diff --git a/AlinaJoji_JyotsnaKoul_HeartFailurePrediction_.ipynb b/AlinaJoji_JyotsnaKoul_HeartFailurePrediction_.ipynb
new file mode 100644
index 0000000..fd7c9dd
--- /dev/null
+++ b/AlinaJoji_JyotsnaKoul_HeartFailurePrediction_.ipynb
@@ -0,0 +1,7563 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "AlinaJoji_JyotsnaKoul_HeartFailurePrediction .ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/jyotsnakoul/AI-ML-Month/blob/main/AlinaJoji_JyotsnaKoul_HeartFailurePrediction_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Team Members:**\n",
+        "\n",
+        "Name: Alina Elizabeth Joji\n",
+        "\n",
+        "Enrollment Number: 15401172021\n",
+        "\n",
+        "Email id: alinaelizabeth154btcsai21@igdtuw.ac.in\n",
+        "\n",
+        "\n",
+        "Name: Jyotsna Koul\n",
+        "\n",
+        "Enrollment Number: 15501172021\n",
+        "\n",
+        "Email id: jyotsna155btcsai21@igdtuw.ac.in\n",
+        "\n",
+        "---"
+      ],
+      "metadata": {
+        "id": "qhib7gfJto1e"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Introduction"
+      ],
+      "metadata": {
+        "id": "kekeySXOtyFs"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Cardiovascular disease (CVD) is a general term for conditions affecting the heart or blood vessels. It's usually associated with a build-up of fatty deposits inside the arteries and an increased risk of blood clots.\n",
+        "\n",
+        "\n",
+        "**There are four main types of CVD:**\n",
+        "\n",
+        "1. Coronary heart disease.\n",
+        "2. Stroke\n",
+        "1. Aortic disease.\n",
+        "2. Peripheral arterial disease.\n",
+        "\n",
+        "Cardiovascular diseases are the leading cause of death worldwide. Together CVD resulted in 17.9 million deaths (32.1%) in 2015.\n",
+        "\n",
+        "Coronary artery disease and stroke account for 80% of CVD deaths in males and 75% of CVD deaths in females.\n",
+        "\n",
+        "In the United States 11% of people between 20 and 40 have CVD, while 37% between 40 and 60, 71% of people between 60 and 80, and 85% of people over 80 have CVD.\n",
+        "\n",
+        "The average age of death from coronary artery disease in the developed world is around 80 while it is around 68 in the developing world.\n",
+        "\n",
+        "Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular disease prediction. "
+      ],
+      "metadata": {
+        "id": "4AGW2R_7uNvu"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Data Analysis\n"
+      ],
+      "metadata": {
+        "id": "bCRbK8exxARs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Importing python libraries\n",
+        "\n",
+        "# Libraries for Data Preprocessing\n",
+        "import numpy as np \n",
+        "import pandas as pd\n",
+        "from sklearn.preprocessing import StandardScaler as ss \n",
+        "\n",
+        "# Libraries for Data Visualization \n",
+        "import plotly.express as px \n",
+        "from plotly.subplots import make_subplots \n",
+        "from matplotlib import pyplot as plt \n",
+        "import seaborn as sns \n",
+        "import plotly.graph_objects as go\n",
+        "\n",
+        "# Libraries for Machine Learning Algorithms\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn.naive_bayes import GaussianNB\n",
+        "from sklearn.svm import SVC\n",
+        "import lightgbm as lgb\n",
+        "from lightgbm import LGBMClassifier\n",
+        "from xgboost import XGBClassifier\n",
+        "\n",
+        "# Libraries for Model Training and Evaluation\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import accuracy_score, classification_report, f1_score, confusion_matrix , precision_score, recall_score\n",
+        "from sklearn import svm,model_selection, tree, linear_model, neighbors, naive_bayes, ensemble, discriminant_analysis, gaussian_process\n",
+        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
+        "from sklearn.metrics import mean_squared_error,confusion_matrix, precision_score, recall_score, auc,roc_curve\n",
+        "from sklearn import model_selection\n",
+        "\n",
+        "%matplotlib inline"
+      ],
+      "metadata": {
+        "id": "hgEhZlUPtxmk"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "U-lu0JnmtPmu",
+        "outputId": "a6ad1571-86c2-421e-d68c-68bcd5d9a8e1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "''"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ],
+      "source": [
+        "#Enabling GPU support for more computationally demanding tasks like Deep Learning.\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "tf.test.gpu_device_name()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Uploading dataset from local computer\n",
+        "\n",
+        "from google.colab import files\n",
+        "\n",
+        "uploaded = files.upload()\n",
+        "\n",
+        "for fn in uploaded.keys():\n",
+        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
+        "      name=fn, length=len(uploaded[fn])))"
+      ],
+      "metadata": {
+        "colab": {
+          "resources": {
+            "http://localhost:8080/nbextensions/google.colab/files.js": {
+              "data": "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",
+              "ok": true,
+              "headers": [
+                [
+                  "content-type",
+                  "application/javascript"
+                ]
+              ],
+              "status": 200,
+              "status_text": ""
+            }
+          },
+          "base_uri": "https://localhost:8080/",
+          "height": 92
+        },
+        "id": "-U5hzKf8xwbB",
+        "outputId": "4ef58deb-896c-4688-dcee-f9cab93d8268"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-888b8ec4-9a56-4307-9366-4afc0f5a392c\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-888b8ec4-9a56-4307-9366-4afc0f5a392c\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving heart_failure_clinical_records_dataset.csv to heart_failure_clinical_records_dataset (2).csv\n",
+            "User uploaded file \"heart_failure_clinical_records_dataset.csv\" with length 12239 bytes\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Uploading the dataset\n",
+        "df= pd.read_csv('heart_failure_clinical_records_dataset.csv')"
+      ],
+      "metadata": {
+        "id": "Y4rKsYnbxzEj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#The data set\n",
+        "df"
+      ],
+      "metadata": {
+        "id": "DshRY-5dx9og",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 467
+        },
+        "outputId": "20af9159-3fbe-4244-8214-ca8e45dd92f5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
+              "0    75.0        0                       582         0                 20   \n",
+              "1    55.0        0                      7861         0                 38   \n",
+              "2    65.0        0                       146         0                 20   \n",
+              "3    50.0        1                       111         0                 20   \n",
+              "4    65.0        1                       160         1                 20   \n",
+              "..    ...      ...                       ...       ...                ...   \n",
+              "294  62.0        0                        61         1                 38   \n",
+              "295  55.0        0                      1820         0                 38   \n",
+              "296  45.0        0                      2060         1                 60   \n",
+              "297  45.0        0                      2413         0                 38   \n",
+              "298  50.0        0                       196         0                 45   \n",
+              "\n",
+              "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
+              "0                      1  265000.00               1.9           130    1   \n",
+              "1                      0  263358.03               1.1           136    1   \n",
+              "2                      0  162000.00               1.3           129    1   \n",
+              "3                      0  210000.00               1.9           137    1   \n",
+              "4                      0  327000.00               2.7           116    0   \n",
+              "..                   ...        ...               ...           ...  ...   \n",
+              "294                    1  155000.00               1.1           143    1   \n",
+              "295                    0  270000.00               1.2           139    0   \n",
+              "296                    0  742000.00               0.8           138    0   \n",
+              "297                    0  140000.00               1.4           140    1   \n",
+              "298                    0  395000.00               1.6           136    1   \n",
+              "\n",
+              "     smoking  time  DEATH_EVENT  \n",
+              "0          0     4            1  \n",
+              "1          0     6            1  \n",
+              "2          1     7            1  \n",
+              "3          0     7            1  \n",
+              "4          0     8            1  \n",
+              "..       ...   ...          ...  \n",
+              "294        1   270            0  \n",
+              "295        0   271            0  \n",
+              "296        0   278            0  \n",
+              "297        1   280            0  \n",
+              "298        1   285            0  \n",
+              "\n",
+              "[299 rows x 13 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e0a2579e-6dfe-40f7-bfd1-df3bfa6e4753\">\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>anaemia</th>\n",
+              "      <th>creatinine_phosphokinase</th>\n",
+              "      <th>diabetes</th>\n",
+              "      <th>ejection_fraction</th>\n",
+              "      <th>high_blood_pressure</th>\n",
+              "      <th>platelets</th>\n",
+              "      <th>serum_creatinine</th>\n",
+              "      <th>serum_sodium</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>smoking</th>\n",
+              "      <th>time</th>\n",
+              "      <th>DEATH_EVENT</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>75.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>582</td>\n",
+              "      <td>0</td>\n",
+              "      <td>20</td>\n",
+              "      <td>1</td>\n",
+              "      <td>265000.00</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>130</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>55.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7861</td>\n",
+              "      <td>0</td>\n",
+              "      <td>38</td>\n",
+              "      <td>0</td>\n",
+              "      <td>263358.03</td>\n",
+              "      <td>1.1</td>\n",
+              "      <td>136</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>65.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>146</td>\n",
+              "      <td>0</td>\n",
+              "      <td>20</td>\n",
+              "      <td>0</td>\n",
+              "      <td>162000.00</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>129</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>50.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>111</td>\n",
+              "      <td>0</td>\n",
+              "      <td>20</td>\n",
+              "      <td>0</td>\n",
+              "      <td>210000.00</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>137</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>65.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>160</td>\n",
+              "      <td>1</td>\n",
+              "      <td>20</td>\n",
+              "      <td>0</td>\n",
+              "      <td>327000.00</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>116</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>294</th>\n",
+              "      <td>62.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>61</td>\n",
+              "      <td>1</td>\n",
+              "      <td>38</td>\n",
+              "      <td>1</td>\n",
+              "      <td>155000.00</td>\n",
+              "      <td>1.1</td>\n",
+              "      <td>143</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>270</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>295</th>\n",
+              "      <td>55.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1820</td>\n",
+              "      <td>0</td>\n",
+              "      <td>38</td>\n",
+              "      <td>0</td>\n",
+              "      <td>270000.00</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>139</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>271</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>296</th>\n",
+              "      <td>45.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2060</td>\n",
+              "      <td>1</td>\n",
+              "      <td>60</td>\n",
+              "      <td>0</td>\n",
+              "      <td>742000.00</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>138</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>278</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>297</th>\n",
+              "      <td>45.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2413</td>\n",
+              "      <td>0</td>\n",
+              "      <td>38</td>\n",
+              "      <td>0</td>\n",
+              "      <td>140000.00</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>140</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>280</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>298</th>\n",
+              "      <td>50.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>196</td>\n",
+              "      <td>0</td>\n",
+              "      <td>45</td>\n",
+              "      <td>0</td>\n",
+              "      <td>395000.00</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>136</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>285</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>299 rows × 13 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e0a2579e-6dfe-40f7-bfd1-df3bfa6e4753')\"\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-e0a2579e-6dfe-40f7-bfd1-df3bfa6e4753 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-e0a2579e-6dfe-40f7-bfd1-df3bfa6e4753');\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": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Displying the column names\n",
+        "df.columns"
+      ],
+      "metadata": {
+        "id": "Ht_fI_7ezkR4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a6b418bf-bedf-4550-b5df-b230274ad936"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n",
+              "       'ejection_fraction', 'high_blood_pressure', 'platelets',\n",
+              "       'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n",
+              "       'DEATH_EVENT'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Displays the no of rows and columns\n",
+        "df.shape"
+      ],
+      "metadata": {
+        "id": "Aaai2L4yzykj",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "044f3a38-90b1-44d8-bcc6-8a15622e04ae"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(299, 13)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "So, the data set consists of 13 columns and 299 rows"
+      ],
+      "metadata": {
+        "id": "nUCPGurZ0S4c"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**THE DESCRIPTION OF THE 13 COLUMNS IS SHOWN BELOW**\n",
+        "1. age: Displays the age of an indivisual.It is the an important risk factor in developing cardiovascular or heart diseases.\n",
+        "\n",
+        "2. anaemia: Diaslays whether there is any decrease of haemoglobin . Anemia is a condition in which you lack enough healthy red blood cells to carry adequate oxygen to your body's tissues. Having anemia, also referred to as low hemoglobin, can make you feel tired and can be very impactful on the progression of heart failure \n",
+        "\n",
+        "  0- Non-Anaemic\n",
+        "\n",
+        "  1- Anaemic\n",
+        "\n",
+        "3. creatinine_phosphokinase: Displays the level of CPK enzyme in blood.An elevated level of creatine kinase is seen in heart attacks, when the heart muscle is damaged, or in conditions that produce damage to the skeletal muscles or brain. CPK normal values: 10 to 120 micrograms per liter.\n",
+        "\n",
+        "4. diabetes: Displays if the patient suffers from diabetes. Over time, high blood sugar levels can damage blood vessels and the nerves that control your heart. People with diabetes are also more likely to have other conditions that raise the risk for heart disease.\n",
+        "\n",
+        "    0: Non-Diabetic\n",
+        "\n",
+        "    1: Diabetic\n",
+        "\n",
+        "5. ejection_fraction: Displays the percentage of blood leaving the heart at each contraction. EF is a measurement, expressed as a percentage, of how much blood the left ventricle pumps out with each contraction.A normal heart’s ejection fraction may be between 50 and 70 percent.\n",
+        "\n",
+        "6. high_blood_pressure: Displays whether the person suffers from high blood pressure or not.It increases the force of blood through your arteries and can damage artery walls.It occurs with other conditions like- obesity, high cholesterol or diabetes.These increase the risk of heart failure.\n",
+        "     \n",
+        "      0- Blood Pressure: Normal\n",
+        "\n",
+        "      1- Blood Pressure: High \n",
+        "\n",
+        "7. platelets: Displays the count of platelets in the blood (kiloplatelets/mL). Platelets are colorless blood cells that help blood clot.But platelets can also form blood clots inside blood vessels and, if they block blood vessels supplying the heart, can cause heart attacks and strokes. The normal range of platelets in the blood is 150,000 to 400,000 platelets per microliter (mcL).\n",
