From 382b51a524f5f3e5085a6d3f91e1d584aa177a84 Mon Sep 17 00:00:00 2001
From: Preeti Rai <80594710+Preetirai-tech@users.noreply.github.com>
Date: Thu, 15 Jun 2023 01:40:40 +0530
Subject: [PATCH] Created using Colaboratory

---
 Cardiovascular_Risk_Prediction.ipynb | 6048 ++++++++++++++++++++++++++
 1 file changed, 6048 insertions(+)
 create mode 100644 Cardiovascular_Risk_Prediction.ipynb

diff --git a/Cardiovascular_Risk_Prediction.ipynb b/Cardiovascular_Risk_Prediction.ipynb
new file mode 100644
index 0000000..5333c8d
--- /dev/null
+++ b/Cardiovascular_Risk_Prediction.ipynb
@@ -0,0 +1,6048 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Preetirai-tech/Cardiovascular-Risk-Prediction/blob/main/Cardiovascular_Risk_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PBEcQTkBu72S"
+      },
+      "source": [
+        "#**Problem Statement**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VnrahanovaTS"
+      },
+      "source": [
+        "**The dataset is from an ongoing cardiovascular study on residents of the town of Framingham,\n",
+        "Massachusetts.**\n",
+        "\n",
+        "**The classification goal is to predict whether the patient has a 10-year risk of future coronary heart disease (CHD).**\n",
+        "\n",
+        "**The dataset provides the patients’ information. It includes\n",
+        "over 4,000 records and 15 attributes.**\n",
+        "\n",
+        "**Variables**:\n",
+        "\n",
+        "**Each attribute is a potential risk factor. There are both demographic, behavioral, and medical risk\n",
+        "factors.**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "deX1BCbHlE59"
+      },
+      "source": [
+        "# **Data Description**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TEo6UkuMlpXe"
+      },
+      "source": [
+        "**Variables**:\n",
+        "\n",
+        "Each attribute is a potential risk factor. There are both demographic, behavioral, and medical risk\n",
+        "factors.\n",
+        "\n",
+        "**Demographic:**\n",
+        "* `Sex`: male or female(\"M\" or \"F\")\n",
+        "* `Age`: Age of the patient;(Continuous - Although the recorded ages have been truncated to whole numbers, the concept of age is continuous)\n",
+        "\n",
+        "**Behavioral:**\n",
+        "* `is_smoking`: whether or not the patient is a current smoker (\"YES\" or \"NO\")\n",
+        "* `Cigs Per Day`: the number of cigarettes that the person smoked on average in one day.(can be considered continuous as one can have any number of cigarettes, even half a cigarette.)\n",
+        "\n",
+        "**Medical( history)**\n",
+        "* `BP Meds`: whether or not the patient was on blood pressure medication (Nominal)\n",
+        "* `Prevalent Stroke`: whether or not the patient had previously had a stroke (Nominal)\n",
+        "* `Prevalent Hyp`: whether or not the patient was hypertensive (Nominal)\n",
+        "* `Diabetes`: whether or not the patient had diabetes (Nominal)\n",
+        "\n",
+        "**Medical(current)**\n",
+        "* `Tot Chol`: total cholesterol level (Continuous)\n",
+        "* `Sys BP`: systolic blood pressure (Continuous)\n",
+        "* `Dia BP`: diastolic blood pressure (Continuous)\n",
+        "* `BMI`: Body Mass Index (Continuous)\n",
+        "* `Heart Rate`: heart rate (Continuous - In medical research, variables such as heart rate though in fact discrete, yet are considered continuous because of large number of possible values.)\n",
+        "* `Glucose`: glucose level (Continuous)\n",
+        "\n",
+        "**Predict variable (desired target):**\n",
+        "* **10-year risk of coronary heart disease CHD(binary: “1”, means “Yes”, “0” means “No”) -**\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5Ece2ae1m51i"
+      },
+      "source": [
+        "##**Importing and Loading Data**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "KZge2I5yqRRT"
+      },
+      "outputs": [],
+      "source": [
+        "#Importing libraries\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+        "\n",
+        "\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from xgboost import XGBClassifier\n",
+        "from sklearn.svm import SVC\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "\n",
+        "from sklearn.model_selection import GridSearchCV\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "from sklearn.metrics import f1_score\n",
+        "from sklearn.metrics import classification_report\n",
+        "from sklearn.metrics import recall_score,precision_score,classification_report,roc_auc_score,roc_curve"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WvFf3J3cg8Wp",
+        "outputId": "0b7b5558-5129-4636-b833-de7f52b3f905"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "tbjVCMf2qmuF"
+      },
+      "outputs": [],
+      "source": [
+        "#Importing Cardiovascular Risk Prediction dataset into Pandas Dataframe\n",
+        "crp_df= pd.read_csv('/content/drive/MyDrive/Feuchr School/data_cardiovascular_risk.csv')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XlFdELVTrPGm"
+      },
+      "source": [
+        "##**Exploring Data Frame**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "tZ5KTaUfrNXk",
+        "outputId": "a0b4fe0e-276b-4a7b-b6db-1dd9f0351592"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   id  age  education sex is_smoking  cigsPerDay  BPMeds  prevalentStroke  \\\n",
+              "0   0   64        2.0   F        YES         3.0     0.0                0   \n",
+              "1   1   36        4.0   M         NO         0.0     0.0                0   \n",
+              "2   2   46        1.0   F        YES        10.0     0.0                0   \n",
+              "3   3   50        1.0   M        YES        20.0     0.0                0   \n",
+              "4   4   64        1.0   F        YES        30.0     0.0                0   \n",
+              "\n",
+              "   prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  \\\n",
+              "0             0         0    221.0  148.0   85.0    NaN       90.0     80.0   \n",
+              "1             1         0    212.0  168.0   98.0  29.77       72.0     75.0   \n",
+              "2             0         0    250.0  116.0   71.0  20.35       88.0     94.0   \n",
+              "3             1         0    233.0  158.0   88.0  28.26       68.0     94.0   \n",
+              "4             0         0    241.0  136.5   85.0  26.42       70.0     77.0   \n",
+              "\n",
+              "   TenYearCHD  \n",
+              "0           1  \n",
+              "1           0  \n",
+              "2           0  \n",
+              "3           1  \n",
+              "4           0  "
+            ],
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+              "  <div id=\"df-70bd6857-bf2a-43e8-9e49-e442f855659c\">\n",
+              "    <div class=\"colab-df-container\">\n",
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+              "      <td>75.0</td>\n",
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+              "      <td>94.0</td>\n",
+              "      <td>0</td>\n",
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+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>50</td>\n",
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+              "      <td>0</td>\n",
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+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70bd6857-bf2a-43e8-9e49-e442f855659c')\"\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-70bd6857-bf2a-43e8-9e49-e442f855659c button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-70bd6857-bf2a-43e8-9e49-e442f855659c');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "#Checking top 5 observations\n",
+        "crp_df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 270
+        },
+        "id": "x34IwnpDroXc",
+        "outputId": "e6d810a0-3e05-40e8-dc7b-50de044fc212"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        id  age  education sex is_smoking  cigsPerDay  BPMeds  \\\n",
+              "3385  3385   60        1.0   F         NO         0.0     0.0   \n",
+              "3386  3386   46        1.0   F         NO         0.0     0.0   \n",
+              "3387  3387   44        3.0   M        YES         3.0     0.0   \n",
+              "3388  3388   60        1.0   M         NO         0.0     NaN   \n",
+              "3389  3389   54        3.0   F         NO         0.0     0.0   \n",
+              "\n",
+              "      prevalentStroke  prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  \\\n",
+              "3385                0             0         0    261.0  123.5   79.0  29.28   \n",
+              "3386                0             0         0    199.0  102.0   56.0  21.96   \n",
+              "3387                0             1         0    352.0  164.0  119.0  28.92   \n",
+              "3388                0             1         0    191.0  167.0  105.0  23.01   \n",
+              "3389                0             0         0    288.0  124.0   77.0  29.88   \n",
+              "\n",
+              "      heartRate  glucose  TenYearCHD  \n",
+              "3385       70.0    103.0           0  \n",
+              "3386       80.0     84.0           0  \n",
+              "3387       73.0     72.0           1  \n",
+              "3388       80.0     85.0           0  \n",
+              "3389       79.0     92.0           0  "
+            ],
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+              "      <th>sex</th>\n",
+              "      <th>is_smoking</th>\n",
+              "      <th>cigsPerDay</th>\n",
+              "      <th>BPMeds</th>\n",
+              "      <th>prevalentStroke</th>\n",
+              "      <th>prevalentHyp</th>\n",
+              "      <th>diabetes</th>\n",
+              "      <th>totChol</th>\n",
+              "      <th>sysBP</th>\n",
+              "      <th>diaBP</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>heartRate</th>\n",
+              "      <th>glucose</th>\n",
+              "      <th>TenYearCHD</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3385</th>\n",
+              "      <td>3385</td>\n",
+              "      <td>60</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>F</td>\n",
+              "      <td>NO</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>261.0</td>\n",
+              "      <td>123.5</td>\n",
+              "      <td>79.0</td>\n",
+              "      <td>29.28</td>\n",
+              "      <td>70.0</td>\n",
+              "      <td>103.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3386</th>\n",
+              "      <td>3386</td>\n",
+              "      <td>46</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>F</td>\n",
+              "      <td>NO</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>199.0</td>\n",
+              "      <td>102.0</td>\n",
+              "      <td>56.0</td>\n",
+              "      <td>21.96</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>84.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3387</th>\n",
+              "      <td>3387</td>\n",
+              "      <td>44</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>M</td>\n",
+              "      <td>YES</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>352.0</td>\n",
+              "      <td>164.0</td>\n",
+              "      <td>119.0</td>\n",
+              "      <td>28.92</td>\n",
+              "      <td>73.0</td>\n",
+              "      <td>72.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3388</th>\n",
+              "      <td>3388</td>\n",
+              "      <td>60</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>M</td>\n",
+              "      <td>NO</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>191.0</td>\n",
+              "      <td>167.0</td>\n",
+              "      <td>105.0</td>\n",
+              "      <td>23.01</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3389</th>\n",
+              "      <td>3389</td>\n",
+              "      <td>54</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>F</td>\n",
+              "      <td>NO</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>288.0</td>\n",
+              "      <td>124.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>29.88</td>\n",
+              "      <td>79.0</td>\n",
+              "      <td>92.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d2ec59f3-1120-42b1-bfe7-17e350b3c01c')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-d2ec59f3-1120-42b1-bfe7-17e350b3c01c button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-d2ec59f3-1120-42b1-bfe7-17e350b3c01c');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ],
+      "source": [
+        "#Checking top 5 observations\n",
+        "crp_df.tail()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bMr6Tz9drrTn",
+        "outputId": "a3ce0f1a-c8ec-4770-f427-f00bb7a60166"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 3390 entries, 0 to 3389\n",
+            "Data columns (total 17 columns):\n",
+            " #   Column           Non-Null Count  Dtype  \n",
+            "---  ------           --------------  -----  \n",
+            " 0   id               3390 non-null   int64  \n",
+            " 1   age              3390 non-null   int64  \n",
+            " 2   education        3303 non-null   float64\n",
+            " 3   sex              3390 non-null   object \n",
+            " 4   is_smoking       3390 non-null   object \n",
+            " 5   cigsPerDay       3368 non-null   float64\n",
+            " 6   BPMeds           3346 non-null   float64\n",
+            " 7   prevalentStroke  3390 non-null   int64  \n",
+            " 8   prevalentHyp     3390 non-null   int64  \n",
+            " 9   diabetes         3390 non-null   int64  \n",
+            " 10  totChol          3352 non-null   float64\n",
+            " 11  sysBP            3390 non-null   float64\n",
+            " 12  diaBP            3390 non-null   float64\n",
+            " 13  BMI              3376 non-null   float64\n",
+            " 14  heartRate        3389 non-null   float64\n",
+            " 15  glucose          3086 non-null   float64\n",
+            " 16  TenYearCHD       3390 non-null   int64  \n",
+            "dtypes: float64(9), int64(6), object(2)\n",
+            "memory usage: 450.4+ KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Exploring the information about our Data Frame\n",
+        "crp_df.info()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nmFtFcljrwUc",
+        "outputId": "c6b7b16e-53da-4221-e5f2-ae18ba6931be"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "CRP dataset has 3390 rows and 17 columns.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Checking the shape of data\n",
+        "print(f'CRP dataset has {crp_df.shape[0]} rows and {crp_df.shape[1]} columns.')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 364
+        },
+        "id": "lIvD3zOLsScC",
+        "outputId": "19ef206d-78ae-427c-ae33-d0c1a912380a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                id          age    education   cigsPerDay       BPMeds  \\\n",
+              "count  3390.000000  3390.000000  3303.000000  3368.000000  3346.000000   \n",
+              "mean   1694.500000    49.542183     1.970936     9.069477     0.029886   \n",
+              "std     978.753033     8.592878     1.019081    11.879078     0.170299   \n",
+              "min       0.000000    32.000000     1.000000     0.000000     0.000000   \n",
+              "25%     847.250000    42.000000     1.000000     0.000000     0.000000   \n",
+              "50%    1694.500000    49.000000     2.000000     0.000000     0.000000   \n",
+              "75%    2541.750000    56.000000     3.000000    20.000000     0.000000   \n",
+              "max    3389.000000    70.000000     4.000000    70.000000     1.000000   \n",
+              "\n",
+              "       prevalentStroke  prevalentHyp     diabetes      totChol       sysBP  \\\n",
+              "count      3390.000000   3390.000000  3390.000000  3352.000000  3390.00000   \n",
+              "mean          0.006490      0.315339     0.025664   237.074284   132.60118   \n",
+              "std           0.080309      0.464719     0.158153    45.247430    22.29203   \n",
+              "min           0.000000      0.000000     0.000000   107.000000    83.50000   \n",
+              "25%           0.000000      0.000000     0.000000   206.000000   117.00000   \n",
+              "50%           0.000000      0.000000     0.000000   234.000000   128.50000   \n",
+              "75%           0.000000      1.000000     0.000000   264.000000   144.00000   \n",
+              "max           1.000000      1.000000     1.000000   696.000000   295.00000   \n",
+              "\n",
+              "             diaBP          BMI    heartRate      glucose   TenYearCHD  \n",
+              "count  3390.000000  3376.000000  3389.000000  3086.000000  3390.000000  \n",
+              "mean     82.883038    25.794964    75.977279    82.086520     0.150737  \n",
+              "std      12.023581     4.115449    11.971868    24.244753     0.357846  \n",
+              "min      48.000000    15.960000    45.000000    40.000000     0.000000  \n",
+              "25%      74.500000    23.020000    68.000000    71.000000     0.000000  \n",
+              "50%      82.000000    25.380000    75.000000    78.000000     0.000000  \n",
+              "75%      90.000000    28.040000    83.000000    87.000000     0.000000  \n",
+              "max     142.500000    56.800000   143.000000   394.000000     1.000000  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b1d98d12-406c-42a2-a9e5-7a192bf07ba5\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>id</th>\n",
+              "      <th>age</th>\n",
+              "      <th>education</th>\n",
+              "      <th>cigsPerDay</th>\n",
+              "      <th>BPMeds</th>\n",
+              "      <th>prevalentStroke</th>\n",
+              "      <th>prevalentHyp</th>\n",
+              "      <th>diabetes</th>\n",
+              "      <th>totChol</th>\n",
+              "      <th>sysBP</th>\n",
+              "      <th>diaBP</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>heartRate</th>\n",
+              "      <th>glucose</th>\n",
+              "      <th>TenYearCHD</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>3390.000000</td>\n",
+              "      <td>3390.000000</td>\n",
+              "      <td>3303.000000</td>\n",
+              "      <td>3368.000000</td>\n",
+              "      <td>3346.000000</td>\n",
