diff --git "a/notebooks/heart disease prediction.ipynb" "b/notebooks/heart disease prediction.ipynb"
new file mode 100644--- /dev/null
+++ "b/notebooks/heart disease prediction.ipynb"
@@ -0,0 +1,2036 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1f41db61-4f75-4696-84a8-2a906dca3e3e",
+   "metadata": {},
+   "source": [
+    "# Heart Failure analysis and detection using Machine Learning techniques\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "b7897612-92c7-41bc-99ed-0f19a23d16ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import the libraries\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import classification_report\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import ConfusionMatrixDisplay, accuracy_score, classification_report, confusion_matrix, RocCurveDisplay\n",
+    "from sklearn import metrics\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d59970e5-184f-4b1e-a269-71223a49dba9",
+   "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>Age</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>ChestPainType</th>\n",
+       "      <th>RestingBP</th>\n",
+       "      <th>Cholesterol</th>\n",
+       "      <th>FastingBS</th>\n",
+       "      <th>RestingECG</th>\n",
+       "      <th>MaxHR</th>\n",
+       "      <th>ExerciseAngina</th>\n",
+       "      <th>Oldpeak</th>\n",
+       "      <th>ST_Slope</th>\n",
+       "      <th>HeartDisease</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>40</td>\n",
+       "      <td>M</td>\n",
+       "      <td>ATA</td>\n",
+       "      <td>140</td>\n",
+       "      <td>289</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>172</td>\n",
+       "      <td>N</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Up</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>49</td>\n",
+       "      <td>F</td>\n",
+       "      <td>NAP</td>\n",
+       "      <td>160</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>156</td>\n",
+       "      <td>N</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Flat</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>37</td>\n",
+       "      <td>M</td>\n",
+       "      <td>ATA</td>\n",
+       "      <td>130</td>\n",
+       "      <td>283</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ST</td>\n",
+       "      <td>98</td>\n",
+       "      <td>N</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Up</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>48</td>\n",
+       "      <td>F</td>\n",
+       "      <td>ASY</td>\n",
+       "      <td>138</td>\n",
+       "      <td>214</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>108</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>Flat</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>54</td>\n",
+       "      <td>M</td>\n",
+       "      <td>NAP</td>\n",
+       "      <td>150</td>\n",
+       "      <td>195</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>122</td>\n",
+       "      <td>N</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Up</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
+       "0   40   M           ATA        140          289          0     Normal    172   \n",
+       "1   49   F           NAP        160          180          0     Normal    156   \n",
+       "2   37   M           ATA        130          283          0         ST     98   \n",
+       "3   48   F           ASY        138          214          0     Normal    108   \n",
+       "4   54   M           NAP        150          195          0     Normal    122   \n",
+       "\n",
+       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
+       "0              N      0.0       Up             0  \n",
+       "1              N      1.0     Flat             1  \n",
+       "2              N      0.0       Up             0  \n",
+       "3              Y      1.5     Flat             1  \n",
+       "4              N      0.0       Up             0  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Read the data\n",
+    "data = pd.read_csv(\"heart.csv\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "992daf53-ba71-44ee-93f9-bf9f74af90fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 918 entries, 0 to 917\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column          Non-Null Count  Dtype  \n",
+      "---  ------          --------------  -----  \n",
+      " 0   Age             918 non-null    int64  \n",
+      " 1   Sex             918 non-null    object \n",
+      " 2   ChestPainType   918 non-null    object \n",
+      " 3   RestingBP       918 non-null    int64  \n",
+      " 4   Cholesterol     918 non-null    int64  \n",
+      " 5   FastingBS       918 non-null    int64  \n",
+      " 6   RestingECG      918 non-null    object \n",
+      " 7   MaxHR           918 non-null    int64  \n",
+      " 8   ExerciseAngina  918 non-null    object \n",
+      " 9   Oldpeak         918 non-null    float64\n",
+      " 10  ST_Slope        918 non-null    object \n",
+      " 11  HeartDisease    918 non-null    int64  \n",
+      "dtypes: float64(1), int64(6), object(5)\n",
+      "memory usage: 86.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Basic information about the dataset\n",
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "df134866-a405-434d-8603-4cefd3181e4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(918, 12)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "effd23f1-8bc0-42a8-9dc4-1f1c9f4c3b6f",
+   "metadata": {},