+        " \n",
+        "\n",
+        "8. serum_creatinine: Displays the level of serum creatinine in the blood (mg/dL). \n",
+        "\n",
+        "  An increased level of creatinine may be a sign of poor kidney function. The normal values by age: 0.9 to 1.3 mg/dL for adult males. 0.6 to 1.1 mg/dL for adult females. 0.5 to 1.0 mg/dL for children ages 3 to 18 years.\n",
+        " \n",
+        "\n",
+        "9. serum_sodium: Displays the level of serum sodium in the blood (mEq/L). The normal range for blood sodium levels is 141 to 143 milliequivalents per liter (mEq/L).\n",
+        " \n",
+        "\n",
+        "10. sex: Displays the gender of the individual. Men are at greater risk of heart disease than pre-menopausal women. If a female has diabetes, she is more likely to develop heart disease than a male with diabetes.\n",
+        "\n",
+        "     0: Female \n",
+        "\n",
+        "     1: Male \n",
+        "\n",
+        "11. smoking: Displays if the patient smokes or not. Smoking is a major risk factor for the development of heart failure. \n",
+        "    \n",
+        "     0: Non-smoker  \n",
+        "     \n",
+        "     1: Smoker \n",
+        "12. time: Displays the number of days in the Follow-up period. Outpatient follow-up within 14 days after HF exacerbation requiring hospitalization or emergency department visit is associated with better outcomes, particularly if the follow-up is with a familiar physician.\n",
+        "\n",
+        "13. DEATH_EVENT: Displays if the patient deceased during the follow-up period. \n",
+        "\n",
+        "     0: Alive\n",
+        "\n",
+        "     1: Deceased \n"
+      ],
+      "metadata": {
+        "id": "CuAX7Um40q3y"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Data Visualization"
+      ],
+      "metadata": {
+        "id": "HQ3CYnwSFcaA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Displaying values of different columns of dataset\n",
+        "df.hist(bins=10, figsize=(15, 10), color=\"#FF6C2F\")\n",
+        "plt.tight_layout()"
+      ],
+      "metadata": {
+        "id": "U---66OQ0OeX",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 729
+        },
+        "outputId": "41b33961-51ee-4c82-d4c9-a189f72f9697"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x720 with 16 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Explanation**\n",
+        "1.The data provided to us belongs to people of age group 40-90.\n",
+        "\n",
+        "2.The Anaemia, Diabetes, High Blood Pressure and Smoking plots displays whether individuals suffer from them(1) or not(0)\n",
+        "\n",
+        "3.The Death Event column shows whether the person is aliver(0) or dead due to heart failure(1)\n",
+        "\n",
+        "4.The sex column signifies whether the individual is a man(1) or a woman(0)\n",
+        "\n",
+        "5.The levels of creatinine phosphokinase, serum sodium, platelets count and serum creatinine are depicted in their specific plots"
+      ],
+      "metadata": {
+        "id": "oAWNtNNqFxH4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Creating a copy of dataset for the visualization\n",
+        "dv= df.copy()\n",
+        "\n",
+        "#Changing values 1, 0 to have the issue or not\n",
+        "dv['anaemia'] = np.where(dv['anaemia'] == 1, 'Anaemic', 'Non-Anaemic')\n",
+        "dv['diabetes'] = np.where(dv['diabetes'] == 1, 'Diabetic', 'Non-Diabetic')\n",
+        "dv['high_blood_pressure'] = np.where(dv['high_blood_pressure'] == 1, 'High', 'Normal')\n",
+        "dv['sex'] = np.where(dv['sex'] == 1, 'Male', 'Female')\n",
+        "dv['smoking'] = np.where(dv['smoking'] == 1, 'Smoker', 'Non-Smoker')\n",
+        "dv['DEATH_EVENT'] = np.where(dv['DEATH_EVENT'] == 0, 'Alive', 'Deceased')\n",
+        "\n",
+        "#explore first five rows in the dataset\n",
+        "dv.head(5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 357
+        },
+        "id": "kV1lBy4HFsin",
+        "outputId": "4ce0aac2-22b6-4524-851f-71acedc9e138"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    age      anaemia  creatinine_phosphokinase      diabetes  \\\n",
+              "0  75.0  Non-Anaemic                       582  Non-Diabetic   \n",
+              "1  55.0  Non-Anaemic                      7861  Non-Diabetic   \n",
+              "2  65.0  Non-Anaemic                       146  Non-Diabetic   \n",
+              "3  50.0      Anaemic                       111  Non-Diabetic   \n",
+              "4  65.0      Anaemic                       160      Diabetic   \n",
+              "\n",
+              "   ejection_fraction high_blood_pressure  platelets  serum_creatinine  \\\n",
+              "0                 20                High  265000.00               1.9   \n",
+              "1                 38              Normal  263358.03               1.1   \n",
+              "2                 20              Normal  162000.00               1.3   \n",
+              "3                 20              Normal  210000.00               1.9   \n",
+              "4                 20              Normal  327000.00               2.7   \n",
+              "\n",
+              "   serum_sodium     sex     smoking  time DEATH_EVENT  \n",
+              "0           130    Male  Non-Smoker     4    Deceased  \n",
+              "1           136    Male  Non-Smoker     6    Deceased  \n",
+              "2           129    Male      Smoker     7    Deceased  \n",
+              "3           137    Male  Non-Smoker     7    Deceased  \n",
+              "4           116  Female  Non-Smoker     8    Deceased  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-300cc9a8-54f6-4473-abed-08912cf1e96a\">\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>anaemia</th>\n",
+              "      <th>creatinine_phosphokinase</th>\n",
+              "      <th>diabetes</th>\n",
+              "      <th>ejection_fraction</th>\n",
+              "      <th>high_blood_pressure</th>\n",
+              "      <th>platelets</th>\n",
+              "      <th>serum_creatinine</th>\n",
+              "      <th>serum_sodium</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>smoking</th>\n",
+              "      <th>time</th>\n",
+              "      <th>DEATH_EVENT</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>75.0</td>\n",
+              "      <td>Non-Anaemic</td>\n",
+              "      <td>582</td>\n",
+              "      <td>Non-Diabetic</td>\n",
+              "      <td>20</td>\n",
+              "      <td>High</td>\n",
+              "      <td>265000.00</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>130</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Non-Smoker</td>\n",
+              "      <td>4</td>\n",
+              "      <td>Deceased</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>55.0</td>\n",
+              "      <td>Non-Anaemic</td>\n",
+              "      <td>7861</td>\n",
+              "      <td>Non-Diabetic</td>\n",
+              "      <td>38</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>263358.03</td>\n",
+              "      <td>1.1</td>\n",
+              "      <td>136</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Non-Smoker</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Deceased</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>65.0</td>\n",
+              "      <td>Non-Anaemic</td>\n",
+              "      <td>146</td>\n",
+              "      <td>Non-Diabetic</td>\n",
+              "      <td>20</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>162000.00</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>129</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Smoker</td>\n",
+              "      <td>7</td>\n",
+              "      <td>Deceased</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>50.0</td>\n",
+              "      <td>Anaemic</td>\n",
+              "      <td>111</td>\n",
+              "      <td>Non-Diabetic</td>\n",
+              "      <td>20</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>210000.00</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>137</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Non-Smoker</td>\n",
+              "      <td>7</td>\n",
+              "      <td>Deceased</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>65.0</td>\n",
+              "      <td>Anaemic</td>\n",
+              "      <td>160</td>\n",
+              "      <td>Diabetic</td>\n",
+              "      <td>20</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>327000.00</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>116</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>Non-Smoker</td>\n",
+              "      <td>8</td>\n",
+              "      <td>Deceased</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-300cc9a8-54f6-4473-abed-08912cf1e96a')\"\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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+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\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-300cc9a8-54f6-4473-abed-08912cf1e96a 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-300cc9a8-54f6-4473-abed-08912cf1e96a');\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
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Visualizing Age, level of serum creatinine, level of serum sodium and ejection fraction\n",
+        "fig = make_subplots(\n",
+        "    rows=1, cols=4,\n",
+        "    subplot_titles=(\"Age\", \"Level of serum creatinine\", \"Level of serum sodium\", \"Ejection fraction\"))\n",
+        "\n",
+        "#Age\n",
+        "fig.add_trace(go.Histogram(x=dv.age[dv['DEATH_EVENT'] == 'Deceased'], name='Deceased',\n",
+        "                          marker=dict(color='#d5a036', line=dict(width=1.5, color='#000000')),\n",
+        "                           xbins=dict(size=10 )),\n",
+        "                           row=1, col=1)\n",
+        "#Age\n",
+        "fig.add_trace(go.Histogram(x=dv.age[dv['DEATH_EVENT'] == 'Alive'], name='Alive',\n",
+        "                          marker=dict(color='#1984c5', line=dict(width=1.5, color='#000000')),\n",
+        "                           xbins=dict(size=10 )),row=1, col=1)\n",
+        "##level of serum creatinine\n",
+        "fig.add_trace(go.Histogram(x=dv.serum_creatinine[dv['DEATH_EVENT'] == 'Deceased'],\n",
+        "                           marker=dict(color='#d5a036', line=dict(width=1.5, color='#000000')),\n",
+        "                           showlegend=False,xbins=dict(size=25)),row=1, col=2)\n",
+        "##level of serum creatinine\n",
+        "fig.add_trace(go.Histogram(x=dv.serum_creatinine[dv['DEATH_EVENT'] == 'Alive'],\n",
+        "                           marker=dict(color='#1984c5', line=dict(width=1.5, color='#000000')),\n",
+        "                           showlegend=False,xbins=dict(size=25 )),row=1, col=2)\n",
+        "##level of serum sodium\n",
+        "fig.add_trace(go.Histogram(x=dv.serum_sodium[dv['DEATH_EVENT'] == 'Deceased'],\n",
+        "                           marker=dict(color='#d5a036', line=dict(width=1.5, color='#000000')),\n",
+        "                           showlegend=False,xbins=dict(size=25)),row=1, col=3)\n",
+        "##level of serum sodium\n",
+        "fig.add_trace(go.Histogram(x=dv.serum_sodium[dv['DEATH_EVENT'] == 'Alive'],\n",
+        "                           marker=dict(color='#1984c5', line=dict(width=1.5, color='#000000')),\n",
+        "                           showlegend=False,xbins=dict(size=25 )),row=1, col=3)\n",
+        "#Ejection Fraction\n",
+        "fig.add_trace(go.Histogram(x=dv.ejection_fraction [dv['DEATH_EVENT'] == 'Deceased'],\n",
+        "                           marker=dict(color='#d5a036', line=dict(width=1.5, color='#000000')),\n",
+        "                           showlegend=False,xbins=dict(size=5 )),row=1, col=4)\n",
+        "#Ejection Fraction\n",
+        "fig.add_trace(go.Histogram(x=dv.ejection_fraction [dv['DEATH_EVENT'] == 'Alive'],\n",
+        "                           marker=dict(color='#1984c5', line=dict(width=1.5, color='#000000')),\n",
+        "                           showlegend=False,xbins=dict(size=5 )),row=1, col=4)\n",
+        "#Tittle config\n",
+        "fig.update_layout(title_x=0.5,title_text=\"Heart failure deaths distribution by:\", height=400,barmode='stack',font_family='Verdana',\n",
+        "                 font=dict(family=\"Verdana,Verdana\",size=12),paper_bgcolor='#edeae7',plot_bgcolor='#edeae7')\n",
+        "#config opacity\n",
+        "fig.update_traces(opacity=0.80)\n",
+        "\n",
+        "fig.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 417
+        },
+        "id": "icPlKIwMGbXC",
+        "outputId": "52ec9f01-34a6-4972-83cb-d0ed4a4efc55"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
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+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<b> Explanation: </b>\n",
+        "<n>\n",
+        "<ol>\n",
+        "<li> Age Plot: \n",
+        "<n>\n",
+        "<ul>\n",
+        "<li> Existing data consists of people belong to the age group 50-70</li>\n",
+        "<li> Heart Failure is common in sixties </li>\n",
+        "<li> Most people in the age group 80-100 are prone to heart failure</li>\n",
+        "<li> Heart failure is less common but still persists int the age group 40-50 </li>\n",
+        "</ul>\n",
+        "</li>\n",
+        "<li> Serum Creatinine Plot:\n",
+        "<ul>\n",
+        "<li> This plot shows unclear values of the level of sodium creatinine in blood </li>\n",
+        "</ul>\n",
+        "</li> \n",
+        "<li> Serum Sodium Plot: \n",
+        "<n>\n",
+        "<ul>\n",
+        "<li> Almost all the individuals have their level of serium sodium in blood in the range of 130-150 mEq/l </li>\n",
+        "</ul>\n",
+        "<li> Ejection Fraction Plot:\n",
+        "<n>\n",
+        "<ul>\n",
+        "<li> Most of the people in the existing dataset have 35-40% as their ejection fraction. </li>\n",
+        "<li> The value of ejection fraction varies widely among individuals ranging majorly from 20 to 70% </li>\n",
+        "<li> Heart failure occured mainly among people with ejection fraction between 20-30%\n",
+        "<li> Heart failure is least seen when individual has an ejection fraction of about 70%\n",
+        "</ul>\n"
+      ],
+      "metadata": {
+        "id": "Xz-L1qjRGkbV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Visualizing the median values of the above columns\n",
+        "\n",
+        "#creating variable with median age, median level of sodium creatinine and median ejection_fraction\n",
+        "median_age=dv.groupby('DEATH_EVENT')['age'].median()\n",
+        "median_creatinine=dv.groupby('DEATH_EVENT')['serum_creatinine'].median()\n",
+        "median_sodium=dv.groupby('DEATH_EVENT')['serum_sodium'].median()\n",
+        "median_ejection_fraction=dv.groupby('DEATH_EVENT')['ejection_fraction'].median()\n",
+        "#reset index and sort values\n",
+        "median_age=median_age.reset_index().sort_values('age', ascending=False)\n",
+        "median_creatinine=median_creatinine.reset_index().sort_values('serum_creatinine',ascending=False )\n",
+        "median_sodium=median_sodium.reset_index().sort_values('serum_sodium')\n",
+        "median_ejection_fraction=median_ejection_fraction.reset_index().sort_values('ejection_fraction')\n",
+        "x_value = median_age.DEATH_EVENT\n",
+        "\n",
+        "#making the plots\n",
+        "fig = make_subplots(\n",
+        "    rows=1, cols=4,\n",
+        "    subplot_titles=(\"Age\", \"Level of serum creatinine\", \"Level of serum sodium\", \"Ejection fraction\"))\n",
+        "\n",
+        "#create bar plot\n",
+        "\n",
+        "#Age\n",
+        "fig.add_trace(go.Bar(x=x_value,y=median_age.age, text=median_age.age,textposition='outside',showlegend=False,\n",
+        "          marker=dict(color=['#d5a036', '#1984c5'],line=dict(width=1.5, color='#000000'), opacity=0.9)),row=1, col=1)\n",
+        "\n",
+        "#Serum creatinine\n",
+        "fig.add_trace(go.Bar(x=x_value,y=median_creatinine.serum_creatinine, text=median_creatinine.serum_creatinine,\n",
+        "                     showlegend=False, textposition='outside',\n",
+        "          marker=dict(color=['#d5a036', '#1984c5'],line=dict(width=1.5, color='#000000'), opacity=0.9)),row=1, col=2)\n",
+        "\n",
+        "#Serum sodium\n",
+        "fig.add_trace(go.Bar(x=x_value,y=median_sodium.serum_sodium, text=median_sodium.serum_sodium,\n",
+        "                     showlegend=False, textposition='outside',\n",
+        "          marker=dict(color=['#d5a036', '#1984c5'],line=dict(width=1.5, color='#000000'), opacity=0.9)),row=1, col=3)\n",
+        "\n",
+        "#ejection fraction\n",
+        "fig.add_trace(go.Bar(x=x_value,y=median_ejection_fraction.ejection_fraction, text=median_ejection_fraction.ejection_fraction,textposition='outside',showlegend=False,\n",
+        "          marker=dict(color=['#d5a036', '#1984c5'],line=dict(width=1.5, color='#000000'), opacity=0.9)),row=1, col=4)\n",
+        "\n",
+        "#bar plot config\n",
+        "fig.update_layout(title_x=0.5,title_text=\"Median Values of:\", height=450,font_family='Verdana',\n",
+        "                 font=dict(family=\"Verdana,Verdana\",size=13),paper_bgcolor='#edeae7',plot_bgcolor='#edeae7')\n",
+        "#bar plot config\n",
+        "fig.update_traces(textfont_size=14, textangle=0, textposition=\"outside\", cliponaxis=False,\n",
+        "                 marker_line_width=1.5,marker_line_color=\"black\")\n",
+        "\n",
+        "#config space subtitles\n",
+        "for i in range(0,4):\n",
+        "    fig.layout.annotations[i].update(y=1.05)\n",