+              "      <td>3390.000000</td>\n",
+              "      <td>3390.000000</td>\n",
+              "      <td>3390.000000</td>\n",
+              "      <td>3352.000000</td>\n",
+              "      <td>3390.00000</td>\n",
+              "      <td>3390.000000</td>\n",
+              "      <td>3376.000000</td>\n",
+              "      <td>3389.000000</td>\n",
+              "      <td>3086.000000</td>\n",
+              "      <td>3390.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>1694.500000</td>\n",
+              "      <td>49.542183</td>\n",
+              "      <td>1.970936</td>\n",
+              "      <td>9.069477</td>\n",
+              "      <td>0.029886</td>\n",
+              "      <td>0.006490</td>\n",
+              "      <td>0.315339</td>\n",
+              "      <td>0.025664</td>\n",
+              "      <td>237.074284</td>\n",
+              "      <td>132.60118</td>\n",
+              "      <td>82.883038</td>\n",
+              "      <td>25.794964</td>\n",
+              "      <td>75.977279</td>\n",
+              "      <td>82.086520</td>\n",
+              "      <td>0.150737</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>978.753033</td>\n",
+              "      <td>8.592878</td>\n",
+              "      <td>1.019081</td>\n",
+              "      <td>11.879078</td>\n",
+              "      <td>0.170299</td>\n",
+              "      <td>0.080309</td>\n",
+              "      <td>0.464719</td>\n",
+              "      <td>0.158153</td>\n",
+              "      <td>45.247430</td>\n",
+              "      <td>22.29203</td>\n",
+              "      <td>12.023581</td>\n",
+              "      <td>4.115449</td>\n",
+              "      <td>11.971868</td>\n",
+              "      <td>24.244753</td>\n",
+              "      <td>0.357846</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>32.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>107.000000</td>\n",
+              "      <td>83.50000</td>\n",
+              "      <td>48.000000</td>\n",
+              "      <td>15.960000</td>\n",
+              "      <td>45.000000</td>\n",
+              "      <td>40.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>847.250000</td>\n",
+              "      <td>42.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>206.000000</td>\n",
+              "      <td>117.00000</td>\n",
+              "      <td>74.500000</td>\n",
+              "      <td>23.020000</td>\n",
+              "      <td>68.000000</td>\n",
+              "      <td>71.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>1694.500000</td>\n",
+              "      <td>49.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>234.000000</td>\n",
+              "      <td>128.50000</td>\n",
+              "      <td>82.000000</td>\n",
+              "      <td>25.380000</td>\n",
+              "      <td>75.000000</td>\n",
+              "      <td>78.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>2541.750000</td>\n",
+              "      <td>56.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>20.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>264.000000</td>\n",
+              "      <td>144.00000</td>\n",
+              "      <td>90.000000</td>\n",
+              "      <td>28.040000</td>\n",
+              "      <td>83.000000</td>\n",
+              "      <td>87.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>3389.000000</td>\n",
+              "      <td>70.000000</td>\n",
+              "      <td>4.000000</td>\n",
+              "      <td>70.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>696.000000</td>\n",
+              "      <td>295.00000</td>\n",
+              "      <td>142.500000</td>\n",
+              "      <td>56.800000</td>\n",
+              "      <td>143.000000</td>\n",
+              "      <td>394.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1d98d12-406c-42a2-a9e5-7a192bf07ba5')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-b1d98d12-406c-42a2-a9e5-7a192bf07ba5 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-b1d98d12-406c-42a2-a9e5-7a192bf07ba5');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "#Descriptive Statistics\n",
+        "crp_df.describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FvPmhC5Zs8EA",
+        "outputId": "810d82d9-be92-40de-8070-24a9b10cd215"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "glucose            304\n",
+              "education           87\n",
+              "BPMeds              44\n",
+              "totChol             38\n",
+              "cigsPerDay          22\n",
+              "BMI                 14\n",
+              "heartRate            1\n",
+              "id                   0\n",
+              "diaBP                0\n",
+              "sysBP                0\n",
+              "prevalentHyp         0\n",
+              "diabetes             0\n",
+              "age                  0\n",
+              "prevalentStroke      0\n",
+              "is_smoking           0\n",
+              "sex                  0\n",
+              "TenYearCHD           0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
+      "source": [
+        "# Missing Values in each columns\n",
+        "crp_df.isnull().sum().sort_values(ascending= False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FiTHgw2CwQ6p"
+      },
+      "source": [
+        "**Findings:**\n",
+        "\n",
+        "* Glucose has the maximum missing values which is 304 followed by 'education', 'BPmeds' and 'totChol'.\n",
+        "* Total 7 columns has missing values and 'heartRate' has the lowest missing value which is 1."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dJ2OCEiww6tD",
+        "outputId": "7d618607-d18b-4b27-af45-b2e856282da6"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "# Checking for duplicate values in the dataset\n",
+        "len(crp_df[crp_df.duplicated()])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F7vKfQbVwNLU"
+      },
+      "source": [
+        "Look's like there are no duplicate values."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "otbu0D7WxpUk"
+      },
+      "source": [
+        "#**Data Preprocessing**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CRUmZR441jWt"
+      },
+      "source": [
+        "We drop the education and id columns because it has no relation while predicting heart disease."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2b-buHgt1lbz"
+      },
+      "outputs": [],
+      "source": [
+        "#Dropping Id and education column\n",
+        "crp_df.drop(['id', 'education'], axis= 1, inplace= True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "cI3F59M8W3D8",
+        "outputId": "b011c1aa-14b9-4710-d6df-0985d7873d9a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   age sex is_smoking  cigsPerDay  BPMeds  prevalentStroke  prevalentHyp  \\\n",
+              "0   64   F        YES         3.0     0.0                0             0   \n",
+              "1   36   M         NO         0.0     0.0                0             1   \n",
+              "2   46   F        YES        10.0     0.0                0             0   \n",
+              "3   50   M        YES        20.0     0.0                0             1   \n",
+              "4   64   F        YES        30.0     0.0                0             0   \n",
+              "\n",
+              "   diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  TenYearCHD  \n",
+              "0         0    221.0  148.0   85.0    NaN       90.0     80.0           1  \n",
+              "1         0    212.0  168.0   98.0  29.77       72.0     75.0           0  \n",
+              "2         0    250.0  116.0   71.0  20.35       88.0     94.0           0  \n",
+              "3         0    233.0  158.0   88.0  28.26       68.0     94.0           1  \n",
+              "4         0    241.0  136.5   85.0  26.42       70.0     77.0           0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-76fc1030-125f-45ea-a112-9b9373bdd149\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>is_smoking</th>\n",
+              "      <th>cigsPerDay</th>\n",
+              "      <th>BPMeds</th>\n",
+              "      <th>prevalentStroke</th>\n",
+              "      <th>prevalentHyp</th>\n",
+              "      <th>diabetes</th>\n",
+              "      <th>totChol</th>\n",
+              "      <th>sysBP</th>\n",
+              "      <th>diaBP</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>heartRate</th>\n",
+              "      <th>glucose</th>\n",
+              "      <th>TenYearCHD</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>64</td>\n",
+              "      <td>F</td>\n",
+              "      <td>YES</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>221.0</td>\n",
+              "      <td>148.0</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>90.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>36</td>\n",
+              "      <td>M</td>\n",
+              "      <td>NO</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>212.0</td>\n",
+              "      <td>168.0</td>\n",
+              "      <td>98.0</td>\n",
+              "      <td>29.77</td>\n",
+              "      <td>72.0</td>\n",
+              "      <td>75.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>46</td>\n",
+              "      <td>F</td>\n",
+              "      <td>YES</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>250.0</td>\n",
+              "      <td>116.0</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>20.35</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>50</td>\n",
+              "      <td>M</td>\n",
+              "      <td>YES</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>233.0</td>\n",
+              "      <td>158.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>28.26</td>\n",
+              "      <td>68.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>64</td>\n",
+              "      <td>F</td>\n",
+              "      <td>YES</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>241.0</td>\n",
+              "      <td>136.5</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>26.42</td>\n",
+              "      <td>70.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-76fc1030-125f-45ea-a112-9b9373bdd149')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-76fc1030-125f-45ea-a112-9b9373bdd149 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-76fc1030-125f-45ea-a112-9b9373bdd149');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ],
+      "source": [
+        "crp_df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0ZLDmfYnTd-y"
+      },
+      "outputs": [],
+      "source": [
+        "#Encoding string categorical variable to int with binary values(0,1)\n",
+        "crp_df['sex']= [0 if i=='F' else 1 for i in crp_df['sex']]\n",
+        "crp_df['is_smoking']= [0 if i== 'NO' else 1 for i in crp_df['is_smoking']]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "xRe-FVQz1zz6",
+        "outputId": "a1d7edf9-19d8-48fd-c62e-359b49b898cc"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   age  sex  is_smoking  cigsPerDay  BPMeds  prevalentStroke  prevalentHyp  \\\n",
+              "0   64    0           1         3.0     0.0                0             0   \n",
+              "1   36    1           0         0.0     0.0                0             1   \n",
+              "2   46    0           1        10.0     0.0                0             0   \n",
+              "3   50    1           1        20.0     0.0                0             1   \n",
+              "4   64    0           1        30.0     0.0                0             0   \n",
+              "\n",
+              "   diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  TenYearCHD  \n",
+              "0         0    221.0  148.0   85.0    NaN       90.0     80.0           1  \n",
+              "1         0    212.0  168.0   98.0  29.77       72.0     75.0           0  \n",
+              "2         0    250.0  116.0   71.0  20.35       88.0     94.0           0  \n",
+              "3         0    233.0  158.0   88.0  28.26       68.0     94.0           1  \n",
+              "4         0    241.0  136.5   85.0  26.42       70.0     77.0           0  "
+            ],
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+              "<style scoped>\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>is_smoking</th>\n",
+              "      <th>cigsPerDay</th>\n",
+              "      <th>BPMeds</th>\n",
+              "      <th>prevalentStroke</th>\n",
+              "      <th>prevalentHyp</th>\n",
+              "      <th>diabetes</th>\n",
+              "      <th>totChol</th>\n",
+              "      <th>sysBP</th>\n",
+              "      <th>diaBP</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>heartRate</th>\n",
+              "      <th>glucose</th>\n",
+              "      <th>TenYearCHD</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>3.0</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>221.0</td>\n",
+              "      <td>148.0</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>90.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>36</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>212.0</td>\n",
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+              "      <td>98.0</td>\n",
+              "      <td>29.77</td>\n",
+              "      <td>72.0</td>\n",
+              "      <td>75.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>46</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>250.0</td>\n",
+              "      <td>116.0</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>20.35</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>50</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>233.0</td>\n",
+              "      <td>158.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>28.26</td>\n",
+              "      <td>68.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>241.0</td>\n",
+              "      <td>136.5</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>26.42</td>\n",
+              "      <td>70.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-efca6d9c-51e0-424f-b19d-38a44b413990')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-efca6d9c-51e0-424f-b19d-38a44b413990 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-efca6d9c-51e0-424f-b19d-38a44b413990');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ],
+      "source": [
+        "crp_df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RB0Ec_pA1GvM"
+      },
+      "source": [
+        "##**Handling Missing Values**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 237
+        },
+        "id": "0MXbX2SK2DGM",
+        "outputId": "81e099d0-bbfe-4845-9807-7a7dad47dbc4"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            Total  Percentage\n",
+              "glucose       304    8.967552\n",
+              "BPMeds         44    1.297935\n",
+              "totChol        38    1.120944\n",
+              "cigsPerDay     22    0.648968\n",
+              "BMI            14    0.412979\n",
+              "heartRate       1    0.029499"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-54ce5581-bf63-401f-9f4e-d4096bd589af\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>Total</th>\n",
+              "      <th>Percentage</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>glucose</th>\n",
+              "      <td>304</td>\n",
+              "      <td>8.967552</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>BPMeds</th>\n",
+              "      <td>44</td>\n",
+              "      <td>1.297935</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>totChol</th>\n",
+              "      <td>38</td>\n",
+              "      <td>1.120944</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cigsPerDay</th>\n",
+              "      <td>22</td>\n",
+              "      <td>0.648968</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>BMI</th>\n",
+              "      <td>14</td>\n",
+              "      <td>0.412979</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>heartRate</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0.029499</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-54ce5581-bf63-401f-9f4e-d4096bd589af')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-54ce5581-bf63-401f-9f4e-d4096bd589af button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-54ce5581-bf63-401f-9f4e-d4096bd589af');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ],
+      "source": [
+        "# percentage of missing data per category\n",
+        "total = crp_df.isnull().sum().sort_values(ascending=False)\n",
+        "percent_total = (crp_df.isnull().sum()/crp_df.isnull().count()).sort_values(ascending=False)*100\n",
+        "missing = pd.concat([total, percent_total], axis=1, keys=[\"Total\", \"Percentage\"])\n",
+        "missing_data = missing[missing['Total']>0]\n",
+        "missing_data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Es3ejpdNSsDP",
+        "outputId": "4568e88c-7b2c-422a-b3e5-e3a5bf0d1e8f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['glucose', 'BPMeds', 'totChol', 'cigsPerDay', 'BMI', 'heartRate'], dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "# Columns name with missing values\n",
+        "missing_data.index"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "x1HzMMin347x",
+        "outputId": "6d8ab18f-f023-4ca1-b934-ee709529348e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Total number of rows with missing values is  386\n",
+            "Total precentage of missing values in the entire dataset is 11\n"
+          ]
+        }
+      ],
+      "source": [
+        "# let's count the all rows which are having missing values\n",
+        "count=0\n",
+        "for i in crp_df.isnull().sum(axis=1):\n",
+        "    if i>0:\n",
+        "        count=count+1\n",
+        "print('Total number of rows with missing values is ', count)\n",
+        "print(f'Total precentage of missing values in the entire dataset is {round((count/len(crp_df.index))*100)}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yUyiVbH24c-0"
+      },
+      "source": [
+        "At 8.97%, the blood glucose entry has the highest percentage of missing data. The other features have very few missing entries as compared to Glucose."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 610
+        },
+        "id": "7o1RFqbn6WLE",
+        "outputId": "0c8c27d3-5cd3-4bc5-c311-5988fcc0fb93"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x720 with 6 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#Checking the distribution for missing data\n",