+   "source": [
+    "There are 918 rows and 12 columns\r\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f9b33bee-56a3-4448-ab53-9132732929c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11016"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ce154df8-9364-4988-b840-a8cd3d493c21",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age               0\n",
+       "Sex               0\n",
+       "ChestPainType     0\n",
+       "RestingBP         0\n",
+       "Cholesterol       0\n",
+       "FastingBS         0\n",
+       "RestingECG        0\n",
+       "MaxHR             0\n",
+       "ExerciseAngina    0\n",
+       "Oldpeak           0\n",
+       "ST_Slope          0\n",
+       "HeartDisease      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5c13c288-af55-498c-a5bb-edf9d289b569",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1600x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### visualize in the heatmap\n",
+    "plt.figure(figsize = (16,8))\n",
+    "sns.heatmap(data.isna(),cmap='BrBG')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "88eee4d5-03fa-4bee-bd8e-7233d215499b",
+   "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>Age</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>ChestPainType</th>\n",
+       "      <th>RestingBP</th>\n",
+       "      <th>Cholesterol</th>\n",
+       "      <th>FastingBS</th>\n",
+       "      <th>RestingECG</th>\n",
+       "      <th>MaxHR</th>\n",
+       "      <th>ExerciseAngina</th>\n",
+       "      <th>Oldpeak</th>\n",
+       "      <th>ST_Slope</th>\n",
+       "      <th>HeartDisease</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>40</td>\n",
+       "      <td>M</td>\n",
+       "      <td>ATA</td>\n",
+       "      <td>140</td>\n",
+       "      <td>289</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>172</td>\n",
+       "      <td>N</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Up</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>49</td>\n",
+       "      <td>F</td>\n",
+       "      <td>NAP</td>\n",
+       "      <td>160</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>156</td>\n",
+       "      <td>N</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Flat</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>37</td>\n",
+       "      <td>M</td>\n",
+       "      <td>ATA</td>\n",
+       "      <td>130</td>\n",
+       "      <td>283</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ST</td>\n",
+       "      <td>98</td>\n",
+       "      <td>N</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Up</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>48</td>\n",
+       "      <td>F</td>\n",
+       "      <td>ASY</td>\n",
+       "      <td>138</td>\n",
+       "      <td>214</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>108</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>Flat</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>54</td>\n",
+       "      <td>M</td>\n",
+       "      <td>NAP</td>\n",
+       "      <td>150</td>\n",
+       "      <td>195</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>122</td>\n",
+       "      <td>N</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Up</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
+       "0   40   M           ATA        140          289          0     Normal    172   \n",
+       "1   49   F           NAP        160          180          0     Normal    156   \n",
+       "2   37   M           ATA        130          283          0         ST     98   \n",
+       "3   48   F           ASY        138          214          0     Normal    108   \n",
+       "4   54   M           NAP        150          195          0     Normal    122   \n",
+       "\n",
+       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
+       "0              N      0.0       Up             0  \n",
+       "1              N      1.0     Flat             1  \n",
+       "2              N      0.0       Up             0  \n",
+       "3              Y      1.5     Flat             1  \n",
+       "4              N      0.0       Up             0  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "677f1259-f567-4629-8924-a15277bdefed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e107b348-5817-42d9-bbb6-5c4c5afcffe3",
+   "metadata": {},
+   "source": [
+    "## Data analysis\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "66174ff8-73c5-4a6c-bb61-4cd7beb1f308",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sex\n",
+       "M    725\n",
+       "F    193\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['Sex'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0d25c72c-aaf6-482f-b703-21c9ddffcd4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Sex', ylabel='count'>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (16,8))\n",
+    "sns.countplot(data = data, x = 'Sex')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7310d0a7-c361-4214-a3e5-708d934cfccb",
+   "metadata": {},
+   "source": [
+    "## According to this dataset, which gender suffered more from heart disease?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4b019dff-6704-4df9-9fd3-7786dc9d95c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sex\n",
+       "F    143\n",
+       "M    267\n",
+       "Name: HeartDisease, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tot = data['HeartDisease'].groupby(data['Sex']).count()\n",