+        "\n",
+        "#bar plot config\n",
+        "fig.update_xaxes(showline=True, linewidth=1, linecolor='black')\n",
+        "\n",
+        "fig.show()"
+      ],
+      "metadata": {
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+          "height": 467
+        },
+        "id": "c0rsLKxpGgUN",
+        "outputId": "c2d1af7e-8d34-40ae-a815-b77abf271562"
+      },
+      "execution_count": null,
+      "outputs": [
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+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<b> Explanation: </b>\n",
+        "<n>\n",
+        "<ol>\n",
+        "<li> Age Plot: \n",
+        "<n>\n",
+        "<ul>\n",
+        "<li> The median age of people who died of hear failure is 65 years</li>\n",
+        "<li> The median age of the alive people in the existing dataset is 60 years </li>\n",
+        "</ul>\n",
+        "</li>\n",
+        "<li> Serum Creatinine Plot:\n",
+        "<ul>\n",
+        "<li> The median value of serum creatinine in blood of deceased individuals is 1.3 mg/dL </li>\n",
+        "<li> The median value of serum creatinine in blood of alive people in the existing datset is 1 mg/dL </li>\n",
+        "<li> The values coincides with the fact that increased level of creatinine is a sign of poor kidney function as contributes to heart failure </li>\n",
+        "</ul>\n",
+        "</li> \n",
+        "<li> Serum Sodium Plot: \n",
+        "<n>\n",
+        "<ul>\n",
+        "<li>  The median value of serum sodium in blood of deceased individuals is 135.5 mEq/L </li>\n",
+        "<li> The median value of serum sodium in blood of alive people in the existing datset is 137 mEq/L </li>\n",
+        "</ul>\n",
+        "<li> Ejection Fraction Plot:\n",
+        "<n>\n",
+        "<ul>\n",
+        "<li>The median value of ejection fraction of deceased individuals is 30% </li>\n",
+        "<li> The median value of ejection fraction of alive people in the existing datset is 38% </li>\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "5m9juaYBGzPu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Visualizing the effect of platelets count\n",
+        "fig, axes = plt.subplots(1,1, figsize=(19,6))\n",
+        "sns.set()\n",
+        "sns.histplot(data=dv, x=\"platelets\", alpha=0.8, hue = \"DEATH_EVENT\",kde=True, multiple=\"dodge\", palette=\"magma\")\n",
+        "plt.title(\"Relationship between Platelets count and heart failure deaths\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 422
+        },
+        "id": "jQaYmRO3GuhY",
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+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Relationship between Platelets count and heart failure deaths')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1368x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Explanation:**\n",
+        "\n",
+        "The graph coincides with the fact that the normal platelets count in the blood lies between 150K to 400K platelets/mcL</li>"
+      ],
+      "metadata": {
+        "id": "WwPOypeJHCvT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Visualizing the effect of creatinine phosphokinase count\n",
+        "fig, axes = plt.subplots(1,1, figsize=(15,5))\n",
+        "sns.set()\n",
+        "sns.kdeplot(x=dv[\"creatinine_phosphokinase\"], hue=dv[\"DEATH_EVENT\"], common_norm=False, palette=\"RdBu\",\n",
+        "   alpha=.5, linewidth=0, shade=True)\n",
+        "plt.title(\"Relationship between Creatinine Phosphokinase and heart failure deaths\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 373
+        },
+        "id": "J-49gcd2G_P4",
+        "outputId": "ce2a1c9d-c894-487e-dbf1-4dfac6cedcf0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Relationship between Creatinine Phosphokinase and heart failure deaths')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x360 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Visualizing all the boolean columns\n",
+        "\n",
+        "visu = pd.DataFrame()\n",
+        "#variable with 0 for the iteration\n",
+        "num = 0\n",
+        "#variable with the name of columns\n",
+        "variables = ['sex','high_blood_pressure','anaemia','diabetes','smoking']\n",
+        "#for loop to get the percentage of columns\n",
+        "for i in variables:\n",
+        "    num += 1\n",
+        "    temp = dv.groupby(i)['DEATH_EVENT'].value_counts(normalize=True)\n",
+        "    temp = temp.mul(100).rename('percentage' + str(num)).reset_index()\n",
+        "    visu = pd.concat([visu, temp], axis=1)\n",
+        "\n",
+        "#drop diplicated values    \n",
+        "visu = visu.loc[:, ~visu.columns.duplicated()]\n",
+        "visu = visu.round(decimals = 2)\n",
+        "#colors for the visualizations\n",
+        "color_rgb = ['rgb(25, 132, 197)','rgb(213, 160, 54)']\n",
+        "num = 0\n",
+        "#loop for creating visualizations\n",
+        "for i in variables:\n",
+        "    num +=1\n",
+        "    fig = px.histogram(visu, x=i, y='percentage' + str(num),\n",
+        "             color='DEATH_EVENT', barmode='group', \n",
+        "             text_auto=True,\n",
+        "             height=450, width = 600,color_discrete_sequence= color_rgb,opacity=0.8)\n",
+        "    #config plot\n",
+        "    fig.update_layout(title_x=0.5,title_text=(f\"Heart failure deaths distribution by {i}\"), height=450,font_family='Verdana',\n",
+        "                 font=dict(family=\"Verdana,Verdana\",size=13),yaxis_title=None, xaxis_title= i,\n",
+        "                      paper_bgcolor='#edeae7',plot_bgcolor='#edeae7')\n",
+        "    \n",
+        "     #config plot\n",
+        "    fig.update_traces(textfont_size=14, textangle=0, textposition=\"outside\", cliponaxis=False,\n",
+        "                 marker_line_width=1,marker_line_color=\"black\")\n",
+        "    fig.update_yaxes(title=\"\", ticksuffix='%')\n",
+        "    fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "AzDAnJXvG_Me",
+        "outputId": "09d5e53f-0c68-40df-a815-b23cb96e6655"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
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+              "                            \n",
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+              "\n",
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+              "</body>\n",
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+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "b> Explanation: </b>\n",
+        "<ol>\n",
+        "<li> Sex Plot:\n",
+        "<n>\n",
+        "<ul> \n",
+        "<li> The percentage of heart failure in females is 32.28% </li>\n",
+        "<li> The percentage of heart failue in males is 31.96% </li>\n",
+        "<li> The graph shows that women are more prone to heart failures than men </li>\n",
+        "</ul>\n",
+        "<li> High Blood Pressure Plot:\n",
+        "<n>\n",
+        "<ul>\n",
+        "<li> Almost 40% of people with high blood pressure suffer heart failure</li>\n",
+        "<li> 30% of people with normal blood pressure also suffer heart failure </li>\n",
+        "</ul>\n",
+        "<li> Anaemia Plot:\n",
+        "<n>\n",
+        "<ul>\n",
+        "<li> 35.66% anaemic people suffer heart failure </li>\n",
+        "<li> 29.41% non-anaemic people suffer hear failure as well. </li>\n",
+        "</ul>\n",
+        "<li> Diabetes Plot: \n",
+        "<n>\n",
+        "<ul>\n",
+        "<li> 32% of people with diabetes suffer heart failure </li>\n",
+        "<li> 32% of all the non-diabetic people also suffer heart failure </li>\n",
+        "</ul>\n",
+        "<li> Smoking Plot:\n",
+        "<ul> \n",
+        "<li> 32% of people who smoke experience heart failures </li>\n",
+        "<li> 31.25% of people who do not smoke still suffer hear failure "
+      ],
+      "metadata": {
+        "id": "pz-jKVslHepU"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.pairplot(dv,hue='DEATH_EVENT',palette='magma')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "5gdNC535G_Jr",
+        "outputId": "936cead3-843c-4e53-edb4-276e797d1de4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<seaborn.axisgrid.PairGrid at 0x7f0c1d1f5490>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 35
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1354.8x1260 with 56 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Inter-relationship among all the factors causing heart failure**"
+      ],
+      "metadata": {
+        "id": "ikX835mqHuEi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.describe()[:].T.style.background_gradient(cmap='magma')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 457
+        },
+        "id": "JCLaoSo0G_GY",
+        "outputId": "957be980-e33e-40e4-9976-1e0f3050fc9d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<pandas.io.formats.style.Styler at 0x7f0c1bd70b10>"
+            ],
+            "text/html": [
+              "<style type=\"text/css\">\n",
+              "#T_21f9e_row0_col0, #T_21f9e_row0_col1, #T_21f9e_row0_col2, #T_21f9e_row0_col3, #T_21f9e_row0_col4, #T_21f9e_row0_col5, #T_21f9e_row0_col6, #T_21f9e_row0_col7, #T_21f9e_row1_col0, #T_21f9e_row1_col1, #T_21f9e_row1_col2, #T_21f9e_row1_col3, #T_21f9e_row1_col4, #T_21f9e_row1_col5, #T_21f9e_row1_col6, #T_21f9e_row1_col7, #T_21f9e_row2_col0, #T_21f9e_row2_col1, #T_21f9e_row2_col3, #T_21f9e_row2_col4, #T_21f9e_row2_col5, #T_21f9e_row2_col6, #T_21f9e_row3_col0, #T_21f9e_row3_col1, #T_21f9e_row3_col2, #T_21f9e_row3_col3, #T_21f9e_row3_col4, #T_21f9e_row3_col5, #T_21f9e_row3_col6, #T_21f9e_row3_col7, #T_21f9e_row4_col0, #T_21f9e_row4_col1, #T_21f9e_row4_col2, #T_21f9e_row4_col3, #T_21f9e_row4_col4, #T_21f9e_row4_col5, #T_21f9e_row4_col6, #T_21f9e_row4_col7, #T_21f9e_row5_col0, #T_21f9e_row5_col1, #T_21f9e_row5_col2, #T_21f9e_row5_col3, #T_21f9e_row5_col4, #T_21f9e_row5_col5, #T_21f9e_row5_col6, #T_21f9e_row5_col7, #T_21f9e_row6_col0, #T_21f9e_row7_col0, #T_21f9e_row7_col1, #T_21f9e_row7_col2, #T_21f9e_row7_col3, #T_21f9e_row7_col4, #T_21f9e_row7_col5, #T_21f9e_row7_col6, #T_21f9e_row7_col7, #T_21f9e_row8_col0, #T_21f9e_row8_col1, #T_21f9e_row8_col2, #T_21f9e_row8_col4, #T_21f9e_row8_col5, #T_21f9e_row8_col6, #T_21f9e_row8_col7, #T_21f9e_row9_col0, #T_21f9e_row9_col1, #T_21f9e_row9_col2, #T_21f9e_row9_col3, #T_21f9e_row9_col4, #T_21f9e_row9_col5, #T_21f9e_row9_col6, #T_21f9e_row9_col7, #T_21f9e_row10_col0, #T_21f9e_row10_col1, #T_21f9e_row10_col2, #T_21f9e_row10_col3, #T_21f9e_row10_col4, #T_21f9e_row10_col5, #T_21f9e_row10_col6, #T_21f9e_row10_col7, #T_21f9e_row11_col0, #T_21f9e_row11_col1, #T_21f9e_row11_col2, #T_21f9e_row11_col3, #T_21f9e_row11_col4, #T_21f9e_row11_col5, #T_21f9e_row11_col6, #T_21f9e_row11_col7, #T_21f9e_row12_col0, #T_21f9e_row12_col1, #T_21f9e_row12_col2, #T_21f9e_row12_col3, #T_21f9e_row12_col4, #T_21f9e_row12_col5, #T_21f9e_row12_col6, #T_21f9e_row12_col7 {\n",
+              "  background-color: #000004;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_21f9e_row2_col2, #T_21f9e_row2_col7 {\n",
+              "  background-color: #010106;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_21f9e_row6_col1, #T_21f9e_row6_col2, #T_21f9e_row6_col3, #T_21f9e_row6_col4, #T_21f9e_row6_col5, #T_21f9e_row6_col6, #T_21f9e_row6_col7 {\n",
+              "  background-color: #fcfdbf;\n",
+              "  color: #000000;\n",
+              "}\n",
+              "#T_21f9e_row8_col3 {\n",
+              "  background-color: #010005;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "</style>\n",
+              "<table id=\"T_21f9e_\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th class=\"blank level0\" >&nbsp;</th>\n",
+              "      <th class=\"col_heading level0 col0\" >count</th>\n",
+              "      <th class=\"col_heading level0 col1\" >mean</th>\n",
+              "      <th class=\"col_heading level0 col2\" >std</th>\n",
+              "      <th class=\"col_heading level0 col3\" >min</th>\n",
+              "      <th class=\"col_heading level0 col4\" >25%</th>\n",
+              "      <th class=\"col_heading level0 col5\" >50%</th>\n",
+              "      <th class=\"col_heading level0 col6\" >75%</th>\n",
+              "      <th class=\"col_heading level0 col7\" >max</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th id=\"T_21f9e_level0_row0\" class=\"row_heading level0 row0\" >age</th>\n",
+              "      <td id=\"T_21f9e_row0_col0\" class=\"data row0 col0\" >299.000000</td>\n",
+              "      <td id=\"T_21f9e_row0_col1\" class=\"data row0 col1\" >60.833893</td>\n",
+              "      <td id=\"T_21f9e_row0_col2\" class=\"data row0 col2\" >11.894809</td>\n",
+              "      <td id=\"T_21f9e_row0_col3\" class=\"data row0 col3\" >40.000000</td>\n",
+              "      <td id=\"T_21f9e_row0_col4\" class=\"data row0 col4\" >51.000000</td>\n",
+              "      <td id=\"T_21f9e_row0_col5\" class=\"data row0 col5\" >60.000000</td>\n",
+              "      <td id=\"T_21f9e_row0_col6\" class=\"data row0 col6\" >70.000000</td>\n",
+              "      <td id=\"T_21f9e_row0_col7\" class=\"data row0 col7\" >95.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_21f9e_level0_row1\" class=\"row_heading level0 row1\" >anaemia</th>\n",
+              "      <td id=\"T_21f9e_row1_col0\" class=\"data row1 col0\" >299.000000</td>\n",
+              "      <td id=\"T_21f9e_row1_col1\" class=\"data row1 col1\" >0.431438</td>\n",
+              "      <td id=\"T_21f9e_row1_col2\" class=\"data row1 col2\" >0.496107</td>\n",
+              "      <td id=\"T_21f9e_row1_col3\" class=\"data row1 col3\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row1_col4\" class=\"data row1 col4\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row1_col5\" class=\"data row1 col5\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row1_col6\" class=\"data row1 col6\" >1.000000</td>\n",
+              "      <td id=\"T_21f9e_row1_col7\" class=\"data row1 col7\" >1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_21f9e_level0_row2\" class=\"row_heading level0 row2\" >creatinine_phosphokinase</th>\n",
+              "      <td id=\"T_21f9e_row2_col0\" class=\"data row2 col0\" >299.000000</td>\n",
+              "      <td id=\"T_21f9e_row2_col1\" class=\"data row2 col1\" >581.839465</td>\n",
+              "      <td id=\"T_21f9e_row2_col2\" class=\"data row2 col2\" >970.287881</td>\n",
+              "      <td id=\"T_21f9e_row2_col3\" class=\"data row2 col3\" >23.000000</td>\n",
+              "      <td id=\"T_21f9e_row2_col4\" class=\"data row2 col4\" >116.500000</td>\n",
+              "      <td id=\"T_21f9e_row2_col5\" class=\"data row2 col5\" >250.000000</td>\n",
+              "      <td id=\"T_21f9e_row2_col6\" class=\"data row2 col6\" >582.000000</td>\n",
+              "      <td id=\"T_21f9e_row2_col7\" class=\"data row2 col7\" >7861.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_21f9e_level0_row3\" class=\"row_heading level0 row3\" >diabetes</th>\n",
+              "      <td id=\"T_21f9e_row3_col0\" class=\"data row3 col0\" >299.000000</td>\n",
+              "      <td id=\"T_21f9e_row3_col1\" class=\"data row3 col1\" >0.418060</td>\n",
+              "      <td id=\"T_21f9e_row3_col2\" class=\"data row3 col2\" >0.494067</td>\n",
+              "      <td id=\"T_21f9e_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row3_col4\" class=\"data row3 col4\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row3_col5\" class=\"data row3 col5\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row3_col6\" class=\"data row3 col6\" >1.000000</td>\n",
+              "      <td id=\"T_21f9e_row3_col7\" class=\"data row3 col7\" >1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_21f9e_level0_row4\" class=\"row_heading level0 row4\" >ejection_fraction</th>\n",
+              "      <td id=\"T_21f9e_row4_col0\" class=\"data row4 col0\" >299.000000</td>\n",
+              "      <td id=\"T_21f9e_row4_col1\" class=\"data row4 col1\" >38.083612</td>\n",
+              "      <td id=\"T_21f9e_row4_col2\" class=\"data row4 col2\" >11.834841</td>\n",
+              "      <td id=\"T_21f9e_row4_col3\" class=\"data row4 col3\" >14.000000</td>\n",
+              "      <td id=\"T_21f9e_row4_col4\" class=\"data row4 col4\" >30.000000</td>\n",
+              "      <td id=\"T_21f9e_row4_col5\" class=\"data row4 col5\" >38.000000</td>\n",
+              "      <td id=\"T_21f9e_row4_col6\" class=\"data row4 col6\" >45.000000</td>\n",
+              "      <td id=\"T_21f9e_row4_col7\" class=\"data row4 col7\" >80.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_21f9e_level0_row5\" class=\"row_heading level0 row5\" >high_blood_pressure</th>\n",
+              "      <td id=\"T_21f9e_row5_col0\" class=\"data row5 col0\" >299.000000</td>\n",
+              "      <td id=\"T_21f9e_row5_col1\" class=\"data row5 col1\" >0.351171</td>\n",
+              "      <td id=\"T_21f9e_row5_col2\" class=\"data row5 col2\" >0.478136</td>\n",
+              "      <td id=\"T_21f9e_row5_col3\" class=\"data row5 col3\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row5_col4\" class=\"data row5 col4\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row5_col6\" class=\"data row5 col6\" >1.000000</td>\n",
+              "      <td id=\"T_21f9e_row5_col7\" class=\"data row5 col7\" >1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_21f9e_level0_row6\" class=\"row_heading level0 row6\" >platelets</th>\n",
+              "      <td id=\"T_21f9e_row6_col0\" class=\"data row6 col0\" >299.000000</td>\n",
+              "      <td id=\"T_21f9e_row6_col1\" class=\"data row6 col1\" >263358.029264</td>\n",
+              "      <td id=\"T_21f9e_row6_col2\" class=\"data row6 col2\" >97804.236869</td>\n",
+              "      <td id=\"T_21f9e_row6_col3\" class=\"data row6 col3\" >25100.000000</td>\n",
+              "      <td id=\"T_21f9e_row6_col4\" class=\"data row6 col4\" >212500.000000</td>\n",