+        "sns.set(style='darkgrid')\n",
+        "fig = plt.figure(figsize = (10,10))\n",
+        "ax = fig.gca()\n",
+        "crp_df[missing_data.index].hist(ax= ax)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "amohujSnSVo5"
+      },
+      "source": [
+        "From above distributions it's clearly visible that most of the data are right skewed. For skewed distribution median and mode are suitable to impute the missing values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rgk_8tV-T913"
+      },
+      "outputs": [],
+      "source": [
+        "#Replacing missing values with median or mode depending on column category\n",
+        "\n",
+        "crp_df['heartRate']= crp_df['heartRate'].fillna(value=crp_df['heartRate'].median())\n",
+        "\n",
+        "crp_df['BMI']= crp_df['BMI'].fillna(crp_df['BMI'].median())\n",
+        "\n",
+        "mean_cigsPerDay= round(crp_df[crp_df['is_smoking'] == 1]['cigsPerDay'].mean(), 0)\n",
+        "crp_df['cigsPerDay']= crp_df['cigsPerDay'].fillna(mean_cigsPerDay)\n",
+        "\n",
+        "median_totChol= crp_df['totChol'].median()\n",
+        "crp_df['totChol']= crp_df['totChol'].fillna(median_totChol)\n",
+        "\n",
+        "mode_BPMeds= crp_df['BPMeds'].mode()[0]\n",
+        "crp_df['BPMeds'].fillna(mode_BPMeds, inplace= True)\n",
+        "\n",
+        "median_glucose= crp_df['glucose'].median()\n",
+        "crp_df['glucose'].fillna(median_glucose, inplace= True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bSxONE7IXLpW",
+        "outputId": "c3e340be-8092-44e6-8ba4-69a610b7a44d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "age                0\n",
+              "sex                0\n",
+              "is_smoking         0\n",
+              "cigsPerDay         0\n",
+              "BPMeds             0\n",
+              "prevalentStroke    0\n",
+              "prevalentHyp       0\n",
+              "diabetes           0\n",
+              "totChol            0\n",
+              "sysBP              0\n",
+              "diaBP              0\n",
+              "BMI                0\n",
+              "heartRate          0\n",
+              "glucose            0\n",
+              "TenYearCHD         0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
+      "source": [
+        "#Checking for the presence of missing values after imputations\n",
+        "crp_df.isnull().sum()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3SZ05evCZvHe"
+      },
+      "source": [
+        "After the above imputations we can say that all the missings values are successfully removed from our dataset."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D8nQO8Lpb7WQ"
+      },
+      "source": [
+        "#**Exploratory Data Analysis**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mRWrQZ5-jzaU"
+      },
+      "source": [
+        "##Univariate Analysis"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "lkMCoX5Nj4VI",
+        "outputId": "d0cc878e-9b2b-4b8d-84c9-095e58f2bdc6"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1296x1440 with 15 Axes>"
+            ],
+            "image/png": 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AANwIBUEAAAAAAADAjVAQBAAAAAAAANwITxkGAACAS1u3bp2mT58uwzBkGIaGDh2qTp06mR0WAACAZVEQhMNGTFsvSXpJX2e+cTTVvGAAuLTk5PPqP3GtummxPooJNzscAC7AMAy9/PLL+uyzz9S4cWPt379fjzzyiCIiIuTpWXKDYWz5SfItsW0Wx6nkc6buHwAAlA0MGQYAAIBL8/T01Pnz5yVJ58+fV82aNUu0GAgAAFDWcIcgAJjs8OHDiomJUWpqqnx9fTVp0iTVr18/2zrp6emaMGGCNm7cKA8PDw0aNEg9e/a0L4+Li9N7770nwzDk4eGhuXPnqkaNGk5uCQA4n4eHh6ZNm6YhQ4aoUqVKunjxombPnl2kbfj7Vy70ut7eJXP6HBBQpUS2Y9X9lQRidh5XjNsVYwYAK6EgCId9MqW5JGm7wiRJT/d6y8xwAJczduxY9enTR9HR0Vryf+zdf1hUdd7/8SczoFZqCAEO4Y/NWmVbMdNs3bVsFQF3Uag7smW1ezNhu/XOMi3JNhE1C1M2U9nSvVfX7N512fpqErchlT+3rHYVK7NaI9EYwCA2zQgc5vsHyyQ/xIEZ5szA63FdXtfMmc8553VgzsfhPZ/zOdu2sXDhQjZt2tSozfbt2ykuLiY/P5+qqioSExMZPXo0ERERvPfee6xZs4Y//vGPhISEcPr0abp162bQ0bgmOvpSjpeO5V1GEFjQm6qCPUZHEhEvd+7cOZ577jmys7MZMWIEf//733nggQd45ZVXuOyyy5zaRkXFGerq7K22aeifhiwodEdsTp063a71AqNvBmhT/xgS0qvd+zOKMnuOL+Z2NrPJ5Nemgr+ISFeiaynEZWUne1F2shcRJz8m4uTHRscR8SkVFRUcOXKE+Ph4AOLj4zly5AiVlZWN2uXl5ZGUlITJZCIoKIjo6Gh27NgBwMaNG5k+fTohISEA9OrVi+7du3v2QNzk8GEz/yoP5OryTwk4fMjoOCLiAz788EPKy8sZMWIEACNGjOCSSy7h2LFjbt1PQ/9ktIDDh9Q/ioiIiMtUEBQRMZDVaiUsLAyz2QyA2WwmNDQUq9XarF14eLjjucViobS0FIBjx45x4sQJfvnLX3LrrbeSnZ2N3d76SBcRkc6ib9++lJaW8umnnwL1fWJFRQX9+/c3OJmIiIiI99IlwyIiPs5ms/HRRx+xYcMGampqmDFjBuHh4SQmJjq9DW+9nKat8wN19fmEdPw6/q4oJCSERYsWcf/99+Pn5wfAsmXLCAw0fjSfiIiIiLfyioKgqxPqr169mv/93/8lNDQUgOuvv5709HRPH4aISJtZLBbKysqw2WyYzWZsNhvl5eVYLJZm7UpKSoiKigIajxgMDw8nLi6Obt260a1bN8aPH8/hw4fbVBB0Zv4sz2hc0GjLnEa+OAeSO+n4dfwtHX9XmT9r8uTJTJ482egYIiIiIj7DKy4ZbphQ/9VXXyU5OZmFCxc2a3P+hPpbtmxh9erVnDx50vF6YmIi27ZtY9u2bSoGiojPCA4OJjIyktzcXAByc3OJjIwkKCioUbu4uDhycnKoq6ujsrKSgoICYmNjgfp5B/ft24fdbqe2tpa33nqLIUOGePxYRERERERExDcYXhB0x4T6IiK+bNGiRWzevJnY2Fg2b95MRkYGACkpKbz33nsAJCQkEBERQUxMDHfccQezZs2iX79+APz85z8nODiYn/3sZyQmJnL11Vdz++23G3Y8IiIiIiIi4t0Mv2S4tQn1zx8h09qE+gCvvPIK+/btIyQkhPvuu4/hw4d77iC6uKjRJQC8RbzBSUR806BBg8jJyWm2fP369Y7HZrPZUShsymQy8cgjj/DII490WEZPmTatht2HStjBBMZed6XRcUREHBr6J6N9M+1XRkcQERGRTsDwgqA73Hnnndx7770EBASwf/9+Zs6cSV5eHn369HF6G746v443TCD+86lFALxMWv2CY19cNJc35G4vX82u3OILVq78lulPFrKWWYxIG2d0HBERh4b+CYy9WcmZlc8Yun8RERHpHAwvCLpjQv2QkBBHu5/85CdYLBY++eQTRo0a5XQO75lQ33neMoF6be25ZsvOzzUzazfVNbZGr/foZib7wbEdns3dvOVn3lZdOXdXmVBfRERERERExFmGzyHojgn1y8rKHO0+/PBDPv/8c773ve957iC6uNITPSk90ZMrT3zElSc+avZ6dY2NIf0DHf+GDrqiWYFQRASgsNBEVdnlDCr7J/6FB42OIyLi0NA/Gc2/8KD6RxEREXGZ4SMEoX5C/bS0NLKzs+nduzeZmZlA/YT6s2fPZujQoSQkJFBYWEhMTAxAown1s7Ky+OCDDzCZTAQEBLB8+fJGowalY21eOQKA5xkJwGsPbjUyjoj4sAkTLgNuYS8/hRfgVPlXRkcSEQG+659+dMNuQ3P0mVB/hYX6RxEREXGFVxQEXZ1Qv6GAKCIiIiIiIiIiIq0z/JJhERERERERERER8RwVBEVERERERERERLoQFQRFRERERERERES6EBUERUREREREREREuhAVBEVERERERERERLoQr7jLsPi2qXP/DsDTPFe/wG5gGBHxaTt3fk3Gxnd4gBWk/+oGo+OIiDg09E/gZ2iOL3fuNnT/IiIi0jmoICgu69vvDACfM7h+QXGVgWlExJcNG1ZHYNi/OMbVnBs23Og4IiIODf0TBBqaQ32jiIiIuIMuGRYREREREREREelCNEJQXJa/5RoAnuPXADw+eoaRcUTEh82d253Dh4bxHKn0PLWVMyufMTqSiAjwXf805J7jhuboOXc2gPpHERERcYkKguKyw2+GA/AjcusXqCAoIu30/PPdgIHEsRPe0x+8IuI9GvonMLYgeMnzGwH1jyIiIuIat1wyXFBQwLlz59yxKRERn6M+UETEOeovRURERLyDWwqCzzzzDGPGjGHx4sUUFha6Y5MiIj5DfaCIiHPUX4qIiIh4B7cUBF9++WU2btxI9+7due+++4iNjSU7O5uTJ0+6Y/MiIl7N1T6wqKiIKVOmEBsby5QpU/jss8+atbHZbGRkZBAdHc2ECRPIyclxvLZ69WpGjx5NQkICCQkJZGRkuOvQRETcSp8ZRURERLyD2+YQHDJkCEOGDOHhhx/mzTff5Mknn2T16tVcf/31TJkyhfj4eEwm3dRYRDonV/rA9PR0kpOTSUhIYNu2bSxcuJBNmzY1arN9+3aKi4vJz8+nqqqKxMRERo8eTUREBACJiYnMnz+/w49TRMRV+swoIiIiYjy33lSkuLiYl19+mZdffhk/Pz9mz56NxWLhhRdeID8/nzVr1rhzdyIiXqU9fWBFRQVHjhxhw4YNAMTHx7NkyRIqKysJCgpytMvLyyMpKQmTyURQUBDR0dHs2LGDGTN0Ex8R8T36zCgiIiJiLLcUBF944QW2bdvG8ePHmThxIsuXL+e6665zvB4bG8uPf/xjd+xKvFBYxGkATvJ9g5OIGMOVPtBqtRIWFobZbAbAbDYTGhqK1WptVBC0Wq2Eh4c7nlssFkpLSx3PX3nlFfbt20dISAj33Xcfw4cPd/dhekRUlI3jpaf5J1cxoG9vo+OIiJv58mfGhv7JaLVR1128kYiIiMhFuKUguGfPHu6++27Gjx9Pt27dmr1+ySWXsHr1anfsSjrQzKzdVNfYHM97dDOT/eDYi643bd4/AFjFuvoFxVUdkk/EWxndB955553ce++9BAQEsH//fmbOnEleXh59+vRxehvBwT07LF9bFBbCpLm7mUMW21cmENLG9UNCenVILl+h49fxezuj+0tXFBScZfqTu4FAQ3NUFewxdP8iIiLSObilIDhq1CgmTpzYbPmGDRu4++67ARgzZow7diUdqLrGxpD+333IParCnohTXOkDLRYLZWVl2Gw2zGYzNpuN8vJyLBZLs3YlJSVERUUBjUcMhoR8Vzb7yU9+gsVi4ZNPPmHUqFFOH0NFxRnq6uxOt/eEU6faNhInJKRXm9fpTHT8Ov6Wjt9k8vOagj/oM6OIiIiIt3DLjM1r165tcfnvfvc7d2xeRMSrudIHBgcHExkZSW5uLgC5ublERkY2ulwYIC4ujpycHOrq6qisrKSgoIDY2FgAysrKHO0+/PBDPv/8c773ve+193BERDqMPjOKiIiIeAeXRgi++eabANhsNt566y3s9u9Gl5w8eZLLLrvMtXTiE1Y8UH9ZsR0/ACY9uNXIOCIe464+cNGiRaSlpZGdnU3v3r3JzMwEICUlhdmzZzN06FASEhIoLCwkJiYGgFmzZtGvXz8AsrKy+OCDDzCZTAQEBLB8+fJGowZ9SWhoLyChvj/JglPlXxkdSUTcoDN8Zmzon4Y8vdvQHCGh9fOrqn8UERERV7hUEHz00UcBqKmpYcGCBY7lfn5+hISE8Jvf/Ma1dCIiXsxdfeCgQYPIyclptnz9+vWOx2azmYyMjBbXbyggioh4K31mFBEREfEuLhUEX3/9dQAefvhhli9f3u7tFBUVkZaWRlVVFYGBgWRmZjJw4MBGbWw2G0uXLmXv3r34+fmRmppKUlJSozaffvopt956K8nJycyfP7/deUREnOGuPlBEpLNTfykiIiLiXdwyh6CrH+zS09NJTk7m1VdfJTk5mYULFzZrs337doqLi8nPz2fLli2sXr2akydPOl632Wykp6cTHR3tUhYRkbbSH7ciIs7pqP7y22+/JT09nZiYGCZNmsRjjz3WIfsRERER6SzaPUJw4sSJ/N///R8AY8eOxc/Pr8V2u3btanU7FRUVHDlyhA0bNgAQHx/PkiVLqKysbDSpfl5eHklJSZhMJoKCgoiOjmbHjh3MmDEDgHXr1nHLLbdw9uxZzp49297DEhFxirv6QBGRzs4T/eVTTz1F9+7defXVV/Hz8+OLL75o97ZEREREuoJ2FwSXLFniePzUU0+1O4DVaiUsLAyz2QzUz5MVGhqK1WptVBC0Wq2Eh4c7nlssFkpLSwE4evQo+/btY9OmTWRnZ7c7i4iIs9zVB4qIdHYd3V9+/fXXbN26ld27dzuKjVdccYXb9yMiIiLSmbS7IDhy5EjH41GjRrklTHvU1tby2GOP8cQTTziKiu0RHNzTjak8JySkl1u3FxDQ+C3R3u03Xa/pdl3ZttGU27O8Nbe39IEiIt6uo/vLEydOEBgYyJo1azhw4ACXXXYZ999/f6P9ioiIiEhjLt1UpMGGDRv40Y9+RGRkJIcOHeKBBx7AZDKxcuVKhg8f3uq6FouFsrIybDYbZrMZm81GeXk5FoulWbuSkhKioqKA70YMnjp1iuLiYlJTUwH46quvsNvtnDlzptE30hdTUXGGujp7G4/cWCEhvTh16rRbt1lbe67Rc2e2P+GOjwH4K3MvuN75220oDro7uyd0xM/cE7pybpPJr8ML/q70gdLYihXV/HHHUdbwX/xn3BCj44iIm3VEf2mz2Thx4gQ/+MEPmD9/PoWFhdx7773s3LmTnj2d6/+dJPPOQgAAIABJREFU+X/iuedgTc6hFr/kbI92f+H13HPtWt9bv2BrjTJ7ji/m9sXMIiLexC2faDZu3Mjtt98OwMqVK/nVr37FZZddxrJly8jJyWl13eDgYCIjI8nNzSUhIYHc3FwiIyMbXS4MEBcXR05ODjExMVRVVVFQUMALL7xAeHg4Bw4ccLRbvXo1Z8+e1V2GPWjYj60AHGBS/YLiKgPTiHieK32gNHbXXbXsKjnOq8Qy5a5xRscRETfriP7SYrHg7+9PfHw8AMOGDaNPnz4UFRUxdOhQp7bhzBfDt94K2z86Tm1tYLtyNtXuL7xu/cW/N+D8+r74xaAye44v5nY2sye+GBYR8VVuucvw6dOn6dWrF2fOnOGjjz5i2rRpJCUlUVRU5NT6ixYtYvPmzcTGxrJ582YyMjIASElJ4b333gMgISGBiIgIYmJiuOOOO5g1axb9+vVzR3wREZe42geKiHQVHdFfBgUFceONN7J//34AioqKqKioYMCAAe6KLSIiItLpuGWEoMVi4R//+Af//Oc/GTlyJGazmTNnzjg9p9+gQYNa/FZ4/fr1jsdms9lRKGzNfffd53xwcYvCv9Vf3p3KOgCORtxkZBwRj3O1D5TvbNoUwPHDA0hlHT02Haf6rruNjiQibtRR/WVGRgYLFiwgMzMTf39/li9fTu/evd2Uul5D/zSk/7/cut226rFpA4D6RxEREXGJWwqCDz/8MLNnz6Zbt24888wzALzxxhtOX6Yhvm3nX74PQD4rAfjjgyoISteiPtB95s3rAVzHf/M7KNAfvCKdTUf1l/369eP55593R8QLauifYuN3d+h+LqbXvPsB9Y8iIiLiGrcUBMeOHcu+ffsaLYuLiyMuLs4dmxcR8WrqA0VEnKP+UkRERMQ7uOc2adTPCVNUVMTXX3/daPno0aPdtQsREa+lPlBExDnqL0VERESM55aC4EsvvcTixYu59NJL6dGjh2O5n58fr732mjt2ISLitdQHum5m1m6qa2xAgtFRRKQDqb8UERER8Q5uKQj+9re/ZdWqVYwdO9YdmxMR8SnqA11XXWNjSP9Aco0OIiIdSv2liIiIiHcwuWMjNpuNMWPGuGNTIiI+R32giIhz1F+KiIiIeAe3FARTUlL43e9+R11dnTs2JyLiU9QHiog4R/2liIiIiHdwyyXDGzdu5IsvvuD3v/89gYGBjV7btWuXO3YhXmze07sBeIhd9QuKq4wLI2IAV/vAoqIi0tLSqKqqIjAwkMzMTAYOHNiojc1mY+nSpezduxc/Pz9SU1NJSkpq1ObTTz/l1ltvJTk5mfnz57t6WIaIf3AbAJPYyh/SxhmcRkTczZc/M5aXn2b6k68DgRdt25FOlX9l6P5FRESkc3BLQfCpp55yx2ZERHySq31geno6ycnJJCQksG3bNhYuXMimTZsatdm+fTvFxcXk5+dTVVVFYmIio0ePJiIiAqgvGKanpxMdHe1SFhGRjqTPjCIiIiLewS0FwVGjRrljMyIiPsmVPrCiooIjR46wYcMGAOLj41myZAmVlZUEBQU52uXl5ZGUlITJZCIoKIjo6Gh27NjBjBkzAFi3bh233HILZ8+e5ezZs64dkIhIB9FnRhERERHv4JY5BGtqavjtb3/L+PHjGTFiBAD79u1j8+bN7ti8eLnnV1zP8yuu5/4Vqdy/ItXoOCIe50ofaLVaCQsLw2w2A2A2mwkNDcVqtTZrFx4e7nhusVgoLS0F4OjRo+zbt49f/epXbjoi4+zdPJa9m8fy280PEhh9s9FxRMTNfPkzY3T0pezdbPzdkQOjb1b/KCIiIi5zywjBZcuWUVZWxooVK0hJSQHgmmuu4YknnmDq1Knu2IV4sbKTvQCI4GODk4gYw8g+sLa2lscee4wnnnjCUVRsj+Dgnm5M1T4BAf78q7x+bq6r+RTKISSkV5u20db2nY2OX8fv7Xz5M+Phw2aMnj8QIODwIaMjiIiISCfgloJgQUEB+fn5XHrppZhM9YMOw8LCKCsrc8fmRUS8mit9oMVioaysDJvNhtlsxmazUV5ejsViadaupKSEqKgo4LsRg6dOnaK4uJjU1PrRuV999RV2u50zZ86wZMkSp4+houIMdXV2p9t3hNrac82WnTp12un1Q0J6tal9Z6Pj1/G3dPwmk59XFPwb6DOjiIiIiHdwS0EwICAAm83WaFllZWWzu8eJtMXMrN1U1zR+X/XoZib7QeMv1xE5nyt9YHBwMJGRkeTm5pKQkEBubi6RkZGN5g8EiIuLIycnh5iYGKqqqigoKOCFF14gPDycAwcOONqtXr2as2fP+tRdhmdm7cbf7Gd0DBHxAH1mFBEREfEObikIxsXFMX/+fB555BEAysvLWbZsGT//+c/dsXlxg6bFNV8orFXX2BjSv/EfCEeLqwxKI3JhrvaBixYtIi0tjezsbHr37k1mZiYAKSkpzJ49m6FDh5KQkEBhYSExMTEAzJo1i379+nXMAXlYS+e6iHRO+swoIiIi4h3cUhCcM2cOK1euZPLkyXzzzTfExsZy++23M2vWLHdsXtyg6R/cKqyJuI+rfeCgQYPIyclptnz9+vWOx2azmYyMjItu67777nM+uIiIh+kzo4iIiIh3cEtBsLi4mO9973v8+te/xmazER0dzeDBg92xaRERr6c+UETEOeovRURERLyDSwVBu93OggUL2Lp1K3379iU0NJSysjLWrl1LQkICy5Ytw89P80J1dlGjSwB4i3iDk4h4lvpA9+s/9DMAdjCBsdddaWwYEXGbztBfTptWw+5DJUbH4JtpvzI6goiIiHQCLhUEt2zZwttvv82WLVscd74EOHz4MHPnzuXPf/4zv/jFL1wOKd4tZsonALzIvPoFuhxZugj1ge4XNaEQgLXMYkTaOIPTiIi7dIb+cuXKb5n+ZCFg7JynZ1Y+Y+j+RUREpHMwubLytm3b+M1vftPogx1AVFQUCxYsYNu2bS6FE2P5m/2Y/uTrjf7NzNptdCwRr6E+UETEOeovRURERLyLSyMEjx07xg033NDiazfccAMPP/ywK5uXDtRQ7Gu67HxXX3l5s/VauhlJ6YmeAIzg7/VtCHNXTBGvpj7Q/arK6vudEfydFQ8X8Wnfq73+jugicnGdob8sLDTV91H9jc3hX3gQgHPDhhsbRERERHyaSwVBm81Gz549W3ytZ8+e1NXVObWdoqIi0tLSqKqqIjAwkMzMTAYOHNhsX0uXLmXv3r34+fmRmppKUlISAC+++CIbN27EZDJRV1dHUlISd911lyuH1um1VOxrr80rRwDwPCMBeO3BrW7btog3c1cfKN/Z98ItAOzlpwBMUn8i0il0hv5ywoTLgFv40Q3GXi3RZ0L9lySnyr8yNIeIiIj4NpcKgufOneOtt97Cbre3+LrNZnNqO+np6SQnJ5OQkMC2bdtYuHAhmzZtatRm+/btFBcXk5+fT1VVFYmJiYwePZqIiAhiY2O57bbb8PPz48yZM0yaNIlRo0YxZMgQVw5PRKRV7uoDRUQ6O/WXIiIiIt7FpYJgcHAwCxYsuODrQUFBF91GRUUFR44cYcOGDQDEx8ezZMkSKisrG62fl5dHUlISJpOJoKAgoqOj2bFjBzNmzGj0jXN1dTW1tbVef6c6EfF97ugDRUS6AvWXIiIiIt7FpYLg66+/fvFGF2G1WgkLC8NsNgNgNpsJDQ3FarU2+nBotVoJDw93PLdYLJSWljqev/baa2RlZVFcXMzcuXMZPHhwm3IEB7d8GYu3Cwnp5XTbgACXft1t2mfTNi3t25nttHe9jmT0/ttLud3PHX2giEhXoP5SRERExLu4p0LkBcaPH8/48eMpKSlh1qxZ3HzzzVx11VVOr19RcYa6upYvY/FWISG9OHXqtNPta2vPuWW/zuyzaZvz991Q5HNmOy1lbssxu1tbf+beoivnNpn8fLbgLyIiIiIiItIRTEYHsFgslJWVOeaOsdlslJeXY7FYmrUrKSlxPLdarfTt27fZ9sLDwxk6dCi7du3q0NwiIiIiIiIiIiK+yPCCYHBwMJGRkeTm5gKQm5tLZGRks7lk4uLiyMnJoa6ujsrKSgoKCoiNjQXg2LFjjnaVlZUcOHCA73//+547CBERERERERERER/hFZcML1q0iLS0NLKzs+nduzeZmZkApKSkMHv2bIYOHUpCQgKFhYXExMQAMGvWLPr16wfAli1b2L9/P/7+/tjtdqZOncqYMWMMO56uZurcvwPwNM/VL/CtK69FxIuM+eUuAB5ghbFBRESa2LnzazI2vgMYe+O6L3fuNnT/IiIi0jl4RUFw0KBB5OTkNFu+fv16x2Oz2UxGRkaL67d21zrpeH37nQHgc/59I5fiKgPTiIgvCwz7FwDHuNrgJCIijQ0bVvfvPirQ0Bznhg03dP8iIiLSOXhFQVCcNzNrN9U1tkbLenQzk/3gWIMSiYiIiIiIiIiIL1FB0MdU19gY0r/+m+mAAH9qa89x1OAReflbrgHgOX4NwOOjZxgZR0R82OGdwwB4jlQA1k6YZWQcEfExa9asYfXq1Wzfvt3t80nPndudw4eGMeSe427dblv1nDsbgDMrnzE0h4iIiPg2FQTFZYffDAfgR9TfGAYVBEWknYrfGwhAHDsBFQRFxHkffPABhw4d4sorr+yQ7T//fDdgIGBsQfCS5zcCKgiKiIiIawy/y7CISFdXVFTElClTiI2NZcqUKXz22WfN2thsNjIyMoiOjmbChAmN5l198cUXmTRpEgkJCUyaNIlNmzZ5ML2IiPFqampYvHgxixYtMjqKiIiIiE/QCEEREYOlp6eTnJxMQkIC27ZtY+HChc2Ketu3b6e4uJj8/HyqqqpITExk9OjRREREEBsby2233Yafnx9nzpxh0qRJjBo1iiFDhhh0RM6ZmaU7ZYqIe6xatYrJkycTERFhdBQRERERn6CCoIiIgSoqKjhy5AgbNmwAID4+niVLllBZWUlQUJCjXV5eHklJSZhMJoKCgoiOjmbHjh3MmDGDnj17OtpVV1dTW1uLn5+fx4+lrZreIElEpD0OHjzI+++/z7x589q9jeDgnhdv9G8BAe75+BwS0suj67u6PyMos+f4Ym5fzCwi4k1UEBQRMZDVaiUsLAyz2QyA2WwmNDQUq9XaqCBotVoJDw93PLdYLJSWljqev/baa2RlZVFcXMzcuXMZPHiw5w5CRMRA77zzDseOHWP8+PEAlJaWcs899/DEE08wZswYp7ZRUXGGujr7RVrVFx9qa8+5Etfh1KnT7VovpB3rh4T0avf+jKLMnuOLuZ3NbDL5tangLyLSlaggKCLSCYwfP57x48dTUlLCrFmzuPnmm7nqqqucXt/oD8utjbhpywiArj5aQMev4++KUlNTSU1NdTwfN24czz77rNvvMiwiIiLSmaggKC4Li6j/du4k+uAt0lYWi4WysjJsNhtmsxmbzUZ5eTkWi6VZu5KSEqKiooDmIwYbhIeHM3ToUHbt2tWmgqBzo2M6TsOIm8tDqwD4J99ld3bUgi+OcHAnHb+Ov6Xj1+gY94mKsnG81Pj3WG3UdUZHEBERkU5ABUFx2bR5/wBgFevqFxRXGZhGxLcEBwcTGRlJbm4uCQkJ5ObmEhkZ2ehyYYC4uDhycnKIiYmhqqqKgoICXnjhBQCOHTvGoEGDAKisrOTAgQPExMR4/Fjc4aap9TcamUOWwUlExFe9/vrrHbLdgoKzTH9yNxDYIdt3VlXBHkP3LyIiIp2DCoIiIgZbtGgRaWlpZGdn07t3bzIzMwFISUlh9uzZDB06lISEBAoLCx2FvlmzZtGvXz8AtmzZwv79+/H398dutzN16lSn580SERERERGRrkcFQRERgw0aNIicnJxmy9evX+94bDabycjIaHH9BQsWdFg2ERERERER6XxUEBSXrXhgLAB2/ACY9OBWI+OIiA/LzUoA1J+IiPcJDe0FJDDk6d2G5ggJ7Q3AqfKvDM0hIiIivs1kdAAREZHWzMzazcwsY/8AFxERERER6Uw0QlBERLxadY3N6AgiIiIiIiKdikYIioiIiIiIiIiIdCEaISg+bWbW7majh3p0M5P94FiDEomIiIiIiIiIeDcVBMWnVdfYGNI/sNGyo8VVBqUREREREREREfF+umRYRERERERERESkC9EIQXHZhDs+BuCvzDU4iYj4uqHRhwD4a5D6ExHxLitWVPPHHUeNjsHpFauMjiAiIiKdgFcUBIuKikhLS6OqqorAwEAyMzMZOHBgozY2m42lS5eyd+9e/Pz8SE1NJSkpCYC1a9eSl5eHyWQiICCAOXPmcNNNNxlwJF3TsB9bATjApPoFumRXRNppQNRxAA70V38iIt7lrrtq2VVyHAi8aNuOVH3X3YbuX0RERDoHr7hkOD09neTkZF599VWSk5NZuHBhszbbt2+nuLiY/Px8tmzZwurVqzl58iQAUVFR/PWvf2X79u0sW7aMOXPmUF1d7enDEBGRDuJv9mNm1m6jY4iIiIiIiHQKhhcEKyoqOHLkCPHx8QDEx8dz5MgRKisrG7XLy8sjKSkJk8lEUFAQ0dHR7NixA4CbbrqJSy65BIDBgwdjt9upqtKoEk8p/JuFwr9ZuPFv27nxb9uNjiMiPuz44QEcPzygWX9y9ZWXN7ujuIiIJ23aFMDxwwOMjkGPTRvosWmD0TFERETExxl+ybDVaiUsLAyz2QyA2WwmNDQUq9VKUFBQo3bh4eGO5xaLhdLS0mbb27p1K/3796dv374dH14A2PmX7wOQz0oA/vigLtcWkfZ5r+A6AG5nOKD+RES8x7x5PYDriI03drRyr3n3A7p0WERERFxjeEHQnd5++21WrVrFH/7whzavGxzcswMSdYyAAP9mj0NCerVpPVc4s6+mbVrad3szu2vb7dWR2+5Iyi3eQpf+ioiIiIiIGMvwgqDFYqGsrAybzYbZbMZms1FeXo7FYmnWrqSkhKioKKD5iMGDBw/y0EMPkZ2dzVVXXdXmHBUVZ6irs7t2MB5SW3sOqC+ENTw+deq00+u5ypl9NW1z/r4bCnjtzdzattuSsT1CQnp12LY7UlfObTL5+VTBvyvQpb8iIiIiIiLGMnwOweDgYCIjI8nNzQUgNzeXyMjIRpcLA8TFxZGTk0NdXR2VlZUUFBQQGxsLwOHDh5kzZw7PPPMM1157rcePQURERERERERExFcYPkIQYNGiRaSlpZGdnU3v3r3JzMwEICUlhdmzZzN06FASEhIoLCwkJiYGgFmzZtGvXz8AMjIyqK6ubnR34uXLlzN48GDPH4yIiIiIiIiIiIgX84qC4KBBg8jJyWm2fP369Y7HZrOZjIyMFtd/8cUXOyybiEhHKyoqIi0tjaqqKgIDA8nMzGTgwIGN2thsNpYuXcrevXvx8/MjNTWVpKQkANauXUteXh4mk4mAgADmzJnDTTd59804/M1+AJyz+cZUDSIiIiIiIp2JVxQERUS6svT0dJKTk0lISGDbtm0sXLiQTZs2NWqzfft2iouLyc/Pp6qqisTEREaPHk1ERARRUVFMnz6dSy65hKNHjzJ16lT27dtHjx49DDqii7v6yssBOFpcZXASERERERGRrkcFQXHZvKfr7xj6ELvqF+gPfBGnVVRUcOTIETZs2ABAfHw8S5YsobKystFcqnl5eSQlJWEymQgKCiI6OpodO3YwY8aMRqMBBw8ejN1up6qqir59+3r8eFyV+NDLADx05a76BepPRMRLlJefZvqTrwOBhuY4Vf6VofsXERGRzkEFwU5oZtbuZnfxbLg8T0S8i9VqJSwsDLPZDNRPjxAaGorVam1UEGx6Z3WLxUJpaWmz7W3dupX+/fv7ZDEQvhs5KCIiIiIiIh1HBcFOqLrGxpD+xn177W/2+/c36N89F5GO9/bbb7Nq1Sr+8Ic/tHnd4OCeHZDowgIC/Ft9fqH2ISG9Wm13sdc7Ox2/jl9ERERExBkqCIrLnl9xPQDvMhKAVfPWGRlHxKdYLBbKysqw2WyYzWZsNhvl5eVYLJZm7UpKSoiKigKajxg8ePAgDz30ENnZ2Vx11VVtzlFRcYa6Os/d4KO29lyLz5v2J/91x/JGr586dfqC2wwJ6dXq652djl/H39Lxm0x+Hi/4d1bR0ZdyvHQsQxYUGpojMPpmAKoK9hiaQ0RERHybCoLisrKT9SMSIvjY4CQivic4OJjIyEhyc3NJSEggNzeXyMjIRpcLA8TFxZGTk0NMTAxVVVUUFBTwwgsvAHD48GHmzJnDM888w7XXXmvEYbiN+hMR8VaHD5sxev5AgIDDh4yOICIiIp2ACoIiIgZbtGgRaWlpZGdn07t3bzIzMwFISUlh9uzZDB06lISEBAoLC4mJiQFg1qxZ9OvXD4CMjAyqq6tZuHChY5vLly9n8ODBnj8YERERERER8XoqCIqIGGzQoEHk5OQ0W75+/XrHY7PZTEZGRovrv/jiix2WTURERERERDofk9EBRERERERERERExHNUEBQREREREREREelCVBAUERERERERERHpQjSHoLgsanQJAG8Rb3ASEfF16k9EpK2+/PJLHn74YYqLi+nWrRsDBgxg8eLFze7W7qpp02rYfajErdtsj2+m/croCCIiItIJqCAoLouZ8gkALzLP4CQi4uua9SfFVQamERFf4Ofnx4wZM7jxxhsByMzMZMWKFSxbtsyt+1m58lumP1kIBLp1u211ZuUzhu5fREREOgddMiwiIiIiPiswMNBRDAS47rrrKCkxfiSfiIiIiDdTQVBcVnqiJ6UnenLliY+48sRHRscRER/WtD/xN/vhb/YDwN/sx8ys3QYnFBFvVldXx5/+9CfGjRvn9m0XFpqoKrvc7dttK//Cg/gXHjQ6hoiIiPg4XTIsLtu8cgQAzzMSgIee3tVh+/I3+zH9ydcbPW+PmVm7qa6xOZ736GYm+8GxLucTEdc07U8+P68/ufrKyzmqS4hFpBVLlizh0ksvZerUqW1aLzi450XbTJgAcAs3/Xh/+8I1ERLSq30rTvj35xW73TP7M5Aye44v5vbFzCIi3kQFQfEpV1/pnm/mq2tsDOn/3RxAKjKIdDyN7hORjpSZmcnx48d59tlnMZnadhFMRcUZ6uouVmCrLz7U1p5rZ8LGTp063a71QtqxfkhIr3bvzyjK7Dmezt30i/n2uLS7P2vm3HzRdiaTn1MFfxGRrkgFQRER8YiWPvy3d5SviMj5srKyeP/991m3bh3dunUzOo6ItKLpF/PtoS/zRURcp4KgtIm7LtkVEQH3jfoVka7rk08+4bnnnmPgwIHceeedAERERLB27VqDk4mIiIh4LxUEpU30x7uIeIOGy48196eIXHPNNXz0kW5qJiIiItIWKgiKOKml+U50MxIRY7g695CIiIiIiEhX1rYZlztIUVERU6ZMITY2lilTpvDZZ581a2Oz2cjIyCA6OpoJEyaQk5PjeG3fvn3cdttt/PCHPyQzM9ODyaUrqa6xMXTQFQzpH+j4p6KEiIiIiIiIiPgarxghmJ6eTnJyMgkJCWzbto2FCxeyadOmRm22b99OcXEx+fn5VFVVkZiYyOjRo4mIiKBfv348/vjj7Nixg5qaGoOOouuaOvfvADzNc06v03QuQtBoO5Gu4GLzjranPxER8YSdO78mY+M7gLHzJ3+5U3dsFxEREdcZXhCsqKjgyJEjbNiwAYD4+HiWLFlCZWUlQUFBjnZ5eXkkJSVhMpkICgoiOjqaHTt2MGPGDAYMGABAQUGBCoIG6NvvDACfM9jpdVqai1B3CxPp/C42D2l7+hMREU8YNqyOwLB/Aa7dHdVV54YNN3T/IiIi0jkYfsmw1WolLCwMs9kMgNlsJjQ0FKvV2qxdeHi447nFYqG0tNSjWUVExHv4m/0cNxcRERERERER5xk+QtBbBAf3NDqC0wIC/Js9DgnpdcE2He3//ncQAM+RCsDW5PkXXedC+dx1HE2340wGZ9ZxZT2j+UrOpnw1d1sUFRWRlpZGVVUVgYGBZGZmMnDgwEZtbDYbS5cuZe/evfj5+ZGamkpSUhJQP49qVlYWH3/8MdOmTWP+/Iufg94qf8s1ADzHrwF4ccq8C7a9+srLNbJYRDxm7tzuHD40jCH3HDc0R8+5swE4s/IZQ3OIiIiIbzO8IGixWCgrK8Nms2E2m7HZbJSXl2OxWJq1KykpISoqCmg+YtBVFRVnqKuzu217Ham29hxQX5hqeHzq1OkW23jCof19ARjFdgBykua22v783E256ziabqclTbftzDqurGekkJBePpGzKXfkNpn8vL7gr3lUv3P4zfp+/UfkAq0XBEVEPOn557sBAwFjC4KXPL8RUEFQREREXGP4JcPBwcFERkaSm1v/x19ubi6RkZGN5g8EiIuLIycnh7q6OiorKykoKCA2NtaIyCIibtMwj2p8fDxQP4/qkSNHqKysbNTuQvOoAgwYMIDIyEj8/Q3/jqdFM7N269JeERERERERL+IVfz0uWrSItLQ0srOz6d27N5mZmQCkpKQwe/Zshg4dSkJCAoWFhcTExAAwa9Ys+vXrB8C7777Lgw8+yJkzZ7Db7bzyyis8/vjj3HTTTYYdk7Rd0zsPX+xupCKdQWvzqJ7/xYgvz6NaXWMzOoKIiIiIiIicxysKgoMGDSInJ6fZ8vXr1zsem81mMjIyWlx/5MiR7Nmzp8PyGWlm1u5Gf0x35iLZxe4+KiIdx1OXVbd1XtCW2jfMK9nSHKpdYc7J1uj4dfwiIiIiIs7wioKgXFh1jY0h/QONjiEiHaSrzaPa1nlBW2rfMK9k0zlUfXWuTHfR8ev4Wzp+X5hHVUREREQ8TwXBTkCX2or4rvPnUU1ISLjoPKoxMTFUVVVRUFDACy+8YFBqERERERER8WUqCHYCRl9qGxZRPyLhJN83NIeIr+oK86g6+0VFe/uTmVm7Mfn5sWacmbG2AAAdgklEQVTOzW3OJiLijKgoG8dLjR+FWht1ndERREREpBNQQVBcNm3ePwBYxTqDk4j4pq4wj6qzX1y0pz+ZmbWbc7Y6ztk6/pJnEem6CgrOMv3J3YCxU7lUFXh3fy8iIiK+QQVB6XSaXkLdo5uZ7AfHGphIRDpSw1yrR4urjI4iIiIiIiLiE1QQlE6n6UgkFQlERERERERERL6jgqAXmZm1m+oaW6NlvnCDkBUP1I++s1Of9aGndxmYRkR8mfoTEfFWoaG9gASGPL3b0Bwhob0BOFX+laE5RERExLepIOhFGi57ExERERERERER6SgmowOIiIiIiIiIiIiI52iEoAjNb0QCuhmJiIiIiIiIiHROKgiK0PxGJODZm5G0NH+kCpIiIiIiIiIi0hFUEBTxAi3NH6m7I4uIiIiIiIhIR9AcgiIiIiIiIiIiIl2IRgiKyybc8TEAf2WuwUlExNe52p/MzNoNoMvtRcTtVqyo5o87jhodg9MrVhkdQURERDoBFQTFZcN+bAXgAJMMTiIivq6t/Ym/2a/R86ZzcYqIuMtdd9Wyq+Q4EHjRth2p+q67Dd2/iIiIdA66ZFhERNxuZtZux2g9d2q4I3hDIfDqKy9vdlMgf7Nfh+xbRERERESks9AIQQM1vbNs05EuvqLwbxYAUlkHwIEfa6SgSFfX3pF6F+tPWrojeFNXX3k5//z8X8zM2q1Lh0XEbTZtCuD44QEM6f8vQ3P02LQB0EhBERERcY0KggZq6c6yvmjnX74PQD4rARUERaT9XOlPAvxN2O12oL4o2PRO3ZpfUERcMW9eD+A6YuONHYHca979gAqCIiIi4hoVBDtA05F/F+KrIwK7ioZLE89/LiIXNzNrt+N8Of/y3o42ZEAQtbXnLvh6dY3NcV736GZWYVBERERERLosFQQ7QGcZ+dfVOXNpoog05819YMN53XT0oIiIiIiISFeigqB0ek1H+jUs66htN6WRSCLGuNgoX11CLCIiIiIiXZVXFASLiopIS0ujqqqKwMBAMjMzGThwYKM2NpuNpUuXsnfvXvz8/EhNTSUpKemir4l05Eg/Z7bd0kikznJDGXEP9YEdo7Xz09/sxzlbHedsdg8mEpGO4kw/KiIiIiLf8YqCYHp6OsnJySQkJLBt2zYWLlzIpk2bGrXZvn07xcXF5OfnU1VVRWJiIqNHjyYiIqLV1zxBxR1pK09eUtn0/akRi97HV/vAlkbYnT9/oDc7/9LhhnNE54aI73KmHxURERGR7xheEKyoqODIkSNs2LABgPj4eJYsWUJlZSVBQUGOdnl5eSQlJWEymQgKCiI6OpodO3YwY8aMVl9zlsnU/j9ge1/WjeHX9G73+u3lH+DPuVqzx/fb1IABjkcA9OnVvdX23pK7PdqT3XLFpaQ992azZRf7ObW0XlPdA8wsmXFjq216X9aNG34QxLl/32zhs7LTbtmup7hybrpj/Y7my31g78u6AZD+h7f5tra+6Nynd3cGhvVq87YatLU/OV97z0+AgWG9HOdGw/v/sd8fcLRrOL4LnRsNbdty3py/TnvWb8n5v8eWtumu/Xgrbz/f3eVCv8eWjr8r/Eyc7Udb48zPqaF/aku/dCGhfS5p/+/m30Haur4vvheU2XM8mTu0zyUun0fOnkO++vsQEfEEwwuCVquVsLAwzOb6P+LMZjOhoaFYrdZGH+KsVivh4eGO5xaLhdLS0ou+5qw+fS5r9zH8z29i2r1uZ7BgesOjz+qfGxVEWuTr78/g4J5GR+hQvtwHdsR7y5v6k+z54zukbUvrtGf9lpx/vrS0TXftx1t19v6iwYV+j13l+Jtyth9tjTN94GefNTz6UfuCusu/gwS3cTVffH8os+d4MrevfzYVEeksTEYHEBEREREREREREc8xvCBosVgoKyvDZqu/HMtms1FeXo7FYmnWrqSkxPHcarXSt2/fi74mIuLN1AeKiLjG2X5URERERL5jeEEwODiYyMhIcnNzAcjNzSUyMrLZJR5xcXHk5ORQV1dHZWUlBQUFxMbGXvQ1ERFvpj5QRMQ1zvajIiIiIvIdP7vdbjc6xLFjx0hLS+Orr76id+/eZGZmctVVV5GSksLs2bMZOnQoNpuNxYsXs3//fgBSUlKYMmUKQKuviYh4O/WBIiKuuVA/KiIiIiIt84qCoIiIiIiIiIiIiHiG4ZcMi4iIiIiIiIiIiOeoICgiIiIiIiIiItKFqCAoIiIiIiIiIiLShaggKCIiIiIiIiIi0oWoIOgjvv32W9LT04mJiWHSpEk89thjABQVFTFlyhRiY2OZMmUKn332mbFBm3jjjTdITEwkISGByZMnk5+fD3hf7szMTMaNG8fgwYP5+OOPHctby+ktx9BS9i+//JKUlBRiY2OZNGkS//3f/01lZaVjnUOHDjF58mRiY2OZPn06FRUVXpH7fGvWrGn2mjfkFs/z5fPTHVo7n1s7JzrT+TJz5kwmT55MYmIiycnJfPjhh0DXeQ80aNovdpXfv6c5896x2WxkZGQQHR3NhAkTyMnJ8XzQJpzJvXbtWn7+858zadIkbrvtNvbu3ev5oOdpy3n66aefMmzYMDIzMz0XsAXOZs7Ly2PSpEnEx8czadIkvvjiC88GbcKZ3BUVFaSmpjJp0iQmTpzIokWLOHfunOfD/tvFPiuCd56LIiI+wy4+YcmSJfbHH3/cXldXZ7fb7fZTp07Z7Xa7fdq0afatW7fa7Xa7fevWrfZp06YZlrGpuro6+8iRI+0fffSR3W632z/88EP7ddddZ7fZbF6X+5133rGXlJTYf/rTnzry2u2t/3y95Rhayv7ll1/a33rrLUebJ5980v7II4/Y7Xa73Waz2aOjo+3vvPOO3W6329euXWtPS0vzitwN3n//ffs999zT6DVvyS2e58vnpztc6Hxu7ZzobOfLV1995Xi8c+dOe2Jiot1u7zrvAbu9eb/YlX7/nubMe+f//b//Z58+fbrdZrPZKyoq7DfddJP9xIkTno7aiDO59+zZYz979qzdbq//XDZixAj7N99849Gc53P2PD137px96tSp9gcffND+5JNPejJiM85kPnz4sH3ixIn28vJyu91e34dVV1d7NGdTzuReunSp4+dbU1Njv/322+2vvPKKR3Oer7XPig288VwUEfEVGiHoA77++mu2bt3K/fffj5+fHwBXXHEFFRUVHDlyhPj4eADi4+M5cuRIo5FgRjOZTJw+fRqA06dPExoaypdfful1uUeOHInFYmm0rLWfrzf97FvKHhgYyI033uh4ft1111FSUgLA+++/T/fu3Rk5ciQAd955Jzt27PBc4H9rKTdATU0NixcvZtGiRY2We0tu8TxfPj/d4ULnc2vnRGc7X3r16uV4fObMGfz8/LrUe6ClfrEr/f49ydn3Tl5eHklJSZhMJoKCgoiOjjb0Z+xs7ptuuolLLrkEgMGDB2O326mqqvJ4XnA+M8C6deu45ZZbGDhwoIdTNuZs5o0bNzJ9+nRCQkKA+j6se/fuHs/bwNncfn5+fP3119TV1VFTU0NtbS1hYWFGRAYu/FnxfN52LoqI+BJ/owPIxZ04cYLAwEDWrFnDgQMHuOyyy7j//vvp0aMHYWFhmM1mAMxmM6GhoVitVoKCggxOXf+h4umnn2bmzJlceumlfP3116xbtw6r1erVuRu0ltNut/vEMQDU1dXxpz/9iXHjxgH1xxUeHu54PSgoiLq6OqqqqggMDDQqpsOqVauYPHkyERERjZZ7e27xrM5yfrbV+edza+dEZzxfHn30Ufbv34/dbuf3v/99l3oPtNQvdrXfv6c4+xml6c/YYrFQWlrq8bzn52nre37r1q3079+fvn37ejKqg7OZjx49yr59+9i0aRPZ2dmGZG3gbOZjx44RERHBL3/5S86ePcuECRP4r//6L8cX+96ae+bMmdx3332MGTOGb775hl/+8peMGDHCkMzO8rZzUUTEl2iEoA+w2WycOHGCH/zgB7z00kvMmzeP++67j7NnzxodrVXnzp3jueeeIzs7mzfeeIPf/e53PPDAA16fu7NZsmQJl156KVOnTjU6ykUdPHiQ999/n+TkZKOjiHglXzqf3e3xxx9n165dzJkzh+XLlxsdx2PUL0pHePvtt1m1ahUrV640Okqramtreeyxx8jIyHAUs3yBzWbjo48+YsOGDTz//PPs2bOHbdu2GR3ronbs2MHgwYPZt28fe/bs4d1339VoOxGRTkwFQR9gsVjw9/d3DPMfNmwYffr0oUePHpSVlWGz2YD6Dx/l5eUXHVrvKR9++CHl5eWObxZHjBjBJZdcQvfu3b06dwOLxXLBnK295k0yMzM5fvw4Tz/9NCZT/elusVgclw8DVFZWYjKZvGLUyDvvvMOxY8cYP34848aNo7S0lHvuuYd9+/Z5dW7xvM5wfrZV0/O5tXOiM58viYmJHDhwgL59+3aJ98CF+sXjx493yd9/R3P2vdP0Z2y1Wg0badeQx9n3/MGDB3nooYdYu3YtV111laejOjiT+dSpUxQXF5Oamsq4ceP44x//yF/+8hfHzfW8MTNAeHg4cXFxdOvWjZ49ezJ+/HgOHz5sRGTA+dybN29m8uTJmEwmevXqxbhx4zhw4IARkZ3mbeeiiIgvUUHQBwQFBXHjjTeyf/9+oP4uYRUVFQwcOJDIyEhyc3MByM3NJTIy0msuh+rbty+lpaV8+umnQP3lExUVFQwYMMCrczcIDg6+YM7WXvMWWVlZvP/++6xdu5Zu3bo5lv/whz+kurqad999F4A///nPxMXFGRWzkdTUVPbt28frr7/O66+/Tt++ffmf//kfxowZ49W5xfN8/fxsq5bO59bOic50vnz99ddYrVbH89dff53LL7+8y7wHLtQvzpgxo0v8/j3N2fdOXFwcOTk51NXVUVlZSUFBAbGxsUZEBpzPffjwYebMmcMzzzzDtddea0RUB2cyh4eHc+DAAcf7/z//8z+54447WLJkiddmhvo5+vbt24fdbqe2tpa33nqLIUOGGBEZcD53REQEe/bsAernLn3zzTe55pprPJ63LbztXBQR8SV+drvdbnQIubgTJ06wYMECqqqq8Pf354EHHmDs2LEcO3aMtLQ0vvrqK3r37k1mZqah3/Y29fLLL7N+/XrHnCmzZ88mOjra63IvXbqU/Px8vvjiC/r06UNgYCCvvPJKqzm95Rhayv70008THx/PwIED6dGjB1D/IW/t2rUA/OMf/yA9PZ1vv/2WK6+8kqeeeoorrrjC8NyvvPJKozbjxo3j2Wef5fvf/77X5BbP8+Xz0x0++eSTC57PrZ0TneV8+eKLL5g5cybffPMNJpOJyy+/nPnz53Pttdd2mffA+c7vF7vC798IF3rvpKSkMHv2bIYOHYrNZmPx4sWOL2tTUlKYMmWK1+f+j//4Dz7//PNGN4pYvnw5gwcP9trM51u9ejVnz55l/vz5huQF5zLX1dWRmZnJnj17MJlMjBkzhvnz5zuu1vDW3MXFxaSnp/PFF19gs9m48cYbefTRR/H3N2ba+Qv9/+/t56KIiK9QQVBERERERERERKQL0SXDIiIiIiIiIiIiXYgKgiIiIiIiIiIiIl2ICoIiIiIiIiIiIiJdiAqCIiIiIiIiIiIiXYgKgiIiIiIiIiIiIl2ICoJiuHHjxvG3v/3N6BgiIj7jwIED3HzzzUbHEBERERERH6WCoHQZL730Er/4xS8aLUtLS+OHP/whw4cPZ9SoUdx9990cO3bMqe2dPHmSwYMHc+7cuY6IKyIiIuLzhg8f7vg3ZMgQoqKiHM9ffvnlNm9v//79jB49msrKSseympoaJk6cyJ/+9Cd3Rgdg+/bt3HbbbQwfPpwxY8YwY8YM3n33XQBWr17NvHnzmq0zePBgjh8/7mhz7bXXOo45NjaWxYsXU15e7vasIiIibaGCoHQJrRXt7rnnHg4ePMiePXsICwvj0Ucf9WAyERERkc7r4MGDjn/h4eE8++yzjueTJ09u8/Z+8pOf8NOf/pTHH3/csSw7O5uQkBDuvPNOt2Ru+Ny4YcMGli1bxr333sv+/ft54403SE5O5rXXXmvT9iZOnMjBgwd5++23WbNmDV988QW33XabioIiImIoFQTFK3z44YdMmjSJESNG8MADD/Dtt98C8MYbb5CQkMDIkSO58847OXr0qGOddevWER0dzfDhw/nZz37Gzp07Ha+99NJL3HnnnSxbtowbb7yROXPmkJ6ezqFDhxg+fDgjR45slqFHjx5MnDix0T527dpFYmIi119/PWPHjmX16tWO16ZOnQrADTfcwPDhwzl48CAAf/3rX5k4cSI33HAD99xzD59//rl7f1gi0mV88MEHJCYmMnz4cGbPns0DDzzAb3/722btzh+NAvWjn89vV1BQQEJCAtdffz3R0dHs2bMHgLKyMu69915GjRrFhAkT+Mtf/uJY5/Dhw9x22/9v7/5jqqr/OI4/ucDlRxGEyQ+TbFMnbZlcvIwUTKRk/krn10zchBljCkmUUmHYclJomoNGaYNR1pKlyxBSB9ltaMwZCILUNHXOjRK8Sv5IhAnXe79/uE7y1fz1tTR5PTa2e8/5nPf9fM4fh3Pf5/353P8QGRnJ6NGjWbFihbGvqamJxMRErFYrU6dOpba29u8Yvojcw5xOp3EvFx0dzcsvv8yZM2eAP2dhbN68mbi4OKKjo/noo4+MY9944w3q6urYsWMHhw4dorS0lLy8PDo6OsjJySE2NpYxY8ZQUFDAxYsXAWhpaSE5OZno6Giio6PJysri999/N2LGx8dTXFzMs88+S0REBOfOnaOwsJC33nqLhIQEfH198fT0JD4+nuzs7Fsas6enJ0OHDqWgoIDAwEDWrVv3f5xBERGR/48SgnJXqKyspKSkhO+++46DBw9SVlbG/v37ycnJITc3l9raWmbNmsWLL75Id3c3AGFhYZSWltLQ0EBGRgavvfZaryetzc3NhIWFsWvXLt577z2WLVtGREQEjY2NxlSPy3V2drJ161YeeeQRY5uPjw8rV66kvr6eoqIivvjiC2w2GwDr168HYM+ePTQ2NmKxWLDZbBQVFfHhhx+ye/duRo4cSVZW1t956kTkHtXd3U1GRgbTp0+nrq6OKVOmGNefm9Hc3Ex2djavv/469fX1lJaW8vDDDwOwaNEiQkJCqKmpobCwkPz8fHbv3g1AXl4eycnJ7N27l2+//ZaJEycCl5KI8+fPJz09nbq6OrKzs8nMzOw1fU9E5Ho+//xzbDYb69evp6amBn9/f3Jzc3u1aWhooKqqis8++4w1a9YYy7r4+fmxbNkyli5dSk5ODgsWLCAsLIzFixfj4eHB9u3bKS8vZ9euXXz55ZcAuFwu5s+fT01NDZWVlRw/frzXg16Abdu2UVxcTH19PY2NjVy4cIHx48ff9rG7u7vz9NNPX/V+VERE5J+ihKDcFZKSkggODiYgIIBx48Zx4MABNm7cyKxZsxgxYgTu7u5Mnz4dT09PmpqagEvTL4KDgzGZTEyaNIlBgwbR3NxsxAwKCiIpKQkPDw+8vb3/8rM/+eQTrFYrkZGRNDQ0sGrVKmNfdHQ0w4YNw2QyER4ezuTJk6mrq/vLWBs2bGDevHkMHjwYDw8P0tLSOHDggKoEReSm7du3D4fDQXJyMp6eniQkJDB8+PCbjrNp0yZmzJhBTEwMJpOJ4OBgBg8eTFtbG3v37uXVV1/Fy8uLxx57jJkzZ1JRUQGAh4cHLS0tnDp1ivvuu4+IiAgAKioqeOqppxg7diwmk4mYmBgef/xxdu7ceVvHLyL3tg0bNrBw4UJCQkIwm81kZGTwzTff9FrmJSMjA29vb8LDwwkPD+81iyM+Pp4RI0bgdDpJTk6mvb2dnTt3kpOTg6+vL/369WPu3Lls27YNgEGDBhETE4PZbCYwMJAXXniBPXv29OpTUlISoaGheHt7c+bMGR588EE8PDyuOY6qqiqsVmuvvxsRFBTE2bNnb/R0iYiI3HbX/g8n8g/p37+/8drHx4cTJ05w9uxZysvLjUo8gJ6eHqMKsLy8nHXr1hnJts7OTk6fPm20DQkJuaHPTklJYeHChbS2tpKamsrRo0cJDw8HLn0hX716NYcPH6anp4fu7m4mTJjwl7FaW1tZvnw5K1euNLa5XC7sdrtRkSMiciNOnDhBcHAwbm5uxrbQ0NCbjtPW1sbYsWOvGt/f35/777/f2DZgwAB++ukn4FKFYGFhIRMnTmTgwIFkZGQwbtw4Wltbqaqqorq62jjO4XAQHR19030Tkb6rtbWVBQsWYDL9WZ9gMpn47bffjPcPPfSQ8drHx4fOzs5eMYYOHYrZbMZkMtHa2orD4SA2NtbY73Q6jetme3s7eXl51NfXc/78eVwuFw888ECveJdfYwMCAjh9+jQOh+OaScEJEyawevXqXtuGDRt23fHb7Xb8/f2v205EROTvooSg3LVCQ0NJS0sjPT39in3Hjh3jzTff5NNPP8ViseDu7s60adN6tbn8S/TV3v+vAQMGsGTJErKzsxk3bhze3t5kZWUxZ84cSkpK8PLyIi8vz0g6Xi3eH32+lUWyRUQu179/f+x2Oy6Xy7jetLW1ERYWdkVbHx8furq6jPcnT54kODgYuHRdamlpueKYP6pTOjo6jKRgW1ubcdyjjz5Kfn4+TqeT7du3k5mZSW1tLaGhoUybNo133nnnto9ZRPqOkJAQli9fzsiRI6/Y9+uvv95SPLPZzA8//HDVBF5+fj5ubm5s2bKFgIAAbDbbFVOUL7+3s1gsmM1mbDbbNR8G3wqn00l1dTWjR4++rXFFRERuhqYMy11r5syZbNiwgX379uFyuejs7GTHjh10dHTQ1dWFm5sbgYGBAHz11VccPnz4mvH69euH3W431iC8mpiYGIKCgti4cSMA58+fx9/fHy8vL5qbm9m6davRNjAwEJPJxC+//GJsS0xMpLi42OjLuXPnqKysvOVzICJ9V0REBO7u7qxfvx6Hw4HNZuPHH3+8atvw8HC2bt3KxYsX+f7773tNg3vuuecoKytj9+7dOJ1O7HY7R44cITQ0FIvFQn5+PhcuXODnn39m06ZNxgONiooKTp06hclkMqpoTCYTU6dOpbq6mpqaGi5evMiFCxeora3l+PHjf/9JEZF7xuzZs3n//feNmR6nTp26pXVS/xAUFERMTAzvvvsuHR0dOJ1OWlpajKVezp8/j6+vL35+ftjtdkpKSq4Zz8/Pj8zMTHJzc7HZbHR1ddHT08POnTt7LS9zMxwOB0eOHGHRokW0t7czd+7cW4ojIiJyOyghKHet4cOH8/bbb5Obm0tUVBQJCQmUlZUBMGTIEFJSUkhMTGT06NEcOnSIyMjIa8Z78sknGTJkCLGxsdec2paamkpJSQnd3d0sXbqUwsJCLBYLa9asMRbVh0sVOWlpacyePRur1UpTUxPjx48nNTWVRYsWERkZyZQpU4xf8xQRuRlms5kPPviATZs2ERUVxddff01cXBxms/mKtkuWLKG6uhqr1cqWLVt45plnjH1PPPEEK1asMCpx5syZQ2trK3CpYubYsWOMGTOGjIwMXnrpJaNipaamhsmTJ2OxWMjLy6OgoABvb29CQ0NZu3YtRUVFjBo1irFjx/Lxxx/jdDr/mRMjIveE5ORk4uPjSUlJwWKx8Pzzz/daC/pWrFq1ip6eHiZNmkRUVBSZmZmcPHkSuLQe4f79+7FarcybN4+EhITrxktJSWHx4sWsXbuWUaNGERcXR2lpaa9r7I2orKzEYrFgtVpJT08nICCAsrIyoyJbRETkTnBzuVyuO90JERERub6ZM2eSmJjIjBkz7nRXRERERETkX0wVgiIiInepuro6Tp48icPhYPPmzRw8eJAxY8bc6W6JiIiIiMi/nH5URERE5C519OhRXnnlFbq6uhg4cCCFhYUEBQXd6W6JiIiIiMi/nKYMi4iIiIiIiIiI9CGaMiwiIiIiIiIiItKHKCEoIiIiIiIiIiLShyghKCIiIiIiIiIi0ocoISgiIiIiIiIiItKHKCEoIiIiIiIiIiLShyghKCIiIiIiIiIi0of8F60/MVZNSd10AAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#Visualizing the mean and median and distribution of all the features\n",
+        "\n",
+        "plt.figure(figsize=(18,20))\n",
+        "features= list(crp_df.columns)\n",
+        "sns.set(style='darkgrid')\n",
+        "for n, col in enumerate(features):\n",
+        "  feature = crp_df[col]\n",
+        "  ax=plt.subplot(5, 4, n+1)\n",
+        "  # fig, ax = plt.subplots()\n",
+        "  sns.histplot(crp_df[col], ax= ax, stat= 'density', element= 'step'  )\n",
+        "  ax.axvline(feature.mean(), color='red', linestyle='dashed', linewidth=2)\n",
+        "  ax.axvline(feature.median(), color='blue', linestyle='dashed', linewidth=2)\n",
+        "  plt.title(f'{col.title()}',weight='bold')\n",
+        "  plt.tight_layout()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Yc39QJgz0jjP"
+      },
+      "source": [
+        "From above distribution plot we can say that the data on the prevalent stroke, diabetes, and blood pressure meds are poorly balanced. Also continuous features like totChol , sysBP, BMI etc are right skewed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sTp57wcG3hAN",
+        "outputId": "8d656dbf-dbee-4e1a-f230-504ac280fa12"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    2879\n",
+              "1     511\n",
+              "Name: TenYearCHD, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ],
+      "source": [
+        "# Visualizing the dependent variable 'TenYearCHD'\n",
+        "crp_df['TenYearCHD'].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 432
+        },
+        "id": "7WlB-yBM3vra",
+        "outputId": "64230c05-c238-4ba9-9072-ff914bdc410f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Cardiovascular Risk rate')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 648x432 with 1 Axes>"
+            ],
+            "image/png": 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+0KZNmzRgwACtWbOmUjW4uLho2rRpGjFihJ588skSn7OzdetW9erVS5s2bapQX08//bTS09P1zjvvaOfOneratavjc3ZOnTql559/Xq1atSp1XL9+/dSkSRMtWrRI+fn5GjZsWJmf0/LQQw9pypQpGjRokG655RY1aNBAX3zxhfbu3as+ffro448/LrH/1KlTdfjwYUVERCgoKEhubm769ttvtW3bNgUFBem2225z7BsXF6cffvhBS5cu1fbt29WzZ0+5ubnp0KFD2rx5s+bMmaOuXbtKku6++269/vrrmjdvnvbs2aO2bdtq//792rRpk/r166fU1NQKvV6VvZ7adO211yo6Olrr16/XBx984Pg8pLIsWLBA27ZtU2RkpK688kp5enrqxx9/1KeffqpmzZpp8ODBFzy2UaNGmjdvnsaOHau3335beXl5+vvf/17tqbmqvC+6d++utWvXauzYsbr55pvl4eGhwMBA3XnnnXJzc1NSUpJGjhypxx57TOHh4QoNDVXDhg3166+/aufOnfr555+1efNmHpuCchF2cNkbM2aMBgwY4HgQ6PLly5WXlydvb2+1b99eI0eOLDEa0r59ey1atEivvPKKPvnkExUUFKh9+/aaOXOmvLy8Kh12pHPrhxYvXqyXX37ZMbVz3XXX6e2339bmzZsrHHa8vb313nvvad68efroo4+0cOFCNWzYUJ06ddIjjzyinj17lnlco0aN1L9/f8dv4EW3pJ9vyJAhcnd311tvvaWVK1fKw8NDkZGRmj59utatW1cqHIwaNUppaWnatWuXtm7dKhcXFwUGBmr06NF68MEHSyyUbdasmZYuXaq33npLa9as0fvvvy+bzaaAgADdfffdJRbu+vr66p133lFiYqJ27NihHTt2KCwsTG+88YYOHTpU4bBT2eupbU8++aQ2bNig2bNna9CgQRe8DT02NlbNmjXT119/rc8//1yFhYXy9/dXbGysHn744RIfzFcWDw8PzZw5U+PHj9d7772nvLw8vfDCCxe8Zb0iqvK+uPfee5WRkaHVq1drwYIFKigo0A033OD4/mvfvr1WrVqlhQsXauPGjVq+fLlsNpv8/PzUoUMHjR07Vs2bN69yzbh8uFiWZTm7CAAAgNrCmh0AAGA0wg4AADAaYQcAABiNsAMAAIxWZ3dj/elPf9KhQ4dks9nk6emp559/XqGhodq3b58mTZqknJwceXt7KyEhQa1bt5akKrcBAAAUqbO7sU6cOOH4+O+0tDTNmjVLK1as0PDhw3X33Xfrjjvu0KpVq7Rs2TItWrRIkqrcBgAAUMQpt56vXLlSixYt0r/+9S/FxMTos88+k6urqwoLC9W1a1etW7dOlmVVqe38Z/lczPHjp2S3c+c9AACXMpvNRc2bN75ge51+qODkyZO1ZcsWWZalBQsWKDMzU/7+/nJ1dZUkubq6qkWLFsrMzJRlWVVqq0zYsdstwg4AAIar07Azbdo0SedGdhITE6v81N2a4uvbxKnnBwAAtc8pj4u48847FRcXp5YtW+rw4cMqLCx0TEcdOXJEAQEBsiyrSm2VkZ19kpEdAAAucTaby0UHMOrk1vNTp04pMzPT8fWGDRvUrFkz+fr6KjQ0VCkpKZKklJQUhYaGysfHp8ptAAAAxdXJAuWsrCz96U9/0unTp2Wz2dSsWTNNnDhRHTt21N69ezVp0iTl5uaqadOmSkhI0DXXXCNJVW6rKEZ2AAC49JU3snNZPwiUsAMAwKWvXkxjAQAAOAthBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNKc8G+ty4dW0oRp6uDm7DKDeOnM2Xydyzzi7DACGI+zUooYeboqdsNjZZQD11ruJD+iECDsAahfTWAAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwWoO6OMnx48c1YcIEHTx4UO7u7rr66qsVHx8vHx8fhYSEKDg4WDbbudyVmJiokJAQSdKGDRuUmJiowsJCdezYUdOnT1ejRo3KbQMAAChSJyM7Li4uGjlypFJTU5WcnKxWrVppxowZjvalS5dq1apVWrVqlSPonDp1Ss8//7zmzp2rjz76SI0bN9brr79ebhsAAEBxdRJ2vL291bVrV8fX119/vTIyMi56zKeffqqwsDC1bt1akjRkyBB9+OGH5bYBAAAUVyfTWMXZ7XYtWbJEUVFRjm3Dhg1TYWGhbrrpJo0dO1bu7u7KzMxUYGCgY5/AwEBlZmZK0kXbAAAAiqvzsPOPf/xDnp6eGjp0qCRp48aNCggI0MmTJ/Xss89q1qxZ+vOf/1wntfj6NqmT8wC4MD8/L2eXAMBwdRp2EhISdODAAc2dO9exIDkgIECS1KRJE917771auHChY/tnn33mODYjI8Ox78XaKiM7+6TsdqvK11MefogD5Tt69ISzSwBwibPZXC46gFFnt56/9NJL2rVrl2bNmiV3d3dJ0m+//aYzZ85IkgoKCpSamqrQ0FBJUq9evbRz507t379f0rlFzAMGDCi3DQAAoLg6Gdn54YcfNG/ePLVu3VpDhgyRJF155ZUaOXKk4uLi5OLiooKCAoWHh2vcuHGSzo30xMfHa9SoUbLb7QoNDdXkyZPLbQMAACjOxbKs2pvHqefqYhordsLiWusfuNS9m/gA01gAqq3eTGMBAAA4A2EHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAo9VJ2Dl+/LgeffRRxcTE6Pbbb9eYMWN07NgxSdJXX32lP/7xj4qJidGIESOUnZ3tOK6qbQAAAEXqJOy4uLho5MiRSk1NVXJyslq1aqUZM2bIbrfr2WefVVxcnFJTUxUZGakZM2ZIUpXbAAAAiquTsOPt7a2uXbs6vr7++uuVkZGhXbt2ycPDQ5GRkZKkIUOGaO3atZJU5TYAAIDi6nzNjt1u15IlSxQVFaXMzEwFBgY62nx8fGS325WTk1PlNgAAgOIa1PUJ//GPf8jT01NDhw7VRx99VNenL8HXt4lTzw9A8vPzcnYJAAxXp2EnISFBBw4c0Ny5c2Wz2RQQEKCMjAxH+7Fjx2Sz2eTt7V3ltsrIzj4pu92q/oVdAD/EgfIdPXrC2SUAuMTZbC4XHcCos2msl156Sbt27dKsWbPk7u4uSQoLC9OZM2eUnp4uSVq6dKn69+9frTYAAIDi6mRk54cfftC8efPUunVrDRkyRJJ05ZVXatasWUpMTNSUKVN09uxZBQUF6cUXX5Qk2Wy2KrUBAAAU52JZVu3N49RzdTGNFTthca31D1zq3k18gGksANVWb6axAAAAnIGwAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwWoXDzuuvv17m9oULF9ZYMQAAADWtwmFn1qxZZW6fM2dOjRUDAABQ0xqUt8PWrVslSXa7Xdu2bZNlWY62Q4cOqXHjxrVXHQAAQDWVG3YmT54sSTp79qz++te/Ora7uLjIz89Pzz33XO1VBwAAUE3lhp0NGzZIkiZMmKDExMRaLwgAAKAmlRt2ihQPOna7vUSbzcZNXQAAoH6qcNj59ttvFR8fr//+9786e/asJMmyLLm4uOi7776rtQIBAACqo8JhZ9KkSerTp49eeOEFNWzYsDZrAgAAqDEVDju//PKL/vznP8vFxaU26wEAAKhRFV5s069fP23evLk2awEAAKhxFR7ZOXv2rMaMGaPOnTvriiuuKNHGXVoAAKC+qnDYadeundq1a1ebtQAAANS4CoedMWPG1GYdAAAAtaLCYafosRFl6d69e7nHJyQkKDU1Vb/88ouSk5MVHBwsSYqKipK7u7s8PDwkSc8884x69eolSfrqq68UFxens2fPKigoSC+++KJ8fX3LbQMAAChS4bBT9NiIIsePH1d+fr78/f21fv36co+Pjo7W8OHD9cADD5Rqe+211xzhp4jdbtezzz6r6dOnKzIyUrNnz9aMGTM0ffr0i7YBAAAUV+GwU/TYiCKFhYWaM2dOhR8EGhkZWanCdu3aJQ8PD8dxQ4YMUXR0tKZPn37RNgAAgOIqHHbO5+rqqtGjR+vmm2/Www8/XK0innnmGVmWpc6dO2v8+PFq2rSpMjMzFRgY6NjHx8dHdrtdOTk5F23z9vau8Hl9fZtUq24A1efn5+XsEgAYrsphR5K2bNlS7Q8ZXLx4sQICApSXl6dp06YpPj5eM2bMqFafFZWdfVJ2u1Vr/fNDHCjf0aMnnF0CgEuczeZy0QGMCoedm2++uUSwOX36tPLy8jRlypRqFRgQECBJcnd3V2xsrB5//HHH9oyMDMd+x44dk81mk7e390XbAAAAiqtw2HnxxRdLfN2oUSO1adNGTZpUfSro999/V2Fhoby8vGRZltasWaPQ0FBJUlhYmM6cOaP09HRFRkZq6dKl6t+/f7ltAAAAxVU47Nxwww2Szt0llZWVpSuuuEI2W4WfNqGpU6dq3bp1ysrK0sMPPyxvb2/NnTtXY8eOVWFhoex2u9q2besYKbLZbEpMTNSUKVNK3F5eXhsAAEBxLpZlVWjRysmTJxUfH681a9aooKBADRo00G233abnnntOXl6X5tqUulizEzthca31D1zq3k18gDU7AKqtvDU7FR6amTp1qk6fPq3k5GR98803Sk5O1unTpzV16tQaKRQAAKA2VHgaa9OmTUpLS1OjRo0kSW3atNH06dPVr1+/WisOAACguio8suPh4aFjx46V2Hb8+HG5u7vXeFEAAAA1pcIjO/fcc49GjBihhx56SIGBgcrIyNCbb76pe++9tzbrAwAAqJYKh53HH39c/v7+Sk5O1pEjR9SiRQuNHDmSsAMAAOq1Ck9jTZs2TW3atNGbb76pNWvW6M0331Tbtm01bdq02qwPAACgWiocdlJSUhQWFlZiW1hYmFJSUmq8KAAAgJpS4bDj4uIiu91eYlvRhwECAADUVxUOO5GRkXr11Vcd4cZutyspKUmRkZG1VhwAAEB1VXiB8uTJkzVq1Cj17NlTgYGByszMlJ+fn+bOnVub9QEAAFRLhcNOy5YttWLFCn3zzTfKzMxUQECAOnXqVKnnYwEAANS1Cocd6dwDOK+//npdf/31tVUPAABAjWJYBgAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwWp2EnYSEBEVFRazUuHgAAA0FSURBVCkkJETff/+9Y/u+ffs0ePBgxcTEaPDgwdq/f3+12wAAAIqrk7ATHR2txYsXKygoqMT2KVOmKDY2VqmpqYqNjVVcXFy12wAAAIqrk7ATGRmpgICAEtuys7O1e/duDRw4UJI0cOBA7d69W8eOHatyGwAAwPkaOOvEmZmZ8vf3l6urqyTJ1dVVLVq0UGZmpizLqlKbj4+Psy4HAADUU04LO/WBr28TZ5cAXPb8/LycXQIAwzkt7AQEBOjw4cMqLCyUq6urCgsLdeTIEQUEBMiyrCq1VVZ29knZ7VYtXN05/BAHynf06AlnlwDgEmezuVx0AMNpt577+voqNDRUKSkpkqSUlBSFhobKx8enym0AAADnc7Esq/aGNv6/qVOnat26dcrKylLz5s3l7e2t1atXa+/evZo0aZJyc3PVtGlTJSQk6JprrpGkKrdVRl2M7MROWFxr/QOXuncTH2BkB0C1lTeyUydhp74i7ADORdgBUBPq7TQWAABAXSDsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYLQGzi5AkqKiouTu7i4PDw9J0jPPPKNevXrpq6++UlxcnM6ePaugoCC9+OKL8vX1laSLtgEAABSpNyM7r732mlatWqVVq1apV69estvtevbZZxUXF6fU1FRFRkZqxowZknTRNgAAgOLqTdg5365du+Th4aHIyEhJ0pAhQ7R27dpy2wAAAIqrF9NY0rmpK8uy1LlzZ40fP16ZmZkKDAx0tPv4+MhutysnJ+eibd7e3s4oH8BlrHkzdzVw93B2GUC9VZB3Vsd/y3Pa+etF2Fm8eLECAgKUl5enadOmKT4+Xv369av18/r6Nqn1cwC4OD8/L2eXUCM+Txzp7BKAeqvzhAXy83PeLwT1IuwEBARIktzd3RUbG6vHH39cw4cPV0ZGhmOfY8eOyWazydvbWwEBARdsq4zs7JOy262auYgymPJDHKhNR4+ecHYJ1cZ7HShfbb7XbTaXiw5gOH3Nzu+//64TJ869AJZlac2aNQoNDVVYWJjOnDmj9PR0SdLSpUvVv39/SbpoGwAAQHFOH9nJzs7W2LFjVVhYKLvdrrZt22rKlCmy2WxKTEzUlClTStxeLumibQAAAMU5Pey0atVKK1euLLMtIiJCycnJlW4DAAAo4vRpLAAAgNpE2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBol3TY2bdvnwYPHqyYmBgNHjxY+/fvd3ZJAACgnrmkw86UKVMUGxur1NRUxcbGKi4uztklAQCAeuaSDTvZ2dnavXu3Bg4cKEkaOHCgdu/erWPHjjm5MgAAUJ80cHYBVZWZmSl/f3+5urpKklxdXdWiRQtlZmbKx8enQn3YbC61WaIk6YrmjWv9HMClrC7eh3XBvamvs0sA6rXafK+X1/clG3ZqQvM6CCKv/eXOWj8HcCnz9W3i7BJqxLWjE5xdAlCvOfO9fslOYwUEBOjw4cMqLCyUJBUWFurIkSMKCAhwcmUAAKA+uWTDjq+vr0JDQ5WSkiJJSklJUWhoaIWnsAAAwOXBxbIsy9lFVNXevXs1adIk5ebmqmnTpkpISNA111zj7LIAAEA9ckmHHQAAgPJcstNYAAAAFUHYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdXBb27dunwYMHKyYmRoMHD9b+/fudXRKAGpaQkKCoqCiFhITo+++/d3Y5qEcIO7gsTJkyRbGxsUpNTVVsbKzi4uKcXRKAGhYdHa3FixcrKCjI2aWgniHswHjZ2dnavXu3Bg4cKEkaOHCgdu/erWPHjjm5MgA1KTIykkcGoUyEHRgvMzNT/v7+cnV1lSS5urqqRYsWyszMdHJlAIC6QNgBAABGI+zAeAEBATp8+LAKCwslSYWFhTpy5AjD3QBwmSDswHi+vr4KDQ1VSkqKJCklJUWhoaHy8fFxcmUAgLrAg0BxWdi7d68mTZqk3NxcNW3aVAkJCbrmmmucXRaAGjR16lStW7dOWVlZat68uby9vbV69Wpnl4V6gLADAACMxjQWAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAakx4eLjjT/v27dWpUyfH1//5z38q3d+WLVvUvXv3Eg9tzcvL04ABA7RkyZKaLF2SlJycrLvuukvh4eHq2bOnRo4cqfT0dElSUlKSnnnmmVLHhISE6MCBA459Onbs6LjmmJgYxcfH68iRIzVeK4CKI+wAqDFffvml409gYKDmzp3r+PqPf/xjpfvr0aOH+vTpo2nTpjm2zZ49W35+fhoyZEiN1FxQUCBJWrhwoV544QWNHj1aW7Zs0ccff6zY2FitX7++Uv0NGDBAX375pbZv366ZM2cqKytLd911F4EHcCLCDoBaZ7fbNX/+fPXt21ddu3bVuHHjlJOTI0k6dOiQQkJCtGLFCvXu3Vtdu3bVnDlzHMf+5S9/0fbt27Vx40Z9//33Wrx4saZNm6aTJ0/qr3/9q3r27KlevXrp5Zdfdjz/7ODBgxo+fLi6du2qrl276umnn1Zubq6jz6ioKM2fP1+33367rr/+ep04cUKvvfaa4uLidMstt8jT01Nubm6KiorSxIkTq3TNbm5u+sMf/qCXX35ZPj4+WrhwYTVeQQDVQdgBUOvefvttpaWl6Z133tGmTZvUrFkzxcfHl9jn888/19q1a/XWW29p1qxZ2rt3ryTJy8tLf//73zVlyhT99a9/1RNPPKFWrVpp0qRJatCggdatW6eVK1dqy5Yt+ve//y1JsixLo0aN0qZNm/Thhx/q119/VVJSUonzrV69WvPnz1d6erq+/PJLnT17Vv369avxa3d1dVV0dLRjOgxA3Wvg7AIAmG/p0qWKi4tTy5YtJUljxoxRnz59HFNIRdsaNmyo9u3bq3379tqzZ4/atm0r6dxIzMqVK3Xo0CENHz5cWVlZ+uSTT5Senq6GDRvK09NTDz30kN577z0NGTJEV199ta6++mpJko+Pjx5++GHNnDmzRE3Dhg1zPPk+JydHzZs3V4MGF/+RuHbtWm3cuLHS19+iRQv99ttvlT4OQM0g7ACodRkZGXriiSdks/3fYLLNZlN2drbj6yuuuMLx90aNGun3338v0ccf/vAHubu7y2azKSMjQwUFBerZs6ej3W63O8JLVlaWpk2bpvT0dJ06dUqWZalp06Yl+ivaV5K8vb11/PhxFRQUXDTw9O/fXzNmzCixLSQkpNzrP3z4sJo1a1bufgBqB2EHQK1r2bKlXnjhBXXu3LlU26FDh6rUn7u7u7Zt21ZmOHnppZfk4uKi5ORkeXt7Ky0trdS0mYuLi+Pv4eHhcnd3V1pamvr371/pei7Gbrfr448/1o033lij/QKoONbsAKh1999/v1555RX98ssvkqRjx44pLS2tyv21aNFCPXr00P/8z//o5MmTstvtOnjwoLZv3y5JOnXqlDw9PeXl5aXDhw9rwYIFF+3Py8tLTz75pOLj45WWlqbTp08rPz9fn3zyiRITE6tUY0FBgfbu3avx48crKytLDz30UJX6AVB9jOwAqHXDhw+XZVkaMWKEjhw5Il9fX916663q27dvlftMTEzUjBkzdOutt+rUqVNq1aqVHn30UUnn1v9MnDhRkZGRuuqqq3THHXfozTffvGh/I0aM0BVXXKHZs2frmWeeUePGjdWxY0eNHj26UnV9+OGHWr9+vSzLUosWLXTjjTdq+fLl8vf3r+qlAqgmF8uyLGcXAQAAUFuYxgIAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARvt/6mRsKIrpuxkAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Visualising dependent variable 'TenYearCHD' using countplot and Pie-chart\n",
+        "plt.figure(figsize= (9, 6))\n",
+        "sns.countplot(x= 'TenYearCHD', data= crp_df)\n",
+        "plt.title('Cardiovascular Risk rate',size = 20)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 487
+        },
+        "id": "qH8WeZ6bBV9A",
+        "outputId": "9c17a126-338c-49fd-c8b2-78c2b352fe60"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x576 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Pie Chart for Type column\n",
+        "plt.figure(figsize=(10,8))\n",
+        "labels = crp_df['TenYearCHD'].value_counts(sort = True).index\n",
+        "sizes = crp_df['TenYearCHD'].value_counts(sort = True)\n",
+        "colors = [\"#98FB98\",\"#C21807\"]\n",
+        "explode = (0.1,0)\n",
+        "plt.pie(sizes, explode= explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=0)\n",
+        "plt.title('Percentage of Cardiovascular Risk rate',size = 20)\n",
+        "plt.legend(labels= labels, bbox_to_anchor=(1,0.5))\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zRYLrmaoDoY0"
+      },
+      "source": [
+        "**Findings:**\n",
+        "\n",
+        "* It's clearly visible that there is a class imbalance problem with our datset!\n",
+        "* Number of people without the disease greatly exceeds the number of people with the disease.\n",
+        "* An imbalance occurs when one or more classes have very low proportions in the training data as compared to the other classes.\n",
+        "\n",
+        "* Problems with Class Imbalance problem is that the abundance of examples from the majority class (or classes) can swamp the minority class. Most machine learning algorithms for classification predictive models are designed and demonstrated on problems that assume an equal distribution of classes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "icZ-IcrmNNtg",
+        "outputId": "8a1cc4fc-0a01-45cb-ac81-8c9eef1fc917"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   age  sex  is_smoking  cigsPerDay  BPMeds  prevalentStroke  prevalentHyp  \\\n",
+              "0   64    0           1         3.0     0.0                0             0   \n",
+              "1   36    1           0         0.0     0.0                0             1   \n",
+              "2   46    0           1        10.0     0.0                0             0   \n",
+              "3   50    1           1        20.0     0.0                0             1   \n",
+              "4   64    0           1        30.0     0.0                0             0   \n",
+              "\n",
+              "   diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  TenYearCHD  \n",
+              "0         0    221.0  148.0   85.0  25.38       90.0     80.0           1  \n",
+              "1         0    212.0  168.0   98.0  29.77       72.0     75.0           0  \n",
+              "2         0    250.0  116.0   71.0  20.35       88.0     94.0           0  \n",
+              "3         0    233.0  158.0   88.0  28.26       68.0     94.0           1  \n",
+              "4         0    241.0  136.5   85.0  26.42       70.0     77.0           0  "
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+              "      <th></th>\n",
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+              "      <td>72.0</td>\n",
+              "      <td>75.0</td>\n",
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+              "      <td>250.0</td>\n",
+              "      <td>116.0</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>20.35</td>\n",
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+              "      <td>94.0</td>\n",
+              "      <td>0</td>\n",
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+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>50</td>\n",
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+              "      <td>1</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>233.0</td>\n",
+              "      <td>158.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>28.26</td>\n",
+              "      <td>68.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
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+              "              title=\"Convert this dataframe to an interactive table.\"\n",
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+              "    .colab-df-convert:hover {\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-ad33b141-bde5-4c97-83f0-5377969f9a4c button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-ad33b141-bde5-4c97-83f0-5377969f9a4c');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
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+              "          element.appendChild(docLink);\n",
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+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ],
+      "source": [
+        "crp_df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 320
+        },
+        "id": "ZB33GNlnNYry",
+        "outputId": "9b46cfd6-32e0-42c0-9459-143037880a71"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Count of Male VS Female')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#Visualising the 'Sex' feature using Seaborn countplot method\n",
+        "\n",
+        "sns.countplot(x= 'sex', data= crp_df )\n",
+        "plt.title('Count of Male VS Female', size= 15, weight='bold')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ddplFsnnNtvk"
+      },
+      "source": [
+        "In our dataset the records of female population are more than that of men."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 320
+        },
+        "id": "XOz-fxtyObCI",
+        "outputId": "d2dfd3c7-8148-40ac-a67c-f0eded5f948a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'No of people smoking vs not smoking')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Peple smoking vs not smoking(1: Yes, 0: No)\n",
+        "sns.set(style='darkgrid')\n",
+        "sns.countplot(x= 'is_smoking', data= crp_df )\n",
+        "plt.title('No of people smoking vs not smoking', size= 15,  weight='bold')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sKTVhcvxPgxF"
+      },
+      "source": [
+        "Number of people who smokes vs people who doesn't is almost similar in the dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 646
+        },
+        "id": "_Kx3pP_pQW1M",
+        "outputId": "cfa17b8c-3996-45ea-ba2e-a512cc643dff"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'No of cigrates consumed per day')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1224x720 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#visualizing 'cigsPerDay' column\n",
+        "plt.figure(figsize= (17, 10))\n",
+        "sns.countplot(x= 'cigsPerDay', data= crp_df)\n",
+        "plt.title('No of cigrates consumed per day', size= 15,  weight='bold')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r6Aa09xGRqqZ"
+      },
+      "source": [
+        "From the above countplot it can be said that most of the people don't smoke cigrates. Among people who smokes cigrates, most of them smokes 20 cigrates per day, followed by 30 cigrates , 15 cigrates etc."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "jhTHUMmLQsyN",
+        "outputId": "a4922ed1-b8db-4ab8-e09d-c30f1a4799d3"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   age  sex  is_smoking  cigsPerDay  BPMeds  prevalentStroke  prevalentHyp  \\\n",
+              "0   64    0           1         3.0     0.0                0             0   \n",
+              "1   36    1           0         0.0     0.0                0             1   \n",
+              "2   46    0           1        10.0     0.0                0             0   \n",
+              "3   50    1           1        20.0     0.0                0             1   \n",
+              "4   64    0           1        30.0     0.0                0             0   \n",
+              "\n",
+              "   diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  TenYearCHD  \n",
+              "0         0    221.0  148.0   85.0  25.38       90.0     80.0           1  \n",
+              "1         0    212.0  168.0   98.0  29.77       72.0     75.0           0  \n",
+              "2         0    250.0  116.0   71.0  20.35       88.0     94.0           0  \n",
+              "3         0    233.0  158.0   88.0  28.26       68.0     94.0           1  \n",
+              "4         0    241.0  136.5   85.0  26.42       70.0     77.0           0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-13e22ddc-8f22-4d1b-b853-f843cc06c72c\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>is_smoking</th>\n",
+              "      <th>cigsPerDay</th>\n",
+              "      <th>BPMeds</th>\n",
+              "      <th>prevalentStroke</th>\n",
+              "      <th>prevalentHyp</th>\n",
+              "      <th>diabetes</th>\n",
+              "      <th>totChol</th>\n",
+              "      <th>sysBP</th>\n",
+              "      <th>diaBP</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>heartRate</th>\n",
+              "      <th>glucose</th>\n",
+              "      <th>TenYearCHD</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>221.0</td>\n",
+              "      <td>148.0</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>25.38</td>\n",
+              "      <td>90.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>36</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>212.0</td>\n",
+              "      <td>168.0</td>\n",
+              "      <td>98.0</td>\n",
+              "      <td>29.77</td>\n",
+              "      <td>72.0</td>\n",
+              "      <td>75.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>46</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>250.0</td>\n",
+              "      <td>116.0</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>20.35</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>50</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>233.0</td>\n",
+              "      <td>158.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>28.26</td>\n",
+              "      <td>68.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>241.0</td>\n",
+              "      <td>136.5</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>26.42</td>\n",
+              "      <td>70.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13e22ddc-8f22-4d1b-b853-f843cc06c72c')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-13e22ddc-8f22-4d1b-b853-f843cc06c72c button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-13e22ddc-8f22-4d1b-b853-f843cc06c72c');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ],
+      "source": [
+        "crp_df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 374
+        },
+        "id": "R91X-15oSBYG",
+        "outputId": "a65c0fef-8eee-44fa-e0bf-7c91b954002a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Count of people who previously had Stroke')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#Visualizing 'prevalentStroke' feature using Sns countplot method\n",
+        "plt.figure(figsize= (8, 5))\n",
+        "sns.countplot(x= 'prevalentStroke', data= crp_df )\n",
+        "plt.title('Count of people who previously had Stroke', size= 15,weight='bold' )"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "W3SqzrqFSvvy",
+        "outputId": "6e6df91e-24ba-4ba6-f697-673e468ab581"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    3368\n",
+              "1      22\n",
+              "Name: prevalentStroke, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ],
+      "source": [
+        "#Checking the count of categories under 'prevalentStroke' feature\n",
+        "crp_df['prevalentStroke'].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LknWqM9YTiXN"
+      },
+      "source": [
+        "Number of people having Stroke in past is very less which is 22 vs number of people who didn't have stroke."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 374
+        },
+        "id": "KIRNV9aHXCX2",
+        "outputId": "42006b8f-3615-417b-b6ea-ce0e6802bd6e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Count of people who previously had Hypertension')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#Visualizing 'prevalentHyp' feature\n",
+        "plt.figure(figsize= (8, 5))\n",
+        "sns.countplot(x= 'prevalentHyp', data= crp_df )\n",
+        "plt.title('Count of people who previously had Hypertension', size= 15, weight='bold')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tPitumilXKtf",
+        "outputId": "820bbef5-8dc9-4318-9039-0adb89f1ff72"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    2321\n",
+              "1    1069\n",
+              "Name: prevalentHyp, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ],
+      "source": [
+        "#Checking the count of categories under 'prevalentHyp' feature\n",
+        "crp_df['prevalentHyp'].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7IT0D7A4Xi1A"
+      },
+      "source": [
+        "* Out of 3390 patients from the records 1069 people had Hypertension before.\n",
+        "\n",
+        "* In Medical terms High blood pressure (hypertension) is a common condition in which the long-term force of the blood against your artery walls is high enough that it may eventually cause health problems, such as heart disease.\n",
+        "\n",
+        "* From medical point of view it can be said that people with hypertension are more likely to have heart disease in future."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 374
+        },
+        "id": "LLC1Gjn0ZDL7",
+        "outputId": "4ee0d9ef-9e59-4097-e8b0-952622052404"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Count of people who had diabetes')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 35
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Visualizing 'diabetes' column\n",
+        "plt.figure(figsize= (8, 5))\n",
+        "sns.countplot(x= 'diabetes', data= crp_df )\n",
+        "plt.title('Count of people who had diabetes', size= 15, weight='bold')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pBLpK62bacEY",
+        "outputId": "e2d34465-715c-4d58-c615-8ebfc222825a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    3303\n",
+              "1      87\n",
+              "Name: diabetes, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ],
+      "source": [
+        "#Checking the count of categories under 'diabetes' feature\n",
+        "crp_df['diabetes'].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NdP3KkK9aiqA"
+      },
+      "source": [
+        "* Out of 3390 patients from our records 87 people had diabetes.\n",
+        "\n",
+        "* **Interesting fact about diabetes is that if a person have diabetes, then they are twice as likely to have heart disease or a stroke than someone who doesn't have diabetes—and at a younger age. The longer anyone have diabetes, the more likely they are to have heart disease.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 576
+        },
+        "id": "Ywo8qjF24NsJ",
+        "outputId": "65d0afc8-aaa0-487a-b92c-41dc7ee67769"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 936x720 with 7 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Barplot of continious variables\n",
+        "cont_features= ['age','totChol', 'sysBP', 'diaBP', 'BMI', 'heartRate', 'glucose']\n",
+        "\n",