+    "suff = data['HeartDisease'].groupby(data['Sex']).sum()\n",
+    "not_suf = tot-suff\n",
+    "not_suf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "26ef5d40-ac18-4ef8-b2c0-20f1aa154562",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='HeartDisease', ylabel='count'>"
+      ]
+     },
+     "execution_count": 14,
+     "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": [
+    "sns.countplot(data = data, x = not_suf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "9ba7095a-b22a-4db7-9996-80adf8f096eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='HeartDisease', ylabel='count'>"
+      ]
+     },
+     "execution_count": 15,
+     "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": [
+    "sns.countplot(data = data, x = suff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6f5b5e9c-ffcd-4a3d-b3c1-750c101da277",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\prajw\\AppData\\Local\\Temp\\ipykernel_9964\\3227672255.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "  female_suffered = suff[0]\n",
+      "C:\\Users\\prajw\\AppData\\Local\\Temp\\ipykernel_9964\\3227672255.py:2: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "  male_suffered = suff[1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "female_suffered = suff[0]\n",
+    "male_suffered = suff[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1b51947d-0785-4f5b-bc9f-de56cedbb420",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Female suffered percent  5.446623093681917\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_gend = data['Sex'].count()\n",
+    "print(\"Female suffered percent \", (female_suffered/total_gend)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c440e9cc-3288-45e7-afbb-a12c88d40018",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Male suffered percent  49.89106753812636\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_gend = data['Sex'].count()\n",
+    "print(\"Male suffered percent \", (male_suffered/total_gend)*100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a15fa1e-b7f6-4b8d-bd01-9325972c5e8b",
+   "metadata": {},
+   "source": [
+    "## Analyze the chest pain type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "877fec62-c514-469f-a0e0-072037f7d0f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ChestPainType\n",
+       "ASY    496\n",
+       "NAP    203\n",
+       "ATA    173\n",
+       "TA      46\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['ChestPainType'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "49a43505-823a-4f83-a731-a71b7ccc75b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='ChestPainType', ylabel='count'>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (16,8))\n",
+    "sns.countplot(data=data, x= 'ChestPainType')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "d3a464a2-e2ac-49a0-9cfe-13d4bb180afe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_me = data['HeartDisease'].groupby(data['ChestPainType']).count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "89559fe8-36e9-47be-8b3b-b9001e2f6a2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sum_me = data['HeartDisease'].groupby(data['ChestPainType']).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b46f0a50-7b75-4fe0-ae83-f8ad5c16d5cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ChestPainType\n",
+       "ASY    392\n",
+       "ATA     24\n",
+       "NAP     72\n",
+       "TA      20\n",
+       "Name: HeartDisease, dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum_me"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "e05b646e-1ce1-45c3-9d68-b9170ca01547",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "no_risk = count_me-sum_me"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "a59db56a-b94f-40d2-8189-8e0412176a9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "392 of people who have ASY got heart disease and 104 of people who have ASY doesn't get heart disease.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\prajw\\AppData\\Local\\Temp\\ipykernel_9964\\2156176294.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "  print(\"{} of people who have ASY got heart disease and {} of people who have ASY doesn't get heart disease.\".format(sum_me[0],no_risk[0]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"{} of people who have ASY got heart disease and {} of people who have ASY doesn't get heart disease.\".format(sum_me[0],no_risk[0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ba4f0a8f-de32-43b5-8794-5aa477bdb2ba",
+   "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>Age</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>ChestPainType</th>\n",
+       "      <th>RestingBP</th>\n",
+       "      <th>Cholesterol</th>\n",
+       "      <th>FastingBS</th>\n",
+       "      <th>RestingECG</th>\n",
+       "      <th>MaxHR</th>\n",
+       "      <th>ExerciseAngina</th>\n",
+       "      <th>Oldpeak</th>\n",
+       "      <th>ST_Slope</th>\n",
+       "      <th>HeartDisease</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>40</td>\n",
+       "      <td>M</td>\n",
+       "      <td>ATA</td>\n",
+       "      <td>140</td>\n",
+       "      <td>289</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>172</td>\n",
+       "      <td>N</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Up</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>49</td>\n",
+       "      <td>F</td>\n",
+       "      <td>NAP</td>\n",
+       "      <td>160</td>\n",