+              "      <td id=\"T_21f9e_row6_col5\" class=\"data row6 col5\" >262000.000000</td>\n",
+              "      <td id=\"T_21f9e_row6_col6\" class=\"data row6 col6\" >303500.000000</td>\n",
+              "      <td id=\"T_21f9e_row6_col7\" class=\"data row6 col7\" >850000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_21f9e_level0_row7\" class=\"row_heading level0 row7\" >serum_creatinine</th>\n",
+              "      <td id=\"T_21f9e_row7_col0\" class=\"data row7 col0\" >299.000000</td>\n",
+              "      <td id=\"T_21f9e_row7_col1\" class=\"data row7 col1\" >1.393880</td>\n",
+              "      <td id=\"T_21f9e_row7_col2\" class=\"data row7 col2\" >1.034510</td>\n",
+              "      <td id=\"T_21f9e_row7_col3\" class=\"data row7 col3\" >0.500000</td>\n",
+              "      <td id=\"T_21f9e_row7_col4\" class=\"data row7 col4\" >0.900000</td>\n",
+              "      <td id=\"T_21f9e_row7_col5\" class=\"data row7 col5\" >1.100000</td>\n",
+              "      <td id=\"T_21f9e_row7_col6\" class=\"data row7 col6\" >1.400000</td>\n",
+              "      <td id=\"T_21f9e_row7_col7\" class=\"data row7 col7\" >9.400000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_21f9e_level0_row8\" class=\"row_heading level0 row8\" >serum_sodium</th>\n",
+              "      <td id=\"T_21f9e_row8_col0\" class=\"data row8 col0\" >299.000000</td>\n",
+              "      <td id=\"T_21f9e_row8_col1\" class=\"data row8 col1\" >136.625418</td>\n",
+              "      <td id=\"T_21f9e_row8_col2\" class=\"data row8 col2\" >4.412477</td>\n",
+              "      <td id=\"T_21f9e_row8_col3\" class=\"data row8 col3\" >113.000000</td>\n",
+              "      <td id=\"T_21f9e_row8_col4\" class=\"data row8 col4\" >134.000000</td>\n",
+              "      <td id=\"T_21f9e_row8_col5\" class=\"data row8 col5\" >137.000000</td>\n",
+              "      <td id=\"T_21f9e_row8_col6\" class=\"data row8 col6\" >140.000000</td>\n",
+              "      <td id=\"T_21f9e_row8_col7\" class=\"data row8 col7\" >148.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_21f9e_level0_row9\" class=\"row_heading level0 row9\" >sex</th>\n",
+              "      <td id=\"T_21f9e_row9_col0\" class=\"data row9 col0\" >299.000000</td>\n",
+              "      <td id=\"T_21f9e_row9_col1\" class=\"data row9 col1\" >0.648829</td>\n",
+              "      <td id=\"T_21f9e_row9_col2\" class=\"data row9 col2\" >0.478136</td>\n",
+              "      <td id=\"T_21f9e_row9_col3\" class=\"data row9 col3\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row9_col4\" class=\"data row9 col4\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row9_col5\" class=\"data row9 col5\" >1.000000</td>\n",
+              "      <td id=\"T_21f9e_row9_col6\" class=\"data row9 col6\" >1.000000</td>\n",
+              "      <td id=\"T_21f9e_row9_col7\" class=\"data row9 col7\" >1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_21f9e_level0_row10\" class=\"row_heading level0 row10\" >smoking</th>\n",
+              "      <td id=\"T_21f9e_row10_col0\" class=\"data row10 col0\" >299.000000</td>\n",
+              "      <td id=\"T_21f9e_row10_col1\" class=\"data row10 col1\" >0.321070</td>\n",
+              "      <td id=\"T_21f9e_row10_col2\" class=\"data row10 col2\" >0.467670</td>\n",
+              "      <td id=\"T_21f9e_row10_col3\" class=\"data row10 col3\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row10_col4\" class=\"data row10 col4\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row10_col5\" class=\"data row10 col5\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row10_col6\" class=\"data row10 col6\" >1.000000</td>\n",
+              "      <td id=\"T_21f9e_row10_col7\" class=\"data row10 col7\" >1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_21f9e_level0_row11\" class=\"row_heading level0 row11\" >time</th>\n",
+              "      <td id=\"T_21f9e_row11_col0\" class=\"data row11 col0\" >299.000000</td>\n",
+              "      <td id=\"T_21f9e_row11_col1\" class=\"data row11 col1\" >130.260870</td>\n",
+              "      <td id=\"T_21f9e_row11_col2\" class=\"data row11 col2\" >77.614208</td>\n",
+              "      <td id=\"T_21f9e_row11_col3\" class=\"data row11 col3\" >4.000000</td>\n",
+              "      <td id=\"T_21f9e_row11_col4\" class=\"data row11 col4\" >73.000000</td>\n",
+              "      <td id=\"T_21f9e_row11_col5\" class=\"data row11 col5\" >115.000000</td>\n",
+              "      <td id=\"T_21f9e_row11_col6\" class=\"data row11 col6\" >203.000000</td>\n",
+              "      <td id=\"T_21f9e_row11_col7\" class=\"data row11 col7\" >285.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_21f9e_level0_row12\" class=\"row_heading level0 row12\" >DEATH_EVENT</th>\n",
+              "      <td id=\"T_21f9e_row12_col0\" class=\"data row12 col0\" >299.000000</td>\n",
+              "      <td id=\"T_21f9e_row12_col1\" class=\"data row12 col1\" >0.321070</td>\n",
+              "      <td id=\"T_21f9e_row12_col2\" class=\"data row12 col2\" >0.467670</td>\n",
+              "      <td id=\"T_21f9e_row12_col3\" class=\"data row12 col3\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row12_col4\" class=\"data row12 col4\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row12_col5\" class=\"data row12 col5\" >0.000000</td>\n",
+              "      <td id=\"T_21f9e_row12_col6\" class=\"data row12 col6\" >1.000000</td>\n",
+              "      <td id=\"T_21f9e_row12_col7\" class=\"data row12 col7\" >1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Visualizing basic statistical details such as-percentile, mean, std etc. of a data frame or a series of numeric values.**"
+      ],
+      "metadata": {
+        "id": "cKHMOCrqIFG4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig = px.imshow(df.corr(), color_continuous_scale=\"magma\")\n",
+        "fig.update_layout(height=750)\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 767
+        },
+        "id": "U-siMg9GG_CK",
+        "outputId": "9a980ab3-a9c3-49cd-b4ad-9c10d17f68ad"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
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+              "<head><meta charset=\"utf-8\" /></head>\n",
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+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Quick overview or exploratory analysis of data from the existing dataset**"
+      ],
+      "metadata": {
+        "id": "Eihc3j30IVMs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Oversampling(Upsampling) the minority class\n",
+        "\n",
+        "from sklearn.utils import resample\n",
+        "#create two different dataframe of majority and minority class \n",
+        "df_majority = df[(df['DEATH_EVENT']==0)] \n",
+        "df_minority = df[(df['DEATH_EVENT']==1)] \n",
+        "# upsample minority class\n",
+        "df_minority_upsampled = resample(df_minority, \n",
+        "                                 replace=True,    # sample with replacement\n",
+        "                                 n_samples= 203, # to match majority class\n",
+        "                                 random_state=42)  # reproducible results\n",
+        "# Combine majority class with upsampled minority class\n",
+        "df_upsampled = pd.concat([df_minority_upsampled, df_majority])\n",
+        "\n",
+        "print(df_upsampled['DEATH_EVENT'].value_counts())\n",
+        "print(df_upsampled)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OaGjPcfrIU50",
+        "outputId": "9ad20482-999c-4ead-ad17-4161273c9d02"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1    203\n",
+            "0    203\n",
+            "Name: DEATH_EVENT, dtype: int64\n",
+            "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
+            "59   72.0        0                       364         1                 20   \n",
+            "230  60.0        0                       166         0                 30   \n",
+            "15   82.0        1                       379         0                 50   \n",
+            "126  46.0        0                       168         1                 17   \n",
+            "72   85.0        0                      5882         0                 35   \n",
+            "..    ...      ...                       ...       ...                ...   \n",
+            "294  62.0        0                        61         1                 38   \n",
+            "295  55.0        0                      1820         0                 38   \n",
+            "296  45.0        0                      2060         1                 60   \n",
+            "297  45.0        0                      2413         0                 38   \n",
+            "298  50.0        0                       196         0                 45   \n",
+            "\n",
+            "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
+            "59                     1   254000.0               1.3           136    1   \n",
+            "230                    0    62000.0               1.7           127    0   \n",
+            "15                     0    47000.0               1.3           136    1   \n",
+            "126                    1   271000.0               2.1           124    0   \n",
+            "72                     0   243000.0               1.0           132    1   \n",
+            "..                   ...        ...               ...           ...  ...   \n",
+            "294                    1   155000.0               1.1           143    1   \n",
+            "295                    0   270000.0               1.2           139    0   \n",
+            "296                    0   742000.0               0.8           138    0   \n",
+            "297                    0   140000.0               1.4           140    1   \n",
+            "298                    0   395000.0               1.6           136    1   \n",
+            "\n",
+            "     smoking  time  DEATH_EVENT  \n",
+            "59         1    59            1  \n",
+            "230        0   207            1  \n",
+            "15         0    13            1  \n",
+            "126        0   100            1  \n",
+            "72         1    72            1  \n",
+            "..       ...   ...          ...  \n",
+            "294        1   270            0  \n",
+            "295        0   271            0  \n",
+            "296        0   278            0  \n",
+            "297        1   280            0  \n",
+            "298        1   285            0  \n",
+            "\n",
+            "[406 rows x 13 columns]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#MACHINE LEARNING MODELS\n"
+      ],
+      "metadata": {
+        "id": "lhqRRLrsA1uF"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We will be training the models above with the upsampled data"
+      ],
+      "metadata": {
+        "id": "oPhstKYjBdnJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<h3> <b> The classification models used in this problem are as follows: </b></h3>\n",
+        "<h4>\n",
+        "<ul>\n",
+        "<li> Logistic Regression </li>\n",
+        "<li> Random Forest </li>\n",
+        "<li> Nearest Neighbour (KNN) Classifier </li>\n",
+        "<li> Decision Tree </li>\n",
+        "<li> Naive Bayes Classifier </li>\n",
+        "<li> SVM </li>\n",
+        "<li> LightGBM </li>\n",
+        "<li> XGBoost </li>\n",
+        "</h4>"
+      ],
+      "metadata": {
+        "id": "XzLRbKSuBlXS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ddf=df_upsampled\n",
+        "x=ddf.drop(columns=['DEATH_EVENT'])\n",
+        "y=ddf['DEATH_EVENT']"
+      ],
+      "metadata": {
+        "id": "yP55a68ZBEha"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2, random_state=0)"
+      ],
+      "metadata": {
+        "id": "3wrlAxuwBzle"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.preprocessing import StandardScaler as ss\n",
+        "scs = ss()\n",
+        "x_train = scs.fit_transform(x_train)\n",
+        "x_test = scs.transform(x_test)"
+      ],
+      "metadata": {
+        "id": "XUGbt8neB2Ni"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_test={}"
+      ],
+      "metadata": {
+        "id": "j7CHn-9JB5CJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <h1 align=\"center\"> Logistic Regression </h1>"
+      ],
+      "metadata": {
+        "id": "wLno0bDkB-4P"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lr=LogisticRegression(solver='lbfgs', max_iter=100)\n",
+        "lr.fit(x_train,y_train)"
+      ],
+      "metadata": {
+        "id": "-OAVBKoeCFU-",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e9fe9a28-fb32-4cfe-a0b6-388157f64db2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LogisticRegression()"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 43
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_test= lr.predict(x_test)\n",
+        "y_pred_test"
+      ],
+      "metadata": {
+        "id": "24i7Fx-sCHmg",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2df4161a-168d-44ea-998b-cc258446f30c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1,\n",
+              "       1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n",
+              "       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1,\n",
+              "       1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_train= lr.predict(x_train)\n",
+        "y_pred_train"
+      ],
+      "metadata": {
+        "id": "z5-ufc20CJu4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3c9766f3-eb9a-474f-c27b-67817b7b6bdc"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1,\n",
+              "       1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
+              "       0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1,\n",
+              "       1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1,\n",
+              "       0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1,\n",
+              "       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
+              "       1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1,\n",
+              "       0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,\n",
+              "       0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0,\n",
+              "       1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1,\n",
+              "       1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1,\n",
+              "       1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
+              "       1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
+              "       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
+              "       1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Testing accuracy:\")\n",
+        "print( accuracy_score(y_test, y_pred_test)*100)"
+      ],
+      "metadata": {
+        "id": "guw4gD9BCOpC",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c03794bd-a1a0-44f4-e656-d728c614da8d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Testing accuracy:\n",
+            "79.26829268292683\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## <h1 align=\"center\"> Confusion Matrix </h1>\n"
+      ],
+      "metadata": {
+        "id": "MFpui1I-CRLP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = y_pred_test\n",
+        "cm = confusion_matrix(y_test, y_pred)"
+      ],
+      "metadata": {
+        "id": "Mf4d-Z-FCRwW"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Confusion matrix\\n', cm)\n",
+        "\n",
+        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
+        "\n",
+        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
+        "\n",
+        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
+        "\n",
+        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
+      ],
+      "metadata": {
+        "id": "or6y8DDnCVea",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c541538c-20b6-40f1-b923-185e89c1af72"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Confusion matrix\n",
+            " [[31  7]\n",
+            " [10 34]]\n",
+            "\n",
+            "True Positives(TP) =  31\n",
+            "\n",
+            "True Negatives(TN) =  34\n",
+            "\n",
+            "False Positives(FP) =  7\n",
+            "\n",
+            "False Negatives(FN) =  10\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
+        "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
+        "\n",
+        "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGn')"
+      ],
+      "metadata": {
+        "id": "m5DgAmWcCXpj",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 286
+        },
+        "outputId": "5c739954-c9a2-4c4d-cbba-fffd6be93f2c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c1a275410>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 49
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "id": "iihXCcBmCZc1",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "47668b55-c191-4af9-a627-c88ad68b7a9c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.76      0.82      0.78        38\n",
+            "           1       0.83      0.77      0.80        44\n",
+            "\n",
+            "    accuracy                           0.79        82\n",
+            "   macro avg       0.79      0.79      0.79        82\n",
+            "weighted avg       0.80      0.79      0.79        82\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "TP = cm[0,0]\n",
+        "TN = cm[1,1]\n",
+        "FP = cm[0,1]\n",
+        "FN = cm[1,0]\n",
+        "\n",
+        "#Classification error\n",
+        "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
+        "print('Classification error : {0:0.4f}'.format(classification_error))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2Exqa70CKX-5",
