                               grid(row=0,columnspan=2)\n",
    "\n",
    "Label(master, text=\"Enter Your Age\").grid(row=1)\n",
    "Label(master, text=\"Male Or Female [1/0]\").grid(row=2)\n",
    "Label(master, text=\"Enter Value of CP\").grid(row=3)\n",
    "Label(master, text=\"Enter Value of trestbps\").grid(row=4)\n",
    "Label(master, text=\"Enter Value of chol\").grid(row=5)\n",
    "Label(master, text=\"Enter Value of fbs\").grid(row=6)\n",
    "Label(master, text=\"Enter Value of restecg\").grid(row=7)\n",
    "Label(master, text=\"Enter Value of thalach\").grid(row=8)\n",
    "Label(master, text=\"Enter Value of exang\").grid(row=9)\n",
    "Label(master, text=\"Enter Value of oldpeak\").grid(row=10)\n",
    "Label(master, text=\"Enter Value of slope\").grid(row=11)\n",
    "Label(master, text=\"Enter Value of ca\").grid(row=12)\n",
    "Label(master, text=\"Enter Value of thal\").grid(row=13)\n",
    "\n",
    "\n",
    "e1 = Entry(master)\n",
    "e2 = Entry(master)\n",
    "e3 = Entry(master)\n",
    "e4 = Entry(master)\n",
    "e5 = Entry(master)\n",
    "e6 = Entry(master)\n",
    "e7 = Entry(master)\n",
    "e8 = Entry(master)\n",
    "e9 = Entry(master)\n",
    "e10 = Entry(master)\n",
    "e11 = Entry(master)\n",
    "e12 = Entry(master)\n",
    "e13 = Entry(master)\n",
    "\n",
    "e1.grid(row=1, column=1)\n",
    "e2.grid(row=2, column=1)\n",
    "e3.grid(row=3, column=1)\n",
    "e4.grid(row=4, column=1)\n",
    "e5.grid(row=5, column=1)\n",
    "e6.grid(row=6, column=1)\n",
    "e7.grid(row=7, column=1)\n",
    "e8.grid(row=8, column=1)\n",
    "e9.grid(row=9, column=1)\n",
    "e10.grid(row=10, column=1)\n",
    "e11.grid(row=11, column=1)\n",
    "e12.grid(row=12, column=1)\n",
    "e13.grid(row=13, column=1)\n",
    "\n",
    "\n",
    "Button(master, text='Predict', command=show_entry_fields).grid()\n",
    "\n",
    "mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ddcaf01",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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