+       "      <td>180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>156</td>\n",
+       "      <td>N</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Flat</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>37</td>\n",
+       "      <td>M</td>\n",
+       "      <td>ATA</td>\n",
+       "      <td>130</td>\n",
+       "      <td>283</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ST</td>\n",
+       "      <td>98</td>\n",
+       "      <td>N</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Up</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>48</td>\n",
+       "      <td>F</td>\n",
+       "      <td>ASY</td>\n",
+       "      <td>138</td>\n",
+       "      <td>214</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>108</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>Flat</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>54</td>\n",
+       "      <td>M</td>\n",
+       "      <td>NAP</td>\n",
+       "      <td>150</td>\n",
+       "      <td>195</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>122</td>\n",
+       "      <td>N</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Up</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
+       "0   40   M           ATA        140          289          0     Normal    172   \n",
+       "1   49   F           NAP        160          180          0     Normal    156   \n",
+       "2   37   M           ATA        130          283          0         ST     98   \n",
+       "3   48   F           ASY        138          214          0     Normal    108   \n",
+       "4   54   M           NAP        150          195          0     Normal    122   \n",
+       "\n",
+       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
+       "0              N      0.0       Up             0  \n",
+       "1              N      1.0     Flat             1  \n",
+       "2              N      0.0       Up             0  \n",
+       "3              Y      1.5     Flat             1  \n",
+       "4              N      0.0       Up             0  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d053d6e-60d0-4976-b713-2df3f6034a81",
+   "metadata": {},
+   "source": [
+    "## Relation between RestingECG and the HeartDisease\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "beb409a5-332e-4b66-80ed-b16cf13a61bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RestingECG\n",
+       "Normal    552\n",
+       "LVH       188\n",
+       "ST        178\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"RestingECG\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "5be6b2df-af7e-4ee9-9d8e-1d98a90915a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([552.,   0.,   0.,   0.,   0., 178.,   0.,   0.,   0., 188.]),\n",
+       " array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (16,8))\n",
+    "plt.hist(data[\"RestingECG\"],color='green')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77794355-8c15-4af6-a513-6f5f5c09bdbe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc8675d2-03b7-49b7-a80d-99ce2f0ee43e",
+   "metadata": {},
+   "source": [
+    "## Unique values to aid backend\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8db5291-fdb5-4520-96c5-6aa4552bfb56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = data[\"Sex\"].unique()\n",
+    "c = data[\"ChestPainType\"].unique()\n",
+    "r = data[\"RestingECG\"].unique()\n",
+    "e = data[\"ExerciseAngina\"].unique()\n",
+    "sl = data[\"ST_Slope\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4fb5831f-8d53-4426-b84a-7eb3aa36b6ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(s)\n",
+    "print(c)\n",
+    "print(r)\n",
+    "print(e)\n",
+    "print(sl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f015da99-69f7-4195-9917-710d6f7fe634",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4b35ad8-003a-42c3-a75b-c1ec01784ea2",
+   "metadata": {},
+   "source": [
+    "## Label Encoding\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "fefab506-4a13-4640-aa27-9ee0971a849a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labelencoder = LabelEncoder()\n",
+    "data[\"Sex\"] = labelencoder.fit_transform(data[\"Sex\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "6e8dcec9-7c3d-4e80-a4c3-9d4e81dca8a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"ChestPainType\"] = labelencoder.fit_transform(data[\"ChestPainType\"])\n",
+    "data[\"RestingECG\"] = labelencoder.fit_transform(data[\"RestingECG\"])\n",
+    "data[\"ExerciseAngina\"] = labelencoder.fit_transform(data[\"ExerciseAngina\"])\n",
+    "data[\"ST_Slope\"] = labelencoder.fit_transform(data[\"ST_Slope\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "08d1ae90-5b55-4e82-acb4-c5ee40bc9b77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = data[\"Sex\"].unique()\n",
+    "c = data[\"ChestPainType\"].unique()\n",
+    "r = data[\"RestingECG\"].unique()\n",
+    "e = data[\"ExerciseAngina\"].unique()\n",
+    "sl = data[\"ST_Slope\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "60195969-f008-430e-a0cc-08ddcfbcc382",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 0]\n",
+      "[1 2 0 3]\n",
+      "[1 2 0]\n",
+      "[0 1]\n",
+      "[2 1 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s)\n",
+    "print(c)\n",
+    "print(r)\n",
+    "print(e)\n",
+    "print(sl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f253193-4257-45f9-ab18-0d23978847f5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "23306642-b970-4271-bc46-484b4f286be6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([40, 49, 37, 48, 54, 39, 45, 58, 42, 38, 43, 60, 36, 44, 53, 52, 51,\n",