+        "plt.figure(figsize= (13,10))\n",
+        "for n, col in enumerate(cont_features):\n",
+        "  ax=plt.subplot(4, 3, n+1)\n",
+        "  # fig, ax = plt.subplots()\n",
+        "  sns.boxplot(crp_df[col], ax= ax, palette='deep')\n",
+        "  plt.title(f'{col.title()}',weight='bold')\n",
+        "  plt.tight_layout()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3xzWmYRf--W8"
+      },
+      "source": [
+        "**Findings:**\n",
+        "\n",
+        "- The median age of the patients is 49.\n",
+        "\n",
+        "- Except age all other continious features have outliers."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zeA8kgLMcKur"
+      },
+      "source": [
+        "## Bivariate & Multivariate Analysis"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 357
+        },
+        "id": "xNM0YComwJPt",
+        "outputId": "5d64bc87-0c46-445f-f310-53fc985a51a1"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Percentage of Cardiovascular Risk with or without diabetes\n",
+        "freq_df = crp_df.groupby('TenYearCHD')['diabetes'].value_counts().unstack(0)\n",
+        "pct_df = freq_df.divide(freq_df.sum(axis=1), axis=0)*100\n",
+        "pct_df.plot(kind=\"bar\", figsize=(8, 5))\n",
+        "plt.ylim(0,100)\n",
+        "plt.title('CardioVascular Heart Disease Vs Diabetes', size= 15, weight= 'bold')\n",
+        "plt.ylabel('Percentage')\n",
+        "plt.xticks(rotation=0)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CWRy8axrWOeC"
+      },
+      "source": [
+        "From the above barplot it's quite evident that diabetic patients tend to have higher 10 year Risk of CHD."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 357
+        },
+        "id": "vGGlWjIP4Y6i",
+        "outputId": "d0cb0f6b-1a15-4ab8-ae5b-ca855729c6fd"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Percentage of Cardiovascular Risk with or without previous hypertension\n",
+        "freq_df = crp_df.groupby('TenYearCHD')['prevalentHyp'].value_counts().unstack(0)\n",
+        "pct_df = freq_df.divide(freq_df.sum(axis=1), axis=0)*100\n",
+        "pct_df.plot(kind=\"bar\", figsize=(8, 5))\n",
+        "plt.ylim(0,100)\n",
+        "plt.title('CardioVascular Heart Disease Vs Hypertension', size= 15, weight= 'bold' )\n",
+        "plt.ylabel('Percentage')\n",
+        "plt.xticks(rotation=0)\n",
+        "plt.legend(title='TenYearCHD', loc='upper right')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IevUVdy0XCYS"
+      },
+      "source": [
+        "Hypertensive patients tend to have higher risk of 10 Year CHD."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 374
+        },
+        "id": "Pj94X-vb45oO",
+        "outputId": "552ac3ea-5699-4f46-f062-263f82af3598"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([0, 1]), <a list of 2 Text major ticklabel objects>)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Percentage of Cardiovascular Risk with or without previous stroke\n",
+        "freq_df = crp_df.groupby('TenYearCHD')['prevalentStroke'].value_counts().unstack(0)\n",
+        "pct_df = freq_df.divide(freq_df.sum(axis=1), axis=0)*100\n",
+        "pct_df.plot(kind=\"bar\",  figsize=(8, 5))\n",
+        "plt.ylim(0,100)\n",
+        "plt.title('CardioVascular Heart Disease Vs Prevalent Stroke:', size= 15, weight= 'bold' )\n",
+        "plt.ylabel('Percentage')\n",
+        "plt.xticks(rotation=0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AcUjpSc2XW-G"
+      },
+      "source": [
+        "Patients with prevalent stroke symptoms have a high 10-year risk of CHD."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 357
+        },
+        "id": "jL-tFfsy5Rts",
+        "outputId": "276cbe0f-270c-44f6-f18b-780d052e6d9d"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Percentage of Cardiovascular Risk with or without Blood Pressure Medication\n",
+        "freq_df = crp_df.groupby('TenYearCHD')['BPMeds'].value_counts().unstack(0)\n",
+        "pct_df = freq_df.divide(freq_df.sum(axis=1), axis=0)*100\n",
+        "pct_df.plot(kind=\"bar\", figsize=(8, 5))\n",
+        "plt.title('CardioVascular Heart Disease Vs BP Meds', size= 15, weight= 'bold' )\n",
+        "plt.ylim(0,100)\n",
+        "plt.ylabel('Percentage')\n",
+        "plt.legend(title='TenYearCHD', loc='upper right')\n",
+        "plt.xticks(rotation=0)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_2OTjHXsX4KP"
+      },
+      "source": [
+        "Patients on Blood Preasure medication have higer risks of getting 10 year CHD as compared to patients who are not on medications."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 374
+        },
+        "id": "EI99LskrYmrr",
+        "outputId": "65ae992d-30bb-4591-e690-f51e3cae4775"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([0, 1]), <a list of 2 Text major ticklabel objects>)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 43
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Percentage of Cardiovascular Risk on smokers vs non smokers\n",
+        "freq_df = crp_df.groupby('TenYearCHD')['is_smoking'].value_counts().unstack(0)\n",
+        "pct_df = freq_df.divide(freq_df.sum(axis=1), axis=0)*100\n",
+        "pct_df.plot(kind=\"bar\", figsize=(8, 5))\n",
+        "plt.title('CardioVascular Heart Disease Vs Is_smoking', size= 15, weight= 'bold' )\n",
+        "plt.ylim(0,100)\n",
+        "plt.ylabel('Percentage')\n",
+        "plt.xticks(rotation=0)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7leB5MQCZkNG"
+      },
+      "source": [
+        "Patients who are smokers vs non-smokers shows similar risks of 10 Year CHD as per the above bar plot"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 374
+        },
+        "id": "kjFbywvpZ_mz",
+        "outputId": "392a80cc-92ae-4053-bc97-f0b26eb925d7"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([0, 1]), <a list of 2 Text major ticklabel objects>)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Percentage of Cardiovascular Risk on male Vs female\n",
+        "freq_df = crp_df.groupby('TenYearCHD')['sex'].value_counts().unstack(0)\n",
+        "pct_df = freq_df.divide(freq_df.sum(axis=1), axis=0)*100\n",
+        "pct_df.plot(kind=\"bar\", figsize=(8, 5))\n",
+        "plt.title('CardioVascular Heart Disease Vs Sex', size= 15, weight= 'bold' )\n",
+        "plt.ylim(0,100)\n",
+        "plt.ylabel('Percentage')\n",
+        "plt.xticks(rotation=0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RstAHPzcaPLe"
+      },
+      "source": [
+        "As per the above visualization Males has slight higher risk of having 10 Year CHD."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "U967XmQP3HgZ",
+        "outputId": "380e0568-2896-4a87-de31-0d7cbee88ca5"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   age  sex  is_smoking  cigsPerDay  BPMeds  prevalentStroke  prevalentHyp  \\\n",
+              "0   64    0           1         3.0     0.0                0             0   \n",
+              "1   36    1           0         0.0     0.0                0             1   \n",
+              "2   46    0           1        10.0     0.0                0             0   \n",
+              "3   50    1           1        20.0     0.0                0             1   \n",
+              "4   64    0           1        30.0     0.0                0             0   \n",
+              "\n",
+              "   diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  TenYearCHD  \n",
+              "0         0    221.0  148.0   85.0  25.38       90.0     80.0           1  \n",
+              "1         0    212.0  168.0   98.0  29.77       72.0     75.0           0  \n",
+              "2         0    250.0  116.0   71.0  20.35       88.0     94.0           0  \n",
+              "3         0    233.0  158.0   88.0  28.26       68.0     94.0           1  \n",
+              "4         0    241.0  136.5   85.0  26.42       70.0     77.0           0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-97917276-24c5-4a01-9799-6501df4a9862\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>is_smoking</th>\n",
+              "      <th>cigsPerDay</th>\n",
+              "      <th>BPMeds</th>\n",
+              "      <th>prevalentStroke</th>\n",
+              "      <th>prevalentHyp</th>\n",
+              "      <th>diabetes</th>\n",
+              "      <th>totChol</th>\n",
+              "      <th>sysBP</th>\n",
+              "      <th>diaBP</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>heartRate</th>\n",
+              "      <th>glucose</th>\n",
+              "      <th>TenYearCHD</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>221.0</td>\n",
+              "      <td>148.0</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>25.38</td>\n",
+              "      <td>90.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>36</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>212.0</td>\n",
+              "      <td>168.0</td>\n",
+              "      <td>98.0</td>\n",
+              "      <td>29.77</td>\n",
+              "      <td>72.0</td>\n",
+              "      <td>75.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>46</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>250.0</td>\n",
+              "      <td>116.0</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>20.35</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>50</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>233.0</td>\n",
+              "      <td>158.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>28.26</td>\n",
+              "      <td>68.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>241.0</td>\n",
+              "      <td>136.5</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>26.42</td>\n",
+              "      <td>70.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97917276-24c5-4a01-9799-6501df4a9862')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-97917276-24c5-4a01-9799-6501df4a9862 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-97917276-24c5-4a01-9799-6501df4a9862');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ],
+      "source": [
+        "crp_df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 568
+        },
+        "id": "bbgaApt-C7-u",
+        "outputId": "4ae2c046-afaf-4461-b800-07859fe45019"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x576 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#Checking the coorelation between all the features\n",
+        "plt.figure(figsize=(15,8))\n",
+        "sns.heatmap(crp_df.corr(), annot = True)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iELnMrhhDGz6"
+      },
+      "source": [
+        "From the above correlation plot we can conclude that,\n",
+        "\n",
+        "* There are no features with more than 0.5 correlation with the Ten year risk of developing CHD and this shows that the features a poor predictors. However the features with the highest correlations are age, prevalent hypertension(prevalentHyp) and systolic blood pressure(sysBP).\n",
+        "\n",
+        "* Also there are a couple of features that are highly correlated with each other and for model building it's best to pick features which are independent of each other.\n",
+        "\n",
+        "Coorelated independent features includes:\n",
+        "\n",
+        "* Blood glucose and diabetes\n",
+        "* systolic and diastolic blood pressures\n",
+        "* cigarette smoking and the number of cigarretes smoked per day\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dzvwAjHWGJv2"
+      },
+      "source": [
+        "# **Feature Engineering**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WdrHqz6JEcDa"
+      },
+      "outputs": [],
+      "source": [
+        "#Creating a copy of orignal Dataset\n",
+        "df2= crp_df.copy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_YiVb0AZEjzA"
+      },
+      "outputs": [],
+      "source": [
+        "# Creating new feature avgBP from sysBP and diaBP\n",
+        "df2['avg_BP']= (df2['sysBP'] + df2['diaBP'])/2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 270
+        },
+        "id": "xdCETmYVFBbn",
+        "outputId": "f885908d-6a77-493b-d77b-2138b34043d8"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   age  sex  is_smoking  cigsPerDay  BPMeds  prevalentStroke  prevalentHyp  \\\n",
+              "0   64    0           1         3.0     0.0                0             0   \n",
+              "1   36    1           0         0.0     0.0                0             1   \n",
+              "2   46    0           1        10.0     0.0                0             0   \n",
+              "3   50    1           1        20.0     0.0                0             1   \n",
+              "4   64    0           1        30.0     0.0                0             0   \n",
+              "\n",
+              "   diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  TenYearCHD  \\\n",
+              "0         0    221.0  148.0   85.0  25.38       90.0     80.0           1   \n",
+              "1         0    212.0  168.0   98.0  29.77       72.0     75.0           0   \n",
+              "2         0    250.0  116.0   71.0  20.35       88.0     94.0           0   \n",
+              "3         0    233.0  158.0   88.0  28.26       68.0     94.0           1   \n",
+              "4         0    241.0  136.5   85.0  26.42       70.0     77.0           0   \n",
+              "\n",
+              "   avg_BP  \n",
+              "0  116.50  \n",
+              "1  133.00  \n",
+              "2   93.50  \n",
+              "3  123.00  \n",
+              "4  110.75  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-60011451-dcff-4c13-adad-d8909560cb42\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>is_smoking</th>\n",
+              "      <th>cigsPerDay</th>\n",
+              "      <th>BPMeds</th>\n",
+              "      <th>prevalentStroke</th>\n",
+              "      <th>prevalentHyp</th>\n",
+              "      <th>diabetes</th>\n",
+              "      <th>totChol</th>\n",
+              "      <th>sysBP</th>\n",
+              "      <th>diaBP</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>heartRate</th>\n",
+              "      <th>glucose</th>\n",
+              "      <th>TenYearCHD</th>\n",
+              "      <th>avg_BP</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>221.0</td>\n",
+              "      <td>148.0</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>25.38</td>\n",
+              "      <td>90.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>116.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>36</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>212.0</td>\n",
+              "      <td>168.0</td>\n",
+              "      <td>98.0</td>\n",
+              "      <td>29.77</td>\n",
+              "      <td>72.0</td>\n",
+              "      <td>75.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>133.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>46</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>250.0</td>\n",
+              "      <td>116.0</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>20.35</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>93.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>50</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>233.0</td>\n",
+              "      <td>158.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>28.26</td>\n",
+              "      <td>68.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>123.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>241.0</td>\n",
+              "      <td>136.5</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>26.42</td>\n",
+              "      <td>70.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>110.75</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60011451-dcff-4c13-adad-d8909560cb42')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-60011451-dcff-4c13-adad-d8909560cb42 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-60011451-dcff-4c13-adad-d8909560cb42');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 50
+        }
+      ],
+      "source": [
+        "df2.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_FNq6XspFFtX"
+      },
+      "outputs": [],
+      "source": [
+        "#Dropping sysBP and diaBP feature\n",
+        "df2.drop(['sysBP', 'diaBP'], axis= 1, inplace= True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "nboccN9mFSC5",
+        "outputId": "3a6ef083-1e5c-4ae1-f4b1-77531a9502c8"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   age  sex  is_smoking  cigsPerDay  BPMeds  prevalentStroke  prevalentHyp  \\\n",
+              "0   64    0           1         3.0     0.0                0             0   \n",
+              "1   36    1           0         0.0     0.0                0             1   \n",
+              "2   46    0           1        10.0     0.0                0             0   \n",
+              "3   50    1           1        20.0     0.0                0             1   \n",
+              "4   64    0           1        30.0     0.0                0             0   \n",
+              "\n",
+              "   diabetes  totChol    BMI  heartRate  glucose  TenYearCHD  avg_BP  \n",
+              "0         0    221.0  25.38       90.0     80.0           1  116.50  \n",
+              "1         0    212.0  29.77       72.0     75.0           0  133.00  \n",
+              "2         0    250.0  20.35       88.0     94.0           0   93.50  \n",
+              "3         0    233.0  28.26       68.0     94.0           1  123.00  \n",
+              "4         0    241.0  26.42       70.0     77.0           0  110.75  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-90d5ce2a-2e95-4e41-bb1a-302435321fc9\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>is_smoking</th>\n",
+              "      <th>cigsPerDay</th>\n",
+              "      <th>BPMeds</th>\n",
+              "      <th>prevalentStroke</th>\n",
+              "      <th>prevalentHyp</th>\n",
+              "      <th>diabetes</th>\n",
+              "      <th>totChol</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>heartRate</th>\n",
+              "      <th>glucose</th>\n",
+              "      <th>TenYearCHD</th>\n",
+              "      <th>avg_BP</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
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+              "      <td>0</td>\n",
+              "      <td>221.0</td>\n",
+              "      <td>25.38</td>\n",
+              "      <td>90.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>116.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>36</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>212.0</td>\n",