+        "outputId": "10194391-2fa0-401f-bda1-32c7e9ac1b91"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification error : 0.2073\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "f1score = f1_score(y_test, y_pred)\n",
+        "accuracy= accuracy_score(y_test, y_pred)\n",
+        "precision= precision_score(y_test, y_pred)\n",
+        "recall= recall_score(y_test, y_pred)\n",
+        "model_test['Logistic Regression']=[f1score , accuracy, precision, recall, classification_error]\n",
+        "\n",
+        "print('f1 Score :',f1score)\n",
+        "print('Accuracy:',accuracy)\n",
+        "print('Recall :',recall)\n",
+        "print('Precision :',precision)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9J-4vAaDKaMP",
+        "outputId": "469fdd0b-0687-41bf-d99d-1d44e57e4f3e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "f1 Score : 0.7999999999999999\n",
+            "Accuracy: 0.7926829268292683\n",
+            "Recall : 0.7727272727272727\n",
+            "Precision : 0.8292682926829268\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <h1 align=\"center\"> Random Forest </h1>"
+      ],
+      "metadata": {
+        "id": "H-uLrAC7KcZm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "clf = RandomForestClassifier(n_estimators = 10, random_state =0)\n",
+        "clf.fit(x_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9Rp5NzhHKfFm",
+        "outputId": "19597a68-9ec9-45bf-8f59-8a35818eb775"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RandomForestClassifier(n_estimators=10, random_state=0)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 53
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_test= clf.predict(x_test)\n",
+        "y_pred_test"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9iPQzB34Kg_g",
+        "outputId": "4f9b454d-a054-4cd5-eb98-5986c5a3e7b3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
+              "       1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
+              "       1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
+              "       1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 54
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_train= clf.predict(x_train)\n",
+        "y_pred_train"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DEiT9yxEKirC",
+        "outputId": "fb94cab6-7107-42df-cb30-244dcca30976"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
+              "       0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
+              "       0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
+              "       0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1,\n",
+              "       1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1,\n",
+              "       0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
+              "       0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
+              "       0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
+              "       1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0,\n",
+              "       1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,\n",
+              "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1,\n",
+              "       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
+              "       1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
+              "       1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
+              "       1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 55
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Training accuracy:\")\n",
+        "print( accuracy_score(y_train, y_pred_train)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CuNJ4wTnKkif",
+        "outputId": "a355a156-3556-4029-abb1-07f4913679e4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training accuracy:\n",
+            "99.38271604938271\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Testing accuracy:\")\n",
+        "print( accuracy_score(y_test, y_pred_test)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-okdzYcDKm7q",
+        "outputId": "45b56400-3f62-4955-ed50-21c922535830"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Testing accuracy:\n",
+            "91.46341463414635\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## <h1 align=\"center\"> Confusion Matrix </h1>"
+      ],
+      "metadata": {
+        "id": "rLz8ZCzyKpK9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = y_pred_test\n",
+        "cm = confusion_matrix(y_test, y_pred)"
+      ],
+      "metadata": {
+        "id": "Yfvo6VNqKsAM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Confusion matrix\\n', cm)\n",
+        "\n",
+        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
+        "\n",
+        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
+        "\n",
+        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
+        "\n",
+        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cfhIV3PjKuGR",
+        "outputId": "a9b88139-ed65-4760-f45c-d69326743e7f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Confusion matrix\n",
+            " [[36  2]\n",
+            " [ 5 39]]\n",
+            "\n",
+            "True Positives(TP) =  36\n",
+            "\n",
+            "True Negatives(TN) =  39\n",
+            "\n",
+            "False Positives(FP) =  2\n",
+            "\n",
+            "False Negatives(FN) =  5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
+        "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
+        "\n",
+        "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGn')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 286
+        },
+        "id": "HRiFw1VNKwOa",
+        "outputId": "72cc5c67-73f4-4f14-9826-da146503cf7a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c19fdbf50>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pZpRi_aSKyak",
+        "outputId": "c17baafa-cfd9-45bd-ea9d-e718359c296f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.88      0.95      0.91        38\n",
+            "           1       0.95      0.89      0.92        44\n",
+            "\n",
+            "    accuracy                           0.91        82\n",
+            "   macro avg       0.91      0.92      0.91        82\n",
+            "weighted avg       0.92      0.91      0.91        82\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "TP = cm[0,0]\n",
+        "TN = cm[1,1]\n",
+        "FP = cm[0,1]\n",
+        "FN = cm[1,0]\n",
+        "\n",
+        "#Classification error\n",
+        "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
+        "print('Classification error : {0:0.4f}'.format(classification_error))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HQXZIQ4NK2GP",
+        "outputId": "28def23c-8b9a-453c-f1ca-f54119235834"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification error : 0.0854\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "f1score = f1_score(y_test, y_pred)\n",
+        "accuracy= accuracy_score(y_test, y_pred)\n",
+        "precision= precision_score(y_test, y_pred)\n",
+        "recall= recall_score(y_test, y_pred)\n",
+        "model_test['Random Forest']=[f1score , accuracy, precision, recall, classification_error]\n",
+        "\n",
+        "print('f1 Score :',f1score)\n",
+        "print('Accuracy:',accuracy)\n",
+        "print('Recall :',recall)\n",
+        "print('Precision :',precision)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HEzohN4pK33Q",
+        "outputId": "dee8a0ad-5644-4d85-ed37-6b673c37fea8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "f1 Score : 0.9176470588235294\n",
+            "Accuracy: 0.9146341463414634\n",
+            "Recall : 0.8863636363636364\n",
+            "Precision : 0.9512195121951219\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <h1 align=\"center\">K Nearest Neighbour (KNN) Classifier </h1>"
+      ],
+      "metadata": {
+        "id": "DRzoM41-K6UJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "knn = KNeighborsClassifier(n_neighbors=5)\n",
+        "knn.fit(x_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NMpmJkb4K6yC",
+        "outputId": "027f5f34-3000-4c3d-9912-7d3d12efc789"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "KNeighborsClassifier()"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 64
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_test = knn.predict(x_test)\n",
+        "y_pred_test"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5DUxcjfpK-CN",
+        "outputId": "1eeff489-f1cf-4ec7-f933-3882355f107e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0,\n",
+              "       1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
+              "       0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
+              "       1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 65
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_train = knn.predict(x_train)\n",
+        "y_pred_train"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BVk8x6wUK_jD",
+        "outputId": "6f1338c8-ee63-45e0-b8c7-a2461a0d58cc"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
+              "       1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0,\n",
+              "       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1,\n",
+              "       0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1,\n",
+              "       0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
+              "       0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
+              "       1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
+              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0,\n",
+              "       1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0,\n",
+              "       1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1,\n",
+              "       1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1,\n",
+              "       1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0,\n",
+              "       0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0,\n",
+              "       1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
+              "       1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 66
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Training accuracy:\")\n",
+        "print( accuracy_score(y_train, y_pred_train)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "K7QB522ILBdy",
+        "outputId": "ef38b3c4-0bfb-4c88-8419-34751c71d977"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training accuracy:\n",
+            "84.25925925925925\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Testing accuracy:\")\n",
+        "print( accuracy_score(y_test, y_pred_test)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0haK05NoLDgm",
+        "outputId": "aa1a3570-a327-4304-a616-6e550619a45f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Testing accuracy:\n",
+            "80.48780487804879\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## <h1 align=\"center\"> Confusion Matrix </h1>"
+      ],
+      "metadata": {
+        "id": "E3EiJBjgLFjU"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = y_pred_test\n",
+        "cm = confusion_matrix(y_test, y_pred)"
+      ],
+      "metadata": {
+        "id": "H_7M_rZ6LGBe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Confusion matrix\\n', cm)\n",
+        "\n",
+        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
+        "\n",
+        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
+        "\n",
+        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
+        "\n",
+        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ysy9Y3QXLJXr",
+        "outputId": "57b0f929-b5dc-475c-d924-e820333901f9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Confusion matrix\n",
+            " [[31  7]\n",
+            " [ 9 35]]\n",
+            "\n",
+            "True Positives(TP) =  31\n",
+            "\n",
+            "True Negatives(TN) =  35\n",
+            "\n",
+            "False Positives(FP) =  7\n",
+            "\n",
+            "False Negatives(FN) =  9\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
+        "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
+        "\n",
+        "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGn')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 290
+        },
+        "id": "BByl0-69LLO8",
+        "outputId": "07b9e930-d124-41eb-95fa-aafd6f56ba0e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c19fe3e10>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 71
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AxmJP_gGLM-8",
+        "outputId": "60e5c2e4-bb65-4915-e247-4169b348563b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.78      0.82      0.79        38\n",
+            "           1       0.83      0.80      0.81        44\n",
+            "\n",
+            "    accuracy                           0.80        82\n",
+            "   macro avg       0.80      0.81      0.80        82\n",
+            "weighted avg       0.81      0.80      0.81        82\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "TP = cm[0,0]\n",
+        "TN = cm[1,1]\n",
+        "FP = cm[0,1]\n",
+        "FN = cm[1,0]\n",
+        "\n",
+        "#Classification error\n",
+        "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
+        "print('Classification error : {0:0.4f}'.format(classification_error))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QtYO9Ew6LOwS",
+        "outputId": "96530586-8918-4111-f5b6-7e4b6932048b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification error : 0.1951\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "f1score = f1_score(y_test, y_pred)\n",
+        "accuracy= accuracy_score(y_test, y_pred)\n",
+        "precision= precision_score(y_test, y_pred)\n",
+        "recall= recall_score(y_test, y_pred)\n",
+        "model_test['KNN']=[f1score , accuracy, precision, recall, classification_error]\n",
+        "\n",
+        "print('f1 Score :',f1score)\n",
+        "print('Accuracy:',accuracy)\n",
+        "print('Recall :',recall)\n",
+        "print('Precision :',precision)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IhtbqRQALQXY",
+        "outputId": "ec7bf7c4-c548-4170-d539-abe8cd2bd963"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "f1 Score : 0.8139534883720929\n",
+            "Accuracy: 0.8048780487804879\n",
+            "Recall : 0.7954545454545454\n",
+            "Precision : 0.8333333333333334\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <h1 align=\"center\"> Decision Tree </h1>"
+      ],
+      "metadata": {
+        "id": "ShW4odsrLSy2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tree = DecisionTreeClassifier()\n",
+        "tree.fit(x_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ny4C0n9dLTO7",
+        "outputId": "3f611e35-18a9-4cc3-b5ca-7c738775f24b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DecisionTreeClassifier()"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 75
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_train = tree.predict(x_train)\n",
+        "y_pred_train"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kxN72NY5LWzP",
+        "outputId": "275c140d-00f4-4f98-92a0-39c6a31f1238"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
+              "       0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
+              "       0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
+              "       0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1,\n",
+              "       1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1,\n",
+              "       0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
+              "       0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
+              "       0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
+              "       1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0,\n",
+              "       1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,\n",
+              "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1,\n",
+              "       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
+              "       1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
+              "       1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
+              "       1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 76
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_test = tree.predict(x_test)\n",
+        "y_pred_test"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TyYgZ6gKLYt8",
+        "outputId": "fec4e98a-e39c-4664-9524-2dd34597086b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
+              "       1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1,\n",
+              "       0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
+              "       1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 77
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Training accuracy:\")\n",
+        "print( accuracy_score(y_train, y_pred_train)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xcyuWXzfLa9F",
+        "outputId": "65ab4c34-d892-4a12-c823-063853b9ef6d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training accuracy:\n",
+            "100.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Testing accuracy:\")\n",
+        "print( accuracy_score(y_test, y_pred_test)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Bs188jyoLc5U",
+        "outputId": "371393cb-851d-4978-8382-d30e64a7e885"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Testing accuracy:\n",
+            "86.58536585365853\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## <h1 align=\"center\"> Confusion Matrix </h1>"
+      ],
+      "metadata": {
+        "id": "OGW_1pvGLf-5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = y_pred_test\n",
+        "cm = confusion_matrix(y_test, y_pred)"
+      ],
+      "metadata": {
+        "id": "XoRx8OiaLgZT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Confusion matrix\\n', cm)\n",
+        "\n",
+        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
+        "\n",
+        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
+        "\n",
+        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
+        "\n",
+        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YPPL6K5yLjvn",
+        "outputId": "f56331ac-b012-49e0-be1f-f7c0514a1b80"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Confusion matrix\n",
+            " [[33  5]\n",
+            " [ 6 38]]\n",
+            "\n",
+            "True Positives(TP) =  33\n",
+            "\n",
+            "True Negatives(TN) =  38\n",
+            "\n",
+            "False Positives(FP) =  5\n",
+            "\n",
+            "False Negatives(FN) =  6\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
+        "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
+        "\n",
+        "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGn')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 286
+        },
+        "id": "NDXRGIYFLlVh",
+        "outputId": "66992354-fcde-4335-b187-a84e1de8e317"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c19e51b10>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 82
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dS4ADoX_LnP7",
+        "outputId": "0002838f-e731-4d07-c6bb-a6039a3fa2e4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.85      0.87      0.86        38\n",
+            "           1       0.88      0.86      0.87        44\n",
+            "\n",
+            "    accuracy                           0.87        82\n",
+            "   macro avg       0.86      0.87      0.87        82\n",
+            "weighted avg       0.87      0.87      0.87        82\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "TP = cm[0,0]\n",
+        "TN = cm[1,1]\n",
+        "FP = cm[0,1]\n",
+        "FN = cm[1,0]\n",
+        "\n",
+        "#Classification error\n",
+        "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
+        "print('Classification error : {0:0.4f}'.format(classification_error))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "g2DTna4bLo4f",
+        "outputId": "dd6a4f4d-ddbf-4f12-83f6-2a42d95c1efa"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification error : 0.1341\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "f1score = f1_score(y_test, y_pred)\n",
+        "accuracy= accuracy_score(y_test, y_pred)\n",
+        "precision= precision_score(y_test, y_pred)\n",
+        "recall= recall_score(y_test, y_pred)\n",
+        "model_test['Decision Tree']=[f1score , accuracy, precision, recall, classification_error]\n",
+        "\n",
+        "print('f1 Score :',f1score)\n",
+        "print('Accuracy:',accuracy)\n",
+        "print('Recall :',recall)\n",
+        "print('Precision :',precision)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_Es0i5cwLqZi",
+        "outputId": "a78791a5-77c7-4a32-e3d3-7813a6b5f76a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "f1 Score : 0.8735632183908046\n",
+            "Accuracy: 0.8658536585365854\n",
+            "Recall : 0.8636363636363636\n",
+            "Precision : 0.8837209302325582\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <h1 align=\"center\"> Naive Bayes Classifier </h1>"
+      ],
+      "metadata": {
+        "id": "OZ2on-TjLsrp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gnb = GaussianNB()\n",
+        "gnb.fit(x_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ofBjhX_ULugC",
+        "outputId": "71439717-cb58-494b-a7e9-c3bfd8c765da"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "GaussianNB()"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 86
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_test = gnb.predict(x_test)\n",
+        "y_pred_test"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RE8k4BtQLxCf",
+        "outputId": "06c71432-adde-4bc1-af94-a8936532215b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
+              "       1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
+              "       1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1,\n",
+              "       0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 87
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_train = gnb.predict(x_train)\n",
+        "y_pred_train"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fdKMRLMwLykI",
+        "outputId": "34b0f9b9-3182-4d3e-b36f-ed1bbd516ce2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,\n",
+              "       1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1,\n",
+              "       0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1,\n",
+              "       0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1,\n",
+              "       0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1,\n",
+              "       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,\n",
+              "       0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1,\n",
+              "       0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,\n",
+              "       0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
+              "       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1,\n",
+              "       1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
+              "       1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
+              "       0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n",
+              "       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
+              "       1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 88
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Training accuracy:\")\n",
+        "print( accuracy_score(y_train, y_pred_train)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Fo6H6AETL0VD",
+        "outputId": "ad8b6832-1e4e-46b1-8608-77a31f192b2a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training accuracy:\n",
+            "77.1604938271605\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Testing accuracy:\")\n",
+        "print( accuracy_score(y_test, y_pred_test)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qzsBl1IhL2Ab",
+        "outputId": "ce0c7db7-a46f-4f67-dac4-7013ee172b92"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Testing accuracy:\n",
+            "75.60975609756098\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## <h1 align=\"center\"> Confusion Matrix </h1>"
+      ],
+      "metadata": {
+        "id": "j2dWPb6gL4P_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = y_pred_test\n",
+        "cm = confusion_matrix(y_test, y_pred)"
+      ],
+      "metadata": {
+        "id": "6uwTGMTmL4lk"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Confusion matrix\\n', cm)\n",
+        "\n",
+        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
+        "\n",
+        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
+        "\n",
+        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
+        "\n",
+        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xVntBf2HL7Ru",
+        "outputId": "76b8d5e0-e32b-4039-e1be-71b9b252be86"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Confusion matrix\n",
+            " [[35  3]\n",
+            " [17 27]]\n",
+            "\n",
+            "True Positives(TP) =  35\n",
+            "\n",
+            "True Negatives(TN) =  27\n",
+            "\n",
+            "False Positives(FP) =  3\n",
+            "\n",
+            "False Negatives(FN) =  17\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
+        "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
+        "\n",
+        "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGn')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 290
+        },
+        "id": "dyBOQDyHL8yM",
+        "outputId": "fd22150a-3c74-4296-f11f-fb2311f6b528"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c19ecc0d0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 93
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-C3MUR51L-3M",
+        "outputId": "7e1932e8-b8ee-4201-c3a5-357fbf13b5bc"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.67      0.92      0.78        38\n",
+            "           1       0.90      0.61      0.73        44\n",
+            "\n",
+            "    accuracy                           0.76        82\n",
+            "   macro avg       0.79      0.77      0.75        82\n",
+            "weighted avg       0.79      0.76      0.75        82\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "TP = cm[0,0]\n",
+        "TN = cm[1,1]\n",
+        "FP = cm[0,1]\n",
+        "FN = cm[1,0]\n",
+        "\n",
+        "#Classification error\n",
+        "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
+        "print('Classification error : {0:0.4f}'.format(classification_error))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "np7JydYxMAjK",
+        "outputId": "7a88b5ca-5a82-46df-dddc-a0bd2a5990fd"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification error : 0.2439\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "f1score = f1_score(y_test, y_pred)\n",
+        "accuracy= accuracy_score(y_test, y_pred)\n",
+        "precision= precision_score(y_test, y_pred)\n",
+        "recall= recall_score(y_test, y_pred)\n",
+        "model_test['Naive Bayes']=[f1score , accuracy, precision, recall, classification_error]\n",
+        "\n",
+        "print('f1 Score :',f1score)\n",
+        "print('Accuracy:',accuracy)\n",
+        "print('Recall :',recall)\n",
+        "print('Precision :',precision)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sXZP-QCfMCFA",
+        "outputId": "865c880e-a28f-4909-8fdc-eede98bdf190"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "f1 Score : 0.7297297297297297\n",
+            "Accuracy: 0.7560975609756098\n",
+            "Recall : 0.6136363636363636\n",
+            "Precision : 0.9\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <h1 align=\"center\"> SVM </h1>"
+      ],
+      "metadata": {
+        "id": "AlQZjNrQMD-B"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "classifier = SVC(kernel = 'rbf')\n",
+        "classifier.fit(x_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pGaRBXMrMENa",
+        "outputId": "853fb2b5-fb38-48ed-c5b8-e55449fa0a3c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SVC()"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 97
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_train = classifier.predict(x_train)\n",
+        "y_pred_train"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EcyjCEZ8MFvD",
+        "outputId": "1c179137-e9dd-42cc-d188-ca1b4efd5d26"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1,\n",
+              "       1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
+              "       0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
+              "       0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1,\n",
+              "       1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1,\n",
+              "       0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
+              "       0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
+              "       0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
+              "       1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0,\n",
+              "       1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1,\n",
+              "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1,\n",
+              "       1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
+              "       1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
+              "       1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
+              "       1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 98
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_test = classifier.predict(x_test)\n",
+        "y_pred_test"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "61zTrIl3MJm4",
+        "outputId": "9a2c03e5-063c-4566-9d84-1a62ae4b3e66"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
+              "       1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1,\n",
+              "       0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0,\n",
+              "       1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 99
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Training accuracy:\")\n",
+        "print( accuracy_score(y_train, y_pred_train)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6JRfMzz9MLMq",
+        "outputId": "7358e39f-5919-420a-d685-cedae8a23e35"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training accuracy:\n",
+            "92.5925925925926\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Testing accuracy:\")\n",
+        "print( accuracy_score(y_test, y_pred_test)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5bzUZM_lMNDA",
+        "outputId": "ec255c74-efa6-4d2c-8e89-a546d473007d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Testing accuracy:\n",
+            "85.36585365853658\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## <h1 align=\"center\"> Confusion Matrix </h1>"
+      ],
+      "metadata": {
+        "id": "seLCjUmlMRYa"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = y_pred_test\n",
+        "cm = confusion_matrix(y_test, y_pred)"
+      ],
+      "metadata": {
+        "id": "Lrd3CIy6MRyK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Confusion matrix\\n', cm)\n",
+        "\n",
+        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
+        "\n",
+        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
+        "\n",
+        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
+        "\n",
+        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5NFpqQ3YMVd3",
+        "outputId": "35125eb9-41ab-4e24-d7a4-e465f040cfe3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Confusion matrix\n",
+            " [[34  4]\n",
+            " [ 8 36]]\n",
+            "\n",
+            "True Positives(TP) =  34\n",
+            "\n",
+            "True Negatives(TN) =  36\n",
+            "\n",
+            "False Positives(FP) =  4\n",
+            "\n",
+            "False Negatives(FN) =  8\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
+        "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
+        "\n",
+        "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGn')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 286
+        },
+        "id": "CDswUFknMXk-",
+        "outputId": "56f29499-b75a-4437-865c-1f8adcc8cdc1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c19d4c110>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 104
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7RjoOTTpMZV1",
+        "outputId": "9e2101e6-1673-4806-82b2-7373553ea59f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.81      0.89      0.85        38\n",
+            "           1       0.90      0.82      0.86        44\n",
+            "\n",
+            "    accuracy                           0.85        82\n",
+            "   macro avg       0.85      0.86      0.85        82\n",
+            "weighted avg       0.86      0.85      0.85        82\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "TP = cm[0,0]\n",
+        "TN = cm[1,1]\n",
+        "FP = cm[0,1]\n",
+        "FN = cm[1,0]\n",
+        "\n",
+        "#Classification error\n",
+        "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
+        "print('Classification error : {0:0.4f}'.format(classification_error))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UVRIP0OvMa4c",
+        "outputId": "a7bce66a-eda1-4316-b4a3-57f304aa576a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification error : 0.1463\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "f1score = f1_score(y_test, y_pred)\n",
+        "accuracy= accuracy_score(y_test, y_pred)\n",
+        "precision= precision_score(y_test, y_pred)\n",
+        "recall= recall_score(y_test, y_pred)\n",
+        "model_test['SVM']=[f1score , accuracy, precision, recall, classification_error]\n",
+        "\n",
+        "print('f1 Score :',f1score)\n",
+        "print('Accuracy:',accuracy)\n",
+        "print('Recall :',recall)\n",
+        "print('Precision :',precision)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QFBMrdqjMcs1",
+        "outputId": "7e5906ca-5f2c-4f2f-94ec-9319aee712f5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "f1 Score : 0.8571428571428572\n",
+            "Accuracy: 0.8536585365853658\n",
+            "Recall : 0.8181818181818182\n",
+            "Precision : 0.9\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <h1 align=\"center\">LightGBM </h1>"
+      ],
+      "metadata": {
+        "id": "0poRNuUHMfnX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "clf = LGBMClassifier()\n",
+        "clf.fit(x_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CRdQHywzMgBY",
+        "outputId": "eb49220a-dfef-4bcf-cf64-c98f4f64decc"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LGBMClassifier()"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 108
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Prediction\n",
+        "y_pred_train = clf.predict(x_train)\n",
+        "print(y_pred_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ditmtzwlMisZ",
+        "outputId": "0bcc1d2d-bc03-4434-8f9c-72300bf0580e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1 0 1 0 0 0 0 1 1 0 1 1 1 1\n",
+            " 1 0 1 1 0 0 1 0 0 1 0 0 1 0 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 0 0 0 1 0 1 0 1\n",
+            " 0 1 0 1 0 0 0 1 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 1 1 0\n",
+            " 0 1 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0 0 1 1\n",
+            " 0 1 0 0 1 1 0 0 0 0 0 1 1 1 1 1 0 1 0 1 0 0 1 1 1 1 1 0 1 0 1 0 0 0 0 0 1\n",
+            " 0 0 1 0 1 1 1 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 0 0 0 0 1 0 1 0 1 1 1 1\n",
+            " 1 1 0 1 0 0 1 0 1 0 1 0 1 1 0 1 0 0 0 1 1 1 1 0 0 0 0 0 1 1 0 0 1 1 0 1 0\n",
+            " 1 0 0 0 1 1 0 0 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 1\n",
+            " 0 1 0 1 1 0 0 1 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 0 1 1 1 1]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Prediction\n",
+        "y_pred_test = clf.predict(x_test)"
+      ],
+      "metadata": {
+        "id": "s3sTc1E0MkcD"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Training accuracy:\")\n",
+        "print( accuracy_score(y_train, y_pred_train)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9GYlWZLrMmRR",
+        "outputId": "dc9bb61c-1c98-4623-e57e-f5def12114e0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training accuracy:\n",
+            "100.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Testing accuracy:\")\n",
+        "print( accuracy_score(y_test, y_pred_test)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uJiA27DSMnsK",
+        "outputId": "1aadd5b2-7d13-4d3a-c5e5-0e4dfb47e5aa"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Testing accuracy:\n",
+            "95.1219512195122\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## <h1 align=\"center\"> Confusion Matrix </h1>"
+      ],
+      "metadata": {
+        "id": "reVTVzdgMte0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = y_pred_test\n",
+        "cm = confusion_matrix(y_test, y_pred)"
+      ],
+      "metadata": {
+        "id": "T7knVgQVMvnR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Confusion matrix\\n', cm)\n",
+        "\n",
+        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
+        "\n",
+        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
+        "\n",
+        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
+        "\n",
+        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zpqT3fRdMxJW",
+        "outputId": "41c695d3-f511-4e0d-e386-759f82e4d9cf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Confusion matrix\n",
+            " [[35  3]\n",
+            " [ 1 43]]\n",
+            "\n",
+            "True Positives(TP) =  35\n",
+            "\n",
+            "True Negatives(TN) =  43\n",
+            "\n",
+            "False Positives(FP) =  3\n",
+            "\n",
+            "False Negatives(FN) =  1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
+        "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
+        "\n",
+        "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGn')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 286
+        },
+        "id": "vVwFnS99My0P",
+        "outputId": "4ee4d9f1-dbe7-4505-a883-b564c669445c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c194192d0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 115
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "B-cv2nRJM0p8",
+        "outputId": "6e87f260-6310-429c-cac2-8ad3178451f0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.97      0.92      0.95        38\n",
+            "           1       0.93      0.98      0.96        44\n",
+            "\n",
+            "    accuracy                           0.95        82\n",
+            "   macro avg       0.95      0.95      0.95        82\n",
+            "weighted avg       0.95      0.95      0.95        82\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "TP = cm[0,0]\n",
+        "TN = cm[1,1]\n",
+        "FP = cm[0,1]\n",
+        "FN = cm[1,0]\n",
+        "\n",
+        "#Classification error\n",
+        "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
+        "print('Classification error : {0:0.4f}'.format(classification_error))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "abxZ3aIWM2Rv",
+        "outputId": "57ba48ea-5cda-4ae4-cc29-c606579d9d50"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification error : 0.0488\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "f1score = f1_score(y_test, y_pred)\n",
+        "accuracy= accuracy_score(y_test, y_pred)\n",
+        "precision= precision_score(y_test, y_pred)\n",
+        "recall= recall_score(y_test, y_pred)\n",
+        "model_test['LightGBM']=[f1score , accuracy, precision, recall, classification_error]\n",
+        "\n",
+        "print('f1 Score :',f1score)\n",
+        "print('Accuracy:',accuracy)\n",
+        "print('Recall :',recall)\n",
+        "print('Precision :',precision)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "riacw6hgM4B4",
+        "outputId": "aa2df160-bd9a-4d22-9fe0-e697f35c6697"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "f1 Score : 0.9555555555555557\n",
+            "Accuracy: 0.9512195121951219\n",
+            "Recall : 0.9772727272727273\n",
+            "Precision : 0.9347826086956522\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <h1 align=\"center\"> XGBoost <h1>"
+      ],
+      "metadata": {
+        "id": "2z_VQSuEM6KA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "xg = XGBClassifier()\n",
+        "xg.fit(x_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CiZTkkucM8MO",
+        "outputId": "bebabf86-25af-4a58-f0d5-b7192d4c8cb2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "XGBClassifier()"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 119
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_train = xg.predict(x_train)\n",
+        "#convert into binary values\n",
+        "for i in range(0, len(y_pred_train)):\n",
+        "    if y_pred_train[i]>= 0.5:       # setting threshold to .5\n",
+        "       y_pred_train[i]=1\n",
+        "    else:  \n",
+        "       y_pred_train[i]=0"
+      ],
+      "metadata": {
+        "id": "aUHdpr0yM-e-"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_test = xg.predict(x_test)\n",
+        "for i in range(0, len(y_pred_test)):\n",
+        "    if y_pred_test[i]>= 0.5:       # setting threshold to .5\n",
+        "       y_pred_test[i]=1\n",
+        "    else:  \n",
+        "       y_pred_test[i]=0"
+      ],
+      "metadata": {
+        "id": "KkRVceIANAWB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Training accuracy:\")\n",
+        "print( accuracy_score(y_train, y_pred_train)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IVtXponzNKDS",
+        "outputId": "55f380cb-c834-408b-f9ae-ef91cf352c1b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training accuracy:\n",
+            "99.07407407407408\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## <h1 align=\"center\"> Confusion Matrix </h1>"
+      ],
+      "metadata": {
+        "id": "Pe9ylIAiNkCH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = y_pred_test\n",
+        "cm = confusion_matrix(y_test, y_pred)"
+      ],
+      "metadata": {
+        "id": "_dL_LdIvNkaT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Confusion matrix\\n', cm)\n",
+        "\n",
+        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
+        "\n",
+        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
+        "\n",
+        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
+        "\n",
+        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BK26SAbzNoPs",
+        "outputId": "f6bedbeb-2dc7-4b9f-ad79-70c56d17a5f5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Confusion matrix\n",
+            " [[35  3]\n",
+            " [ 4 40]]\n",
+            "\n",
+            "True Positives(TP) =  35\n",
+            "\n",
+            "True Negatives(TN) =  40\n",
+            "\n",
+            "False Positives(FP) =  3\n",
+            "\n",
+            "False Negatives(FN) =  4\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
+        "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
+        "\n",
+        "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGn')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 290
+        },
+        "id": "hXUHpzHjN9r-",
+        "outputId": "dc896785-09f0-4b63-d6cd-c778203952cf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c19cda210>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 125
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZxRujgmXOAFi",
+        "outputId": "450b946f-aa76-4d39-dbd4-9300344a2a32"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.90      0.92      0.91        38\n",
+            "           1       0.93      0.91      0.92        44\n",
+            "\n",
+            "    accuracy                           0.91        82\n",
+            "   macro avg       0.91      0.92      0.91        82\n",
+            "weighted avg       0.92      0.91      0.91        82\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "TP = cm[0,0]\n",
+        "TN = cm[1,1]\n",
+        "FP = cm[0,1]\n",
+        "FN = cm[1,0]\n",
+        "\n",
+        "#Classification error\n",
+        "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
+        "print('Classification error : {0:0.4f}'.format(classification_error))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PSzOUlauOBuB",
+        "outputId": "3d98895f-b5c8-448a-a9fe-23f79d300ef4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification error : 0.0854\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "f1score = f1_score(y_test, y_pred)\n",
+        "accuracy= accuracy_score(y_test, y_pred)\n",
+        "precision= precision_score(y_test, y_pred)\n",
+        "recall= recall_score(y_test, y_pred)\n",
+        "model_test['XGBoost']=[f1score , accuracy, precision, recall, classification_error]\n",
+        "\n",
+        "print('f1 Score :',f1score)\n",
+        "print('Accuracy:',accuracy)\n",
+        "print('Recall :',recall)\n",
+        "print('Precision :',precision)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QewgeQyBODcf",
+        "outputId": "83c16434-ab27-4dd2-db62-972073fef912"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "f1 Score : 0.9195402298850575\n",
+            "Accuracy: 0.9146341463414634\n",
+            "Recall : 0.9090909090909091\n",
+            "Precision : 0.9302325581395349\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <b> <h1 align=\"center\"> MODEL COMPARISON </h1> </b>"
+      ],
+      "metadata": {
+        "id": "BbpwfBsTOF6v"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#comparing all the model algorithms together\n",
+        "model_test"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mCsTWJSnOJSC",
+        "outputId": "24a4f0c5-dbdb-43da-e524-03c46f7c3915"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'Decision Tree': [0.8735632183908046,\n",
+              "  0.8658536585365854,\n",
+              "  0.8837209302325582,\n",
+              "  0.8636363636363636,\n",
+              "  0.13414634146341464],\n",
+              " 'KNN': [0.8139534883720929,\n",
+              "  0.8048780487804879,\n",
+              "  0.8333333333333334,\n",
+              "  0.7954545454545454,\n",
+              "  0.1951219512195122],\n",
+              " 'LightGBM': [0.9555555555555557,\n",
+              "  0.9512195121951219,\n",
+              "  0.9347826086956522,\n",
+              "  0.9772727272727273,\n",
+              "  0.04878048780487805],\n",
+              " 'Logistic Regression': [0.7999999999999999,\n",
+              "  0.7926829268292683,\n",
+              "  0.8292682926829268,\n",
+              "  0.7727272727272727,\n",
+              "  0.2073170731707317],\n",
+              " 'Naive Bayes': [0.7297297297297297,\n",
+              "  0.7560975609756098,\n",
+              "  0.9,\n",
+              "  0.6136363636363636,\n",
+              "  0.24390243902439024],\n",
+              " 'Random Forest': [0.9176470588235294,\n",
+              "  0.9146341463414634,\n",
+              "  0.9512195121951219,\n",
+              "  0.8863636363636364,\n",
+              "  0.08536585365853659],\n",
+              " 'SVM': [0.8571428571428572,\n",
+              "  0.8536585365853658,\n",
+              "  0.9,\n",
+              "  0.8181818181818182,\n",
+              "  0.14634146341463414],\n",
+              " 'XGBoost': [0.9195402298850575,\n",
+              "  0.9146341463414634,\n",
+              "  0.9302325581395349,\n",
+              "  0.9090909090909091,\n",
+              "  0.08536585365853659]}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 129
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Mapping the F1 score, accuracy, precision, recall and classification error of all the models\n",
+        "model_comp_df = pd.DataFrame.from_dict(model_test).T\n",
+        "model_comp_df.columns = ['F1 Score','Accuracy','Precision','Recall','Classification Error']\n",
+        "model_comp_df = model_comp_df.sort_values('F1 Score', ascending=True)\n",
+        "model_comp_df.style.background_gradient(cmap='magma')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "-PBOdO2hOK8d",
+        "outputId": "eb825277-5c0e-4441-ca9d-3caad4254528"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
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+              "  background-color: #fea772;\n",
+              "  color: #000000;\n",
+              "}\n",
+              "#T_5b1d8_row5_col3 {\n",
+              "  background-color: #fb8761;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5b1d8_row6_col0 {\n",
+              "  background-color: #feb47b;\n",
+              "  color: #000000;\n",
+              "}\n",
+              "#T_5b1d8_row6_col2 {\n",
+              "  background-color: #feac76;\n",
+              "  color: #000000;\n",
+              "}\n",
+              "#T_5b1d8_row7_col2 {\n",
+              "  background-color: #febf84;\n",
+              "  color: #000000;\n",
+              "}\n",
+              "</style>\n",
+              "<table id=\"T_5b1d8_\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th class=\"blank level0\" >&nbsp;</th>\n",
+              "      <th class=\"col_heading level0 col0\" >F1 Score</th>\n",
+              "      <th class=\"col_heading level0 col1\" >Accuracy</th>\n",
+              "      <th class=\"col_heading level0 col2\" >Precision</th>\n",
+              "      <th class=\"col_heading level0 col3\" >Recall</th>\n",
+              "      <th class=\"col_heading level0 col4\" >Classification Error</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5b1d8_level0_row0\" class=\"row_heading level0 row0\" >Naive Bayes</th>\n",
+              "      <td id=\"T_5b1d8_row0_col0\" class=\"data row0 col0\" >0.729730</td>\n",
+              "      <td id=\"T_5b1d8_row0_col1\" class=\"data row0 col1\" >0.756098</td>\n",
+              "      <td id=\"T_5b1d8_row0_col2\" class=\"data row0 col2\" >0.900000</td>\n",
+              "      <td id=\"T_5b1d8_row0_col3\" class=\"data row0 col3\" >0.613636</td>\n",
+              "      <td id=\"T_5b1d8_row0_col4\" class=\"data row0 col4\" >0.243902</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5b1d8_level0_row1\" class=\"row_heading level0 row1\" >Logistic Regression</th>\n",
+              "      <td id=\"T_5b1d8_row1_col0\" class=\"data row1 col0\" >0.800000</td>\n",
+              "      <td id=\"T_5b1d8_row1_col1\" class=\"data row1 col1\" >0.792683</td>\n",
+              "      <td id=\"T_5b1d8_row1_col2\" class=\"data row1 col2\" >0.829268</td>\n",
+              "      <td id=\"T_5b1d8_row1_col3\" class=\"data row1 col3\" >0.772727</td>\n",
+              "      <td id=\"T_5b1d8_row1_col4\" class=\"data row1 col4\" >0.207317</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5b1d8_level0_row2\" class=\"row_heading level0 row2\" >KNN</th>\n",
+              "      <td id=\"T_5b1d8_row2_col0\" class=\"data row2 col0\" >0.813953</td>\n",
+              "      <td id=\"T_5b1d8_row2_col1\" class=\"data row2 col1\" >0.804878</td>\n",
+              "      <td id=\"T_5b1d8_row2_col2\" class=\"data row2 col2\" >0.833333</td>\n",
+              "      <td id=\"T_5b1d8_row2_col3\" class=\"data row2 col3\" >0.795455</td>\n",
+              "      <td id=\"T_5b1d8_row2_col4\" class=\"data row2 col4\" >0.195122</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5b1d8_level0_row3\" class=\"row_heading level0 row3\" >SVM</th>\n",
+              "      <td id=\"T_5b1d8_row3_col0\" class=\"data row3 col0\" >0.857143</td>\n",
+              "      <td id=\"T_5b1d8_row3_col1\" class=\"data row3 col1\" >0.853659</td>\n",
+              "      <td id=\"T_5b1d8_row3_col2\" class=\"data row3 col2\" >0.900000</td>\n",
+              "      <td id=\"T_5b1d8_row3_col3\" class=\"data row3 col3\" >0.818182</td>\n",
+              "      <td id=\"T_5b1d8_row3_col4\" class=\"data row3 col4\" >0.146341</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5b1d8_level0_row4\" class=\"row_heading level0 row4\" >Decision Tree</th>\n",
+              "      <td id=\"T_5b1d8_row4_col0\" class=\"data row4 col0\" >0.873563</td>\n",
+              "      <td id=\"T_5b1d8_row4_col1\" class=\"data row4 col1\" >0.865854</td>\n",
+              "      <td id=\"T_5b1d8_row4_col2\" class=\"data row4 col2\" >0.883721</td>\n",
+              "      <td id=\"T_5b1d8_row4_col3\" class=\"data row4 col3\" >0.863636</td>\n",
+              "      <td id=\"T_5b1d8_row4_col4\" class=\"data row4 col4\" >0.134146</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5b1d8_level0_row5\" class=\"row_heading level0 row5\" >Random Forest</th>\n",
+              "      <td id=\"T_5b1d8_row5_col0\" class=\"data row5 col0\" >0.917647</td>\n",
+              "      <td id=\"T_5b1d8_row5_col1\" class=\"data row5 col1\" >0.914634</td>\n",
+              "      <td id=\"T_5b1d8_row5_col2\" class=\"data row5 col2\" >0.951220</td>\n",
+              "      <td id=\"T_5b1d8_row5_col3\" class=\"data row5 col3\" >0.886364</td>\n",
+              "      <td id=\"T_5b1d8_row5_col4\" class=\"data row5 col4\" >0.085366</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5b1d8_level0_row6\" class=\"row_heading level0 row6\" >XGBoost</th>\n",
+              "      <td id=\"T_5b1d8_row6_col0\" class=\"data row6 col0\" >0.919540</td>\n",
+              "      <td id=\"T_5b1d8_row6_col1\" class=\"data row6 col1\" >0.914634</td>\n",
+              "      <td id=\"T_5b1d8_row6_col2\" class=\"data row6 col2\" >0.930233</td>\n",
+              "      <td id=\"T_5b1d8_row6_col3\" class=\"data row6 col3\" >0.909091</td>\n",
+              "      <td id=\"T_5b1d8_row6_col4\" class=\"data row6 col4\" >0.085366</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5b1d8_level0_row7\" class=\"row_heading level0 row7\" >LightGBM</th>\n",
+              "      <td id=\"T_5b1d8_row7_col0\" class=\"data row7 col0\" >0.955556</td>\n",
+              "      <td id=\"T_5b1d8_row7_col1\" class=\"data row7 col1\" >0.951220</td>\n",
+              "      <td id=\"T_5b1d8_row7_col2\" class=\"data row7 col2\" >0.934783</td>\n",
+              "      <td id=\"T_5b1d8_row7_col3\" class=\"data row7 col3\" >0.977273</td>\n",
+              "      <td id=\"T_5b1d8_row7_col4\" class=\"data row7 col4\" >0.048780</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 130
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<b> Explanation: </b>\n",
+        "<ul>\n",
+        "<li>LightGBM shows the highest accuracy of 95.122% with a recall of 97.7%\n",
+        "<li> Random Forest shows an accuracy of 91.4634% with a precesion value 95.12%\n",
+        "<li> XGBoost also shows an accuracy of 91.4634% with a precision of 93.02%"
+      ],
+      "metadata": {
+        "id": "YDNq9dskOPbz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Plotting the above map\n",
+        "\n",
+        "fig = go.Figure(data=[\n",
+        "    go.Bar(name='F1 Score', y=model_comp_df.index, x=model_comp_df['F1 Score'], orientation='h'),\n",
+        "    go.Bar(name='Accuracy', y=model_comp_df.index, x=model_comp_df['Accuracy'], orientation='h'),\n",
+        "    go.Bar(name='Precision', y=model_comp_df.index, x=model_comp_df['Precision'], orientation='h'),\n",
+        "    go.Bar(name='Recall', y=model_comp_df.index, x=model_comp_df['Recall'], orientation='h'),\n",
+        "    go.Bar(name='Classification Error', y=model_comp_df.index, x=model_comp_df['Classification Error'], orientation='h')\n",
+        "])\n",
+        "fig.update_layout(autosize=False, width=1100,\n",
+        "    height=600,barmode='group')\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 617
+        },
+        "id": "L7FXU09qOSkW",
+        "outputId": "0163f15e-976f-4ccf-afc6-5cfb6de2e108"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
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+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
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+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<b> \n",
+        "<ul>\n",
+        "<li>It can clearly be seen that LightGBM, XGBoost and Random Forest models perform the best in that order respectively\n",
+        "<li> LightGBM has minimum classification error out of all the models\n",
+        "<li> LightGBM has maximum recall value\n",
+        "<li>Random Forest has maximum precision value\n",
+        "<li> LightGBM is also the most accurate model\n"
+      ],
+      "metadata": {
+        "id": "fSMC4mfzOUvB"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<h1 align=\"center\"> <b> TOP 3 MODELS"
+      ],
+      "metadata": {
+        "id": "fsuXmNHJOXra"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<b> \n",
+        "<ol>\n",
+        "<li>LightGBM \n",
+        "<li>Random Forest\n",
+        "<li>XGBoost\n",
+        "</b>\n",
+        "</ol>"
+      ],
+      "metadata": {
+        "id": "1-4H_tatOavN"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<h3> <b> Hyper Paramater tuning </b></h3>\n",
+        "<ul>\n",
+        "<li>Cross-validation is the process of splitting the same dataset in K-partitions, and for each split, we search the whole grid of hyperparameters to an algorithm, in a brute force manner of trying every combination.\n",
+        "<li>Grid Search with Cross-Validation (GridSearchCV) is a brute force on finding the best hyperparameters for a specific dataset and model.\n",
+        "<li>We will use GridSearchCV to tune the parameters to get the best result from our top 3 models"
+      ],
+      "metadata": {
+        "id": "O1-GagmROdRu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_comp={}"
+      ],
+      "metadata": {
+        "id": "GAVMdi5DOfP2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <h1 align=\"center\"> Random Forest </h1>"
+      ],
+      "metadata": {
+        "id": "pCUzoaeQOjzH"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In Random Forest, we are going to use GridSearchCV to find the best paramteres to hypertune our model. This will increase the model's accuracy and other factors. "
+      ],
+      "metadata": {
+        "id": "B6k1et2xOnYy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import GridSearchCV\n",
+        "from sklearn.datasets import make_classification\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "# Building a classification task using 3 informative features\n",
+        "X, y = make_classification(n_samples=1000,\n",
+        "                           n_features=10,\n",
+        "                           n_informative=3,\n",
+        "                           n_redundant=0,\n",
+        "                           n_repeated=0,\n",
+        "                           n_classes=2,\n",
+        "                           random_state=0,\n",
+        "                           shuffle=False)\n",
+        "\n",
+        "\n",
+        "rfc = RandomForestClassifier(n_jobs=-1,max_features= 'sqrt' ,n_estimators=50, oob_score = True) \n",
+        "\n",
+        "param_grid = { \n",
+        "    'n_estimators': [200, 700],\n",
+        "    'max_features': ['auto', 'sqrt', 'log2']\n",
+        "}\n",
+        "\n",
+        "CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)\n",
+        "CV_rfc.fit(X, y)\n",
+        "print(CV_rfc.best_params_)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VqXx04WqOs-A",
+        "outputId": "88863616-01c6-4e4d-d240-c11a29032987"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'max_features': 'log2', 'n_estimators': 200}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "clf = RandomForestClassifier(n_estimators = 700, max_features='sqrt', random_state =0)\n",
+        "clf.fit(x_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YMT7dp7CO0yY",
+        "outputId": "bffff4f5-261d-44ab-ee57-4f086f3848aa"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RandomForestClassifier(max_features='sqrt', n_estimators=700, random_state=0)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 134
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_test= clf.predict(x_test)\n",
+        "y_pred_test"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "U_wd8cP0O25V",
+        "outputId": "3891b05c-7436-49d5-e405-7aef087ce69b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
+              "       1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1,\n",
+              "       1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
+              "       1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 135
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_train= clf.predict(x_train)\n",
+        "y_pred_train"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HVBGdj7LO4cJ",
+        "outputId": "2c53e80d-eeee-487e-a044-177547c61c2b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
+              "       0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
+              "       0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
+              "       0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1,\n",
+              "       1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1,\n",
+              "       0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
+              "       0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
+              "       0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
+              "       1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0,\n",
+              "       1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,\n",
+              "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1,\n",
+              "       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
+              "       1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
+              "       1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
+              "       1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 136
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Training accuracy:\")\n",
+        "print( accuracy_score(y_train, y_pred_train)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pB3Sn0iXO6QA",
+        "outputId": "e63bfb7a-2762-45c4-a8ed-cb70973adc17"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training accuracy:\n",
+            "100.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Testing accuracy:\")\n",
+        "print( accuracy_score(y_test, y_pred_test)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CGL75bN3O8N0",
+        "outputId": "3e6639bb-1229-4b44-9d7b-79c01facd762"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Testing accuracy:\n",
+            "92.6829268292683\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## <h1 align=\"center\"> Confusion Matrix </h1>"
+      ],
+      "metadata": {
+        "id": "mrZwZUB1O-Wj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = y_pred_test\n",
+        "cm = confusion_matrix(y_test, y_pred)"
+      ],
+      "metadata": {
+        "id": "WHUJcn2JPAMP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Confusion matrix\\n', cm)\n",
+        "\n",
+        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
+        "\n",
+        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
+        "\n",
+        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
+        "\n",
+        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oMKeTK3LPB15",
+        "outputId": "1732398f-2b45-4567-e30f-dd1be64a6225"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Confusion matrix\n",
+            " [[36  2]\n",
+            " [ 4 40]]\n",
+            "\n",
+            "True Positives(TP) =  36\n",
+            "\n",
+            "True Negatives(TN) =  40\n",
+            "\n",
+            "False Positives(FP) =  2\n",
+            "\n",
+            "False Negatives(FN) =  4\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
+        "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
+        "\n",
+        "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGn')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 290
+        },
+        "id": "ysPYHRX_PDvC",
+        "outputId": "98bf93cb-a259-4171-ae5e-619e541d6967"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c191c4310>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 141
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3v6aSgzsPF6y",
+        "outputId": "3fa3aa20-5dda-45be-df5f-fabed3f9b5cf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.90      0.95      0.92        38\n",
+            "           1       0.95      0.91      0.93        44\n",
+            "\n",
+            "    accuracy                           0.93        82\n",
+            "   macro avg       0.93      0.93      0.93        82\n",
+            "weighted avg       0.93      0.93      0.93        82\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "TP = cm[0,0]\n",
+        "TN = cm[1,1]\n",
+        "FP = cm[0,1]\n",
+        "FN = cm[1,0]\n",
+        "\n",
+        "#Classification error\n",
+        "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
+        "print('Classification error : {0:0.4f}'.format(classification_error))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0XwsgAGCPHnD",
+        "outputId": "e45a5b27-a111-43d3-d2d3-4056355f0c5a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification error : 0.0732\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "f1score = f1_score(y_test, y_pred)\n",
+        "accuracy= accuracy_score(y_test, y_pred)\n",
+        "precision= precision_score(y_test, y_pred)\n",
+        "recall= recall_score(y_test, y_pred)\n",
+        "model_comp['Random Forest']=[f1score , accuracy, precision, recall, classification_error]\n",
+        "\n",
+        "print('f1 Score :',f1score)\n",
+        "print('Accuracy:',accuracy)\n",
+        "print('Recall :',recall)\n",
+        "print('Precision :',precision)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_dDIBlwvPJZv",
+        "outputId": "272e0936-d089-49db-aec0-fced838d2ce5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "f1 Score : 0.9302325581395349\n",
+            "Accuracy: 0.926829268292683\n",
+            "Recall : 0.9090909090909091\n",
+            "Precision : 0.9523809523809523\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <h1 align=\"center\">LightGBM </h1>\n"
+      ],
+      "metadata": {
+        "id": "Vf6D9N6GPLhZ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "As we know, LightGBM doesn't provide desired result in smaller dataset. Still, we will apply Random search cv function on our LightGBM model to hypertune our predictions."
+      ],
+      "metadata": {
+        "id": "5yMu3n3uPOlf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import RandomizedSearchCV\n",
+        "import lightgbm as lgb\n",
+        "lgb=lgb.LGBMClassifier()\n",
+        "parameters = {'num_leaves':[20,40,60,80,100], 'min_child_samples':[5,10,15],'max_depth':[-1,5,10,20],\n",
+        "             'learning_rate':[0.05,0.1,0.2],'reg_alpha':[0,0.01,0.03]}\n",
+        "clf=RandomizedSearchCV(lgb,parameters,scoring='accuracy',n_iter=100)\n",
+        "clf.fit(x_train, y_train)\n",
+        "print(clf.best_params_)\n",
+        "predicted=clf.predict(x_test)\n",
+        "print('Classification of the result is:')\n",
+        "print(accuracy_score(y_test, predicted))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dZhoQ6WnPQB_",
+        "outputId": "851416c0-6520-4374-9318-7629f579fe13"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'reg_alpha': 0.03, 'num_leaves': 40, 'min_child_samples': 10, 'max_depth': 10, 'learning_rate': 0.1}\n",
+            "Classification of the result is:\n",
+            "0.926829268292683\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "clf= LGBMClassifier(reg_alpha=0.03, num_leaves=40, min_child_samples=10, max_depth=10, learning_rate=0.1)\n",
+        "clf.fit(x_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oxXaGX98PRv7",
+        "outputId": "2214ed7c-5b21-46ea-f795-6ebd5d54f20b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LGBMClassifier(max_depth=10, min_child_samples=10, num_leaves=40,\n",
+              "               reg_alpha=0.03)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 146
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Prediction\n",
+        "y_pred_train = clf.predict(x_train)\n",
+        "y_pred_train"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6MOvOA80PTOP",
+        "outputId": "49a9b1b5-ba4d-4621-e69a-9974474831e5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
+              "       0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
+              "       0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
+              "       0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1,\n",
+              "       1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1,\n",
+              "       0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
+              "       0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
+              "       0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
+              "       1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0,\n",
+              "       1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,\n",
+              "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1,\n",
+              "       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
+              "       1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
+              "       1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
+              "       1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 147
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Prediction\n",
+        "y_pred_test = clf.predict(x_test)\n",
+        "y_pred_test"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6Vm9XBcPPU2Y",
+        "outputId": "082ce04f-934b-4952-eb26-165dfbf6c15d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
+              "       1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
+              "       1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
+              "       0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 148
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Training accuracy:\")\n",
+        "print( accuracy_score(y_train, y_pred_train)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oF-wssSePWwM",
+        "outputId": "97111452-a09f-47ee-d3f9-40ff6bf68ee4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training accuracy:\n",
+            "100.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Testing accuracy:\")\n",
+        "print( accuracy_score(y_test, y_pred_test)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "adHFfTZXPZX5",
+        "outputId": "dff0f359-1b04-4494-c522-0d55f08a690c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Testing accuracy:\n",
+            "92.6829268292683\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We tried using GridSearchCV with our LightGBM