+       "       56, 41, 32, 65, 35, 59, 50, 47, 31, 46, 57, 55, 63, 66, 34, 33, 61,\n",
+       "       29, 62, 28, 30, 74, 68, 72, 64, 69, 67, 73, 70, 77, 75, 76, 71],\n",
+       "      dtype=int64)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#label = data[\"HeartDisease\"].copy()\n",
+    "\n",
+    "data[\"Age\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "507b778b-2cea-4e41-af16-2a4cadf41fac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 35,
+     "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": [
+    "sns.boxplot(data[\"RestingBP\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "6f549d13-c001-4e9f-b08a-f6ca16225a23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 36,
+     "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": [
+    "sns.boxplot(data[\"Cholesterol\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "e18b333b-6651-48f1-9ec8-b18d44e923f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 37,
+     "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": [
+    "sns.boxplot(data[\"MaxHR\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "0a92e979-1c5c-4cf1-991b-5f89c9082a69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age               13.00\n",
+       "Sex                0.00\n",
+       "ChestPainType      2.00\n",
+       "RestingBP         20.00\n",
+       "Cholesterol       93.75\n",
+       "FastingBS          0.00\n",
+       "RestingECG         0.00\n",
+       "MaxHR             36.00\n",
+       "ExerciseAngina     1.00\n",
+       "Oldpeak            1.50\n",
+       "ST_Slope           1.00\n",
+       "HeartDisease       1.00\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#### Remove the outlier\n",
+    "Q1 = data.quantile(0.25)\n",
+    "Q3 = data.quantile(0.75)\n",
+    "IQR = Q3-Q1\n",
+    "IQR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "ecc614ef-39c9-43f3-bc46-8b1763721b20",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(278, 12)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = data[~((data < (Q1 - 1.5 * IQR)) | (data > (Q3 + 1.5 * IQR))).any(axis = 1)]\n",
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "b6e0e66c-3014-4d13-a7b8-e29eca3c03f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 41,
+     "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": [
+    "sns.boxplot(data[\"RestingBP\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "d5482366-f3d5-4c7a-b3e0-01daaf022efc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label = data[\"HeartDisease\"].copy()\n",
+    "data = data.drop(\"HeartDisease\",axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b54c2525-5e46-4dfb-9eea-07186ec88519",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35e25dfd-4b17-4049-8bd8-d9db61c43574",
+   "metadata": {},
+   "source": [
+    "## Train Test Split\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "e68d6405-3820-4eb6-bb2b-8b0b7cf5b9a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(data, label, test_size = 0.2, random_state = 42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c117be1c-dc30-4ea8-b48b-fd5f25fdd4ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scale = StandardScaler()\n",
+    "X_train = scale.fit_transform(X_train)\n",
+    "X_test = scale.fit_transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "7eb74d73-76ea-4225-a245-30245b9e2217",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.84      0.90      0.87        29\n",
+      "           1       0.88      0.81      0.85        27\n",
+      "\n",
+      "    accuracy                           0.86        56\n",
+      "   macro avg       0.86      0.86      0.86        56\n",
+      "weighted avg       0.86      0.86      0.86        56\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\prajw\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: 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"
+     ]
+    }
+   ],
+   "source": [
+    "lor = LogisticRegression()\n",
+    "lor.fit(X_train, y_train)\n",
+    "y_pred = lor.predict(X_test)\n",
+    "print(classification_report(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64875722-03a3-42a5-a0c6-0efe349442df",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "75a96873-9c98-48a1-9c22-b2a9c52a688a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1e424a99610>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm = confusion_matrix(y_test, y_pred, labels=lor.classes_)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=lor.classes_)\n",
+    "disp.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "5bf01287-c2cc-49a5-afe0-dc48a6ccf9bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy of Logistic Regression model is 85.71428571428571\n"
+     ]
+    }
+   ],
+   "source": [
+    "#printing the accuracy for test set\n",
+    "print('Accuracy of Logistic Regression model is {}'.format(accuracy_score(y_test,y_pred)*100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "67ffcb57-1ecc-4898-9dd3-7b2684b4109f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display = RocCurveDisplay.from_estimator(lor, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33af49d3-3daf-4b9a-95fd-c1ded3c2c037",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26c8a6dd-7d3e-4165-b802-013a646bb851",
+   "metadata": {},
+   "source": [