+              "      <td>29.77</td>\n",
+              "      <td>72.0</td>\n",
+              "      <td>75.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>133.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>46</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>250.0</td>\n",
+              "      <td>20.35</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>93.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>50</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>233.0</td>\n",
+              "      <td>28.26</td>\n",
+              "      <td>68.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>123.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>241.0</td>\n",
+              "      <td>26.42</td>\n",
+              "      <td>70.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>110.75</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90d5ce2a-2e95-4e41-bb1a-302435321fc9')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-90d5ce2a-2e95-4e41-bb1a-302435321fc9 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-90d5ce2a-2e95-4e41-bb1a-302435321fc9');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 52
+        }
+      ],
+      "source": [
+        "df2.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ok6ObjS6azi7"
+      },
+      "source": [
+        "# **Handling Class Imbalance Problem**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CVSXdHhHdShf"
+      },
+      "source": [
+        "Since our dependent class is imbalanced i.e for every positive case there are about 5-6 negative cases. We may end up with a classifier that is biased to the negative cases. The classifier may have a high accuracy but poor a precision and recall.\n",
+        "\n",
+        "To handle this problem we will balance the dataset using the Synthetic Minority Oversampling Technique (SMOTE).\n",
+        "\n",
+        "**SMOTE :**\n",
+        "\n",
+        "SMOTE (Synthetic Minority Oversampling Technique) works by randomly picking a point from the minority class and computing the k-nearest neighbors for this point. The synthetic points are then added between the chosen point and its neighbors.\n",
+        "\n",
+        "**SMOTE** algorithm works in 4 simple steps:\n",
+        "\n",
+        "* Choose a minority class as the input vector\n",
+        "* Find its k nearest neighbors (k_neighbors is specified as an argument in the SMOTE() function)\n",
+        "* Choose one of these neighbors and place a synthetic point anywhere on the line joining the point under consideration and its chosen neighbor\n",
+        "* Repeat the steps until data is balanced"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b03HTw8bm9pT"
+      },
+      "outputs": [],
+      "source": [
+        "#define the features\n",
+        "X = df2.drop('TenYearCHD', axis= 1)\n",
+        "y = df2['TenYearCHD']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-m3F5PeazEjy",
+        "outputId": "8c0a1dd2-6030-4874-b101-8eea06c0f375"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "13"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 54
+        }
+      ],
+      "source": [
+        "#Total Number of independent features\n",
+        "len(X.columns)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "X_P0_DwJzHkm",
+        "outputId": "795724df-ce51-4efb-fd80-0ddfa2db0d86"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Original dataset shape 3390\n",
+            "Resampled dataset shape 5758\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Applying SMOTE technique to handle imbalance target class\n",
+        "from imblearn.over_sampling import SMOTE\n",
+        "\n",
+        "smote = SMOTE()\n",
+        "\n",
+        "# fit predictor and target variable\n",
+        "x_smote, y_smote = smote.fit_resample(X, y)\n",
+        "\n",
+        "print('Original dataset shape', len(df2))\n",
+        "print('Resampled dataset shape', len(y_smote))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EKQhJdwK2iWS",
+        "outputId": "c40d74d2-8d40-4bb3-d796-e234a2920c7a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(5758, 13)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 67
+        }
+      ],
+      "source": [
+        "#Checking shape of independent features after applying SMOTE\n",
+        "x_smote.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TVD_dD2vYwo6",
+        "outputId": "8d761f86-ea0d-4646-8178-98cfd555b564"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(5758,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 57
+        }
+      ],
+      "source": [
+        "#Checking shape of Dependent variable after applying SMOTE\n",
+        "y_smote.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NXmxVCD41CBb"
+      },
+      "outputs": [],
+      "source": [
+        "#Spilliting the data into train and validation sets\n",
+        "X_train, X_test, y_train, y_test= train_test_split(x_smote, y_smote, test_size = 0.2, random_state = 42)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Nn7rCSsh32Pv"
+      },
+      "outputs": [],
+      "source": [
+        "# Function to get train and test accuracy and Roc score!\n",
+        "def evaluation_metrics(train_preds, test_preds):\n",
+        "\n",
+        "  train_accuracy = accuracy_score(y_train,train_preds)\n",
+        "  test_accuracy = accuracy_score(y_test,test_preds)\n",
+        "  train_aoc= roc_auc_score(y_train,train_preds)\n",
+        "  test_aoc= roc_auc_score(y_test,test_preds)\n",
+        "\n",
+        "  print(\"The accuracy on train data is \", train_accuracy)\n",
+        "  print(\"The accuracy on test data is \", test_accuracy)\n",
+        "\n",
+        "  print(\"\\nThe aoc score on train data is:\", train_aoc)\n",
+        "  print(\"The aoc score on test data is:\", test_aoc)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-DqgLvivvxxf"
+      },
+      "outputs": [],
+      "source": [
+        "#Function to get Confusion matrix\n",
+        "\n",
+        "def conf_mtrx( test_preds):\n",
+        "\n",
+        "  cm=confusion_matrix(y_test, test_preds)\n",
+        "  conf_matrix=pd.DataFrame(data=cm,columns=['Predicted:0','Predicted:1'],index=['Actual:0','Actual:1'])\n",
+        "  plt.figure(figsize = (8,5))\n",
+        "  sns.heatmap(conf_matrix, annot=True,fmt='d',cmap=\"YlGnBu\")\n",
+        "  plt.title('Confusion Matrix', weight= 'bold')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Agbgqe1Sw4s4"
+      },
+      "outputs": [],
+      "source": [
+        "#Function to get the AUC curve\n",
+        "\n",
+        "def roc_auc(clf):\n",
+        "\n",
+        "  # ROC curve and AUC\n",
+        "  probs = clf.predict_proba(X_test)\n",
+        "  # keep probabilities for the positive outcome only\n",
+        "  probs = probs[:, 1]\n",
+        "  # calculate AUC\n",
+        "  log_auc = roc_auc_score(y_test, probs)\n",
+        "\n",
+        "  # calculate roc curve\n",
+        "  fpr, tpr, thresholds = roc_curve(y_test, probs)\n",
+        "  # plot curve\n",
+        "  sns.set_style('whitegrid')\n",
+        "  plt.figure(figsize=(10,6))\n",
+        "  plt.plot([0, 1], [0, 1], linestyle='--')\n",
+        "  plt.plot(fpr, tpr, marker='.')\n",
+        "  plt.ylabel('True positive rate')\n",
+        "  plt.xlabel('False positive rate')\n",
+        "  plt.title(f\"AUC = {round(log_auc,3)}\", weight= 'bold')\n",
+        "  plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "T-VZcI3HFaPj"
+      },
+      "source": [
+        "#Supervised ML Classification Models"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2Wd7ra3gFjqw"
+      },
+      "source": [
+        "The five algorithms that we will be using are:\n",
+        "\n",
+        "1. **Logistic Regression**\n",
+        "2. **Decision Trees**\n",
+        "3. **Random Forrest**\n",
+        "4. **XGBoost**\n",
+        "5. **Support Vector Machine**\n",
+        "\n",
+        "\n",
+        "Here, we will be using **GridsearchCV** search algorithm for above algorithms."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rFt3gsv_1TSa"
+      },
+      "source": [
+        "## **Logistic Regression**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8YvrprGT3vh9"
+      },
+      "outputs": [],
+      "source": [
+        "# search for optimun parameters using gridsearch\n",
+        "params = {'penalty':['l1','l2'],\n",
+        "         'C' : [1e-6,1e-5,1e-4,1e-3,1e-2,1e-1,1,10,100,1e-3,1e+4,1e+5,1e+6],\n",
+        "         'class_weight':['balanced',None]}\n",
+        "logistic_clf = GridSearchCV(LogisticRegression(),param_grid=params,cv=10, scoring='roc_auc')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ezRkxx733fMZ",
+        "outputId": "9c0ec775-7b83-4415-f86d-48a6b3d7039f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LogisticRegression(max_iter=10000, random_state=42)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 71
+        }
+      ],
+      "source": [
+        "#logistic regression model\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "\n",
+        "clf_lr = LogisticRegression(fit_intercept=True, max_iter=10000, random_state= 42)\n",
+        "clf_lr.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NRALw2wt3op_",
+        "outputId": "f8640262-749d-4964-80c8-6b40c5ee118a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.687147199305254"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 72
+        }
+      ],
+      "source": [
+        "clf_lr.score(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0rsBGw7UA2hR",
+        "outputId": "84855753-72d9-41d8-faf5-fe27f711b589"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The accuracy on train data is  0.687147199305254\n",
+            "The accuracy on test data is  0.6666666666666666\n",
+            "\n",
+            "The aoc score on train data is: 0.6870943352112888\n",
+            "The aoc score on test data is: 0.6664967868591436\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Checking the Accuracy and Roc_Auc score on train and test sets\n",
+        "train_preds_lr= clf_lr.predict(X_train)\n",
+        "test_preds_lr= clf_lr.predict(X_test)\n",
+        "evaluation_metrics(train_preds_lr, test_preds_lr)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 338
+        },
+        "id": "FZY1PgNAugmE",
+        "outputId": "a17b1701-e63f-44af-85f2-91d537343e5f"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# confusion matrix of Logistic Model\n",
+        "conf_mtrx(test_preds_lr)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pyFh1IRcu2WQ",
+        "outputId": "3328f405-0822-433a-c3b4-22b2af0f549c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.67      0.67      0.67       589\n",
+            "           1       0.66      0.66      0.66       563\n",
+            "\n",
+            "    accuracy                           0.67      1152\n",
+            "   macro avg       0.67      0.67      0.67      1152\n",
+            "weighted avg       0.67      0.67      0.67      1152\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Classification Report for Logistic Regression\n",
+        "print(classification_report(y_test,test_preds_lr))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "id": "95MLKjzdxYez",
+        "outputId": "1771bd81-ecae-4547-ba17-eadb4bfbc3a9"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Roc_Auc curve for Logistic Regression model\n",
+        "roc_auc(clf_lr)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WBr14K0FEIWx"
+      },
+      "source": [
+        "## **Decision Trees**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bRlIXkkRH1oB"
+      },
+      "outputs": [],
+      "source": [
+        "#Defining the set of parameters for Decision Tree Model\n",
+        "param_dict= {'criterion': ['gini', 'entropy'],\n",
+        "                'max_depth': [4, 5, 6],\n",
+        "                'min_samples_leaf': [20, 30, 40, 50],\n",
+        "                'min_samples_split': [30, 40, 50, 100, 150]}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XoCD5utQH3ex",
+        "outputId": "e43328a1-fd5d-4762-eeaf-db0c942921b0"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(random_state=42),\n",
+              "             param_grid={'criterion': ['gini', 'entropy'],\n",
+              "                         'max_depth': [4, 5, 6],\n",
+              "                         'min_samples_leaf': [20, 30, 40, 50],\n",
+              "                         'min_samples_split': [30, 40, 50, 100, 150]})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 78
+        }
+      ],
+      "source": [
+        "#Fitting the training data to our Decision Tree Model\n",
+        "dt_grid= GridSearchCV(DecisionTreeClassifier(random_state= 42), param_grid= param_dict, cv= 5)\n",
+        "\n",
+        "dt_grid.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FJdkto2wIJaC",
+        "outputId": "f531fb15-bc51-4cb6-9250-e20501625ff8"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'criterion': 'entropy',\n",
+              " 'max_depth': 6,\n",
+              " 'min_samples_leaf': 20,\n",
+              " 'min_samples_split': 30}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 79
+        }
+      ],
+      "source": [
+        "#Best parameters of Decision Tree\n",
+        "dt_grid.best_params_"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Zpry8Y4HIPJJ",
+        "outputId": "ad4a8c59-35d7-452a-9d47-ebf76d42caa3"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.7340425531914894"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 80
+        }
+      ],
+      "source": [
+        "dt_grid.score(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2DU2e6c3JtuR",
+        "outputId": "f659a2d9-62ca-45f4-e0a3-9b8ae5b51517"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The accuracy on train data is  0.7340425531914894\n",
+            "The accuracy on test data is  0.7048611111111112\n",
+            "\n",
+            "The aoc score on train data is: 0.7335450747034113\n",
+            "The aoc score on test data is: 0.7067100513559725\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Accuracy and Roc_Auc score on train and test sets\n",
+        "\n",
+        "train_preds_dt_gd= dt_grid.predict(X_train)\n",
+        "test_preds_dt_gd= dt_grid.predict(X_test)\n",
+        "evaluation_metrics(train_preds_dt_gd, test_preds_dt_gd)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 338
+        },
+        "id": "VqaZNtG_xyMj",
+        "outputId": "81b49562-afdf-4ff2-e561-7a070f6a29c5"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#Confusion matrix of Decision Tree model\n",
+        "conf_mtrx(test_preds_dt_gd)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4TEYBom6yVoK",
+        "outputId": "ad7aa68f-ad9a-4469-ee9b-486dbfb26b23"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.76      0.62      0.68       589\n",
+            "           1       0.67      0.79      0.72       563\n",
+            "\n",
+            "    accuracy                           0.70      1152\n",
+            "   macro avg       0.71      0.71      0.70      1152\n",
+            "weighted avg       0.71      0.70      0.70      1152\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Classification report of Decision Tree model\n",
+        "print(classification_report(y_test,test_preds_dt_gd))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "id": "l_E-LQIcy6V0",
+        "outputId": "e7b81733-5727-41bf-dfb4-ea7c0a0f7e18"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# ROC and AUC curve for Decision Tree Model\n",
+        "roc_auc(dt_grid)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h5xzrA57JxI4"
+      },
+      "source": [
+        "## **Random Forest Model**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "al3M5prsLXse",
+        "outputId": "17aa132e-59fc-4ede-c688-80255e8a6dfe"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "GridSearchCV(cv=10, estimator=RandomForestClassifier(random_state=42),\n",
+              "             param_grid={'max_depth': [4, 6, 8], 'min_samples_leaf': [40, 50],\n",
+              "                         'min_samples_split': [50, 100, 150],\n",
+              "                         'n_estimators': [50, 80, 100]},\n",
+              "             scoring='roc_auc')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 84
+        }
+      ],
+      "source": [
+        "# search for optimun parameters using gridsearch for Rf model\n",
+        "params_rf = {\n",
+        "    'max_depth': [4, 6, 8],\n",
+        "    'min_samples_leaf': [40, 50],\n",
+        "    'min_samples_split': [50, 100, 150],\n",
+        "    'n_estimators': [50, 80, 100]\n",
+        "  }\n",
+        "\n",
+        "random_clf = GridSearchCV(RandomForestClassifier(random_state= 42),param_grid= params_rf,cv=10, scoring='roc_auc')\n",
+        "random_clf.fit(X_train,y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SbIPrIfyMLRl",
+        "outputId": "c76d56b6-f013-4f79-8184-505a84f65e2d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'max_depth': 8,\n",
+              " 'min_samples_leaf': 40,\n",
+              " 'min_samples_split': 50,\n",
+              " 'n_estimators': 80}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 85
+        }
+      ],
+      "source": [
+        "#Best parameters of Random Forest model\n",
+        "random_clf.best_params_"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YQt5SA0IMjgl",
+        "outputId": "9e406ff9-0e87-47a2-f64b-be1efaae2f7c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8542259655632736"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 86
+        }
+      ],
+      "source": [
+        "random_clf.score(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "n9tVJAOnJU-a",
+        "outputId": "65e5ff58-59ad-4196-d7e1-6b0b84b69c58"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The accuracy on train data is  0.756838905775076\n",
+            "The accuracy on test data is  0.7152777777777778\n",
+            "\n",
+            "The aoc score on train data is: 0.7566375168752026\n",
+            "The aoc score on test data is: 0.7160343418564747\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Accuracy and Roc_Auc score on train and test sets\n",
+        "\n",
+        "train_preds_rf_cv= random_clf.predict(X_train)\n",
+        "test_preds_rf_cv= random_clf.predict(X_test)\n",
+        "evaluation_metrics(train_preds_rf_cv, test_preds_rf_cv)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 338
+        },
+        "id": "TC6Kj01QzfUN",
+        "outputId": "72afddce-151f-4e72-f693-48bfbcb3b247"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Confusion matrix of Random Forest\n",
+        "conf_mtrx(test_preds_rf_cv)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "M6Tl1sCmzmOX",
+        "outputId": "74af8146-161a-473b-a5c4-b524e89dca21"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.74      0.68      0.71       589\n",
+            "           1       0.69      0.75      0.72       563\n",
+            "\n",
+            "    accuracy                           0.72      1152\n",
+            "   macro avg       0.72      0.72      0.72      1152\n",
+            "weighted avg       0.72      0.72      0.72      1152\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Classification report of Random Forest model\n",
+        "print(classification_report(y_test,test_preds_rf_cv))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "id": "EU3Poddfzw0w",
+        "outputId": "fc11d109-75bc-4de1-f67d-12bac15fabb0"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# ROC curve and AUC for Random Forest Model\n",
+        "roc_auc(random_clf)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oKKmJCn-PMOF"
+      },
+      "source": [
+        "## **XG Boost**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Vzzb-PRYPSvq"
+      },
+      "outputs": [],
+      "source": [
+        "#Changing parameters again to get the best results and remove overfitting issue\n",
+        "param_dict_xgb= {\n",
+        "    'gamma' : [1, 2],\n",
+        "    'max_depth': [4],\n",
+        "    'n_estimators': [60,80,100,120],\n",
+        "    'learning_rate': [0.1,0.2]\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ijpqx-ccP7Fo",
+        "outputId": "fd8ccae0-2183-406c-9848-b95fd7624265"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "GridSearchCV(cv=5, estimator=XGBClassifier(random_state=42),\n",
+              "             param_grid={'gamma': [1, 2], 'learning_rate': [0.1, 0.2],\n",
+              "                         'max_depth': [4], 'n_estimators': [60, 80, 100, 120]},\n",
+              "             scoring='roc_auc')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 92
+        }
+      ],
+      "source": [
+        "#Fitting the training data to our XG Boost Model\n",
+        "xgb_grid = GridSearchCV(estimator=XGBClassifier(random_state= 42),\n",
+        "                       param_grid = param_dict_xgb,\n",
+        "                       cv = 5, scoring='roc_auc')\n",
+        "\n",
+        "xgb_grid.fit(X_train,y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "984BUhjxQPCW",
+        "outputId": "e2fea9e7-b6c6-4f90-f868-926493a742ae"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'gamma': 1, 'learning_rate': 0.2, 'max_depth': 4, 'n_estimators': 120}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 93
+        }
+      ],
+      "source": [
+        "#Best Parametrs after Grid Search\n",
+        "xgb_grid.best_params_"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PY9C1ypuQTFo",
+        "outputId": "0f5b12c3-0102-42da-e6d1-daf60777a249"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9818326658672157"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 94
+        }
+      ],
+      "source": [
+        "xgb_grid.score(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7qwLbHXvQXt8",
+        "outputId": "1419ab5c-86af-4d79-cb55-fe80ef98757e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The accuracy on train data is  0.9337820234476769\n",
+            "The accuracy on test data is  0.8671875\n",
+            "\n",
+            "The aoc score on train data is: 0.9339625238515434\n",
+            "The aoc score on test data is: 0.8666297756078731\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Accuracy and Roc_Auc score on train and test sets\n",
+        "train_preds_xgb= xgb_grid.predict(X_train)\n",
+        "test_preds_xgb= xgb_grid.predict(X_test)\n",
+        "evaluation_metrics(train_preds_xgb,test_preds_xgb)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 338
+        },
+        "id": "-9h-N1wy0LhB",
+        "outputId": "64c66f21-3315-43b0-9bc5-eb2c3d2bc9ab"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#Confusion Matrix of XG Boost Model\n",
+        "conf_mtrx(test_preds_xgb)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iUz2V1Tn0K0c",
+        "outputId": "a7c00840-05f6-4834-98d4-bc9a28880e78"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.83      0.88      0.86       589\n",
+            "           1       0.87      0.82      0.84       563\n",
+            "\n",
+            "    accuracy                           0.85      1152\n",
+            "   macro avg       0.85      0.85      0.85      1152\n",
+            "weighted avg       0.85      0.85      0.85      1152\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Classification Report\n",
+        "print(classification_report(y_test,test_preds_xgb))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "id": "Wh97vpVb0Uw2",
+        "outputId": "cd2fc83d-b6e9-4fae-d648-bc467cbe8f97"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# ROC curve and AUC for XG Boost Model\n",
+        "\n",
+        "roc_auc(xgb_grid)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MCvHtM5OgFiI"
+      },
+      "source": [
+        "##**Support Vector Machine**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "urGL9Lz0gI-g"
+      },
+      "outputs": [],
+      "source": [
+        "# Grid search for optimum parameters\n",
+        "Cs = [5, 6, 7]\n",
+        "gammas = [0.001, 0.01, 0.1, 1]\n",
+        "kernel= ['rbf']\n",
+        "param_grid = {'C': Cs, 'gamma' : gammas, 'kernel': kernel}\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QubmteD6gN7x",
+        "outputId": "d9349071-1958-493e-da7a-07e707a296a0"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "GridSearchCV(cv=10, estimator=SVC(probability=True, random_state=42),\n",
+              "             param_grid={'C': [5, 6, 7], 'gamma': [0.001, 0.01, 0.1, 1],\n",
+              "                         'kernel': ['rbf']})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 96
+        }
+      ],
+      "source": [
+        "#Fitting the training data to our SVM model\n",
+        "\n",
+        "svm_clf = GridSearchCV(SVC(probability=True, random_state= 42), param_grid, cv=10)\n",
+        "\n",
+        "svm_clf.fit(X_train,y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qxbzU-sUgQWN",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d1f8e1b4-a0a3-41a4-f17b-120c449e23f2"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'C': 5, 'gamma': 0.01, 'kernel': 'rbf'}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 111
+        }
+      ],
+      "source": [
+        "#Best Parameters after Grid Search\n",
+        "svm_clf.best_params_"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nM7ePQSggU9R",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4628a802-bad3-495d-e030-6a5ce2abfed5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The accuracy on train data is  0.9991315675206253\n",
+            "The accuracy on test data is  0.9236111111111112\n",
+            "\n",
+            "The aoc score on train data is: 0.9991266375545851\n",
+            "The aoc score on test data is: 0.9237289924519085\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Accuracy and Roc_Auc score on train and test sets\n",
+        "train_preds_svc_gd= svm_clf.predict(X_train)\n",
+        "test_preds_svc_gd= svm_clf.predict(X_test)\n",
+        "evaluation_metrics(train_preds_svc_gd, test_preds_svc_gd)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4Uc-3Yim08wu",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 338
+        },
+        "outputId": "15f746c4-ac5c-442e-9a51-639117adaec1"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Confusion Matrix of SVM model\n",
+        "conf_mtrx(test_preds_svc_gd)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "j7Qkrp_x1Bbt",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "33eed2f5-53e2-4a1f-9e03-99594d26937a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.93      0.92      0.92       589\n",
+            "           1       0.92      0.93      0.92       563\n",
+            "\n",
+            "    accuracy                           0.92      1152\n",
+            "   macro avg       0.92      0.92      0.92      1152\n",
+            "weighted avg       0.92      0.92      0.92      1152\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Classification Report\n",
+        "print(classification_report(y_test,test_preds_svc_gd))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xwPsTjNO1Sfw",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "outputId": "fd576913-6ed4-4b72-80f7-0c316ce80401"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# ROC curve and AUC for SVM model\n",
+        "roc_auc(svm_clf)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g8CB1yCdfakQ"
+      },
+      "source": [
+        "#**Model Comparision Matrix**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WcBY6Br7gm7Z"
+      },
+      "outputs": [],
+      "source": [
+        "#Creating a function to store Auc score\n",
+        "def auc_score(clf):\n",
+        "\n",
+        "  probs = clf.predict_proba(X_test)\n",
+        "  # keep probabilities for the positive outcome only\n",
+        "  probs = probs[:, 1]\n",
+        "  # calculate AUC\n",
+        "  log_auc = roc_auc_score(y_test, probs)\n",
+        "  return log_auc\n",
+        "\n",
+        "# Creating a dataframe to store the performance of each models\n",
+        "Comparision_df= pd.DataFrame({\n",
+        "    'Logistic Regression' : { 'Train Accuracy': accuracy_score(y_train,train_preds_lr).round(4)*100  , 'Test Accuracy': accuracy_score(y_test,test_preds_lr).round(4)*100 , 'Precision':  precision_score(y_test, test_preds_lr).round(4)*100 , 'Recall': recall_score(y_test, test_preds_lr).round(4)*100 ,'F1_score': f1_score(y_test, test_preds_lr).round(4)*100  , 'AUC': auc_score(clf_lr).round(4)*100 },\n",
+        "\n",
+        "    'Decision Trees' : { 'Train Accuracy': accuracy_score(y_train,train_preds_dt_gd).round(4)*100 , 'Test Accuracy': accuracy_score(y_test,test_preds_dt_gd).round(4)*100 , 'Precision':  precision_score(y_test, test_preds_dt_gd).round(4)*100 , 'Recall': recall_score(y_test, test_preds_dt_gd).round(4)*100 ,'F1_score': f1_score(y_test, test_preds_dt_gd).round(4)*100 , 'AUC': auc_score(dt_grid).round(4)*100 },\n",
+        "\n",
+        "    'Random Forest' : { 'Train Accuracy': accuracy_score(y_train,train_preds_rf_cv).round(4)*100 , 'Test Accuracy': accuracy_score(y_test,test_preds_rf_cv).round(4)*100 , 'Precision':  precision_score(y_test, test_preds_rf_cv).round(4)*100 , 'Recall': recall_score(y_test, test_preds_rf_cv).round(4)*100 ,'F1_score': f1_score(y_test, test_preds_rf_cv).round(4)*100 , 'AUC': auc_score(random_clf).round(4)*100 },\n",
+        "\n",
+        "    'XG Boost' : { 'Train Accuracy': accuracy_score(y_train,train_preds_xgb).round(4)*100 , 'Test Accuracy': accuracy_score(y_test,test_preds_xgb).round(4)*100 , 'Precision':  precision_score(y_test, test_preds_xgb).round(4)*100 , 'Recall': recall_score(y_test, test_preds_xgb).round(4)*100 ,'F1_score':f1_score(y_test, test_preds_xgb).round(4)*100  , 'AUC': auc_score(xgb_grid).round(4)*100 },\n",
+        "\n",
+        "    'SVM' : { 'Train Accuracy': accuracy_score(y_train,train_preds_svc_gd).round(4)*100 , 'Test Accuracy': accuracy_score(y_test,test_preds_svc_gd).round(4)*100 , 'Precision':  precision_score(y_test, test_preds_svc_gd).round(4)*100 , 'Recall': recall_score(y_test, test_preds_svc_gd).round(4)*100 ,'F1_score': f1_score(y_test, test_preds_svc_gd).round(4)*100 , 'AUC': auc_score(svm_clf).round(4)*100 },\n",
+        "\n",
+        "}).T"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pYVHFe126CVp",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "04fa4bb3-94ff-416f-9247-c9d7330f7e76"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                     Train Accuracy  Test Accuracy  Precision  Recall  \\\n",
+              "Logistic Regression           68.71          66.67      65.90   65.90   \n",
+              "Decision Trees                73.40          70.49      66.77   78.86   \n",
+              "Random Forest                 75.68          71.53      69.29   74.96   \n",
+              "XG Boost                      93.38          86.72      88.10   84.19   \n",
+              "SVM                           99.91          92.36      91.59   92.90   \n",
+              "\n",
+              "                     F1_score    AUC  \n",
+              "Logistic Regression     65.90  73.18  \n",
+              "Decision Trees          72.31  77.78  \n",
+              "Random Forest           72.01  81.06  \n",
+              "XG Boost                86.10  93.25  \n",
+              "SVM                     92.24  97.37  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-765b9bce-fdc4-4767-99d6-e54811c28ab1\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>Train Accuracy</th>\n",
+              "      <th>Test Accuracy</th>\n",
+              "      <th>Precision</th>\n",
+              "      <th>Recall</th>\n",
+              "      <th>F1_score</th>\n",
+              "      <th>AUC</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Logistic Regression</th>\n",
+              "      <td>68.71</td>\n",
+              "      <td>66.67</td>\n",
+              "      <td>65.90</td>\n",
+              "      <td>65.90</td>\n",
+              "      <td>65.90</td>\n",
+              "      <td>73.18</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Decision Trees</th>\n",
+              "      <td>73.40</td>\n",
+              "      <td>70.49</td>\n",
+              "      <td>66.77</td>\n",
+              "      <td>78.86</td>\n",
+              "      <td>72.31</td>\n",
+              "      <td>77.78</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Random Forest</th>\n",
+              "      <td>75.68</td>\n",
+              "      <td>71.53</td>\n",
+              "      <td>69.29</td>\n",
+              "      <td>74.96</td>\n",
+              "      <td>72.01</td>\n",
+              "      <td>81.06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>XG Boost</th>\n",
+              "      <td>93.38</td>\n",
+              "      <td>86.72</td>\n",
+              "      <td>88.10</td>\n",
+              "      <td>84.19</td>\n",
+              "      <td>86.10</td>\n",
+              "      <td>93.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SVM</th>\n",
+              "      <td>99.91</td>\n",
+              "      <td>92.36</td>\n",
+              "      <td>91.59</td>\n",
+              "      <td>92.90</td>\n",
+              "      <td>92.24</td>\n",
+              "      <td>97.37</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-765b9bce-fdc4-4767-99d6-e54811c28ab1')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-765b9bce-fdc4-4767-99d6-e54811c28ab1 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-765b9bce-fdc4-4767-99d6-e54811c28ab1');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 119
+        }
+      ],
+      "source": [
+        "#Getting the result of our Comparision Matrix\n",
+        "Comparision_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Observation from above table:\n",
+        "\n",
+        "* **XG Boost, Support vector machine** gives highest Accuracy, Recall, Precision and AUC score.\n",
+        "* Highest recall is given by **Support vector machine**\n",
+        "* Highest AUC is given by **Support vector machine**\n",
+        "* Overall we can say that **Support vector machine** is the best model that can be used for the risk prediction of Cardiovasular heart disease."
+      ],
+      "metadata": {
+        "id": "Iw_lq5ius8ix"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7U9l5pOz7VoM"
+      },
+      "source": [
+        "#**Analysis Summary**:\n",
+        "\n",
+        "* The people who have Cardiovascular heart disease is almost equal between smokers and non smokers.\n",
+        "\n",
+        "* The median age of the patients in the dataset is 49.\n",
+        "- Out of 3390 patients from the records 1069 people had Hypertension before.\n",
+        "- Number of people having Stroke in past is very less which is 22 vs number of people who didn't have stroke.\n",
+        "- People having diabetes is very less compared to people who didn't have diabetes.\n",
+        "- Diabetic patients tend to have higher 10 year Risk of CHD vs not Diabetic patients.\n",
+        "- Hypertensive patients are at higher risk of CHD vs patients who are not suffering from hypertension.\n",
+        "- Patients with prevalent stroke symptoms have a high 10-year risk of CHD vs patients without any stroke symptoms.\n",
+        "- Patients on Blood Preasure medication have higer risks of getting 10 year CHD as compared to patients who were not on medications.\n",
+        "- Males has slightly higher risk of having 10 Year CHD vs Female.\n",
+        "- In the dataset number of people without the disease(CHD) greatly exceeds the number of people with the disease.(Creating class imbalance problem)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ba2FflPO9vGJ"
+      },
+      "source": [
+        "#**Summary**\n",
+        "\n",
+        "* If we want to completely avoid any situations where the patient has heart disease, a high recall is desired. Whereas if we want to avoid treating a patient with no heart diseases a high precision is desired.\n",
+        "\n",
+        "* Assuming that in our case the patients who were incorrectly classified as suffering from heart disease are equally important since they could be indicative of some other ailment, so we want a balance between precision and recall and a high f1 score is desired.\n",
+        "\n",
+        "* We have used Logistic Regression, Decision Trees, Random Forest, XGBoost and SVC for modelling. Based on our observations, the Support vector machine with the radial kernel is the best performing model in terms of accuracy and the F1 score and it's high AUC-score shows that it has a high true positive rate.\n",
+        "\n",
+        "* Balancing the dataset by using the SMOTE technique helped in improving the models sensitivity and improving the scores of all the models.\n",
+        "\n",
+        "- With scaling the dataset the performance of SVM models is worse. Without scaling, the performance of SVM is best among all other models.\n",
+        "\n",
+        "* With more data(especially that of the minority class) better models can be built."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file