+    "## Decision Tree\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "609d8e9c-b92e-4d7d-ac40-f8edca2020ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mdl = DecisionTreeClassifier(criterion=\"entropy\", max_depth=6)\n",
+    "mdl.fit(X_train,y_train)\n",
+    "y_p = mdl.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "cda9a8e7-83f6-43c7-841e-ce6f85d92d84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.76      0.76      0.76        29\n",
+      "           1       0.74      0.74      0.74        27\n",
+      "\n",
+      "    accuracy                           0.75        56\n",
+      "   macro avg       0.75      0.75      0.75        56\n",
+      "weighted avg       0.75      0.75      0.75        56\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test, y_p))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "7e85f163-fb7d-4fef-bc5e-cf27645dba8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1e424cbff50>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm = confusion_matrix(y_test, y_p, labels=mdl.classes_)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=mdl.classes_)\n",
+    "disp.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "30867fc9-15c0-4513-9f31-5c11c2aa0518",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy of Decision Tree model is 75.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#printing the accuracy for test set\n",
+    "print('Accuracy of Decision Tree model is {}'.format(accuracy_score(y_test,y_p)*100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "8ca87bb0-5907-4b65-8bf1-10449cb933ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display = RocCurveDisplay.from_estimator(mdl, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "384b3609-330d-400d-a53a-e98885bd1566",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71b6fedb-87d0-4b24-924a-17a0b0f97337",
+   "metadata": {},
+   "source": [
+    "## Random Forest\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "61aec462-a4c1-4093-b2be-b5b16d537c74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf = RandomForestClassifier(n_estimators =100)\n",
+    "clf.fit(X_train, y_train)\n",
+    "pp = clf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "20e279ea-a247-48f9-8267-7327aa9cface",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.90      0.90      0.90        29\n",
+      "           1       0.89      0.89      0.89        27\n",
+      "\n",
+      "    accuracy                           0.89        56\n",
+      "   macro avg       0.89      0.89      0.89        56\n",
+      "weighted avg       0.89      0.89      0.89        56\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test, pp))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "2c3d055e-cdc4-413e-a944-b54d5eec27e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1e422a07450>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm = confusion_matrix(y_test, pp, labels=clf.classes_)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)\n",
+    "disp.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "eeed3511-1c37-47bc-b18b-bcd5a7545d07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy of Random forest classifier model is 89.28571428571429\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Accuracy of Random forest classifier model is {}'.format(accuracy_score(y_test,pp)*100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38bb3b07-3826-4095-8ac5-a0f758168a95",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "8a59d445-be1c-4e4e-91fe-89f2f82051cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display = RocCurveDisplay.from_estimator(clf, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "a51764c6-c1cf-40c6-9d5b-8b775fac0e6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import joblib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "a132fcd8-0865-4e5f-8844-8dfd103d6f65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['heart_model.sav']"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heart_file = 'heart_model.sav'\n",
+    "joblib.dump(mdl, heart_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f0a6616-21ff-48cc-9443-0b2d0e2b4765",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e3aa27c-1050-46cc-9c0e-a65f8fd60453",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ed45717-3cb6-4f7c-bee6-c43c8e9e3af7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e163807a-71d8-4095-a812-31ab4adcdafa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15f6f610-9422-40c1-a05c-12bc3c9dec85",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b2bd7b4-1696-46b3-a652-1b506f370919",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5790ec8e-9a36-449a-817d-d9986898e970",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d130da8-a439-4ed3-9e0f-9b2d8b17d075",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a4bd3e7-9468-4b53-9fed-863182d3509b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59f85a06-fad5-49d6-85ec-d2a0ef8cef0e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e043b11-daa3-4189-ad8c-adc956080216",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b6f8ad7-1aa1-4bcf-88c9-a1b977d87466",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0c307ba-82ec-49c4-9cea-3f25833b75a8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04f26b1a-c44b-4a70-91fd-59b3cbf499ea",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "302c2c19-b4c2-4d0b-9b97-a87528112ff1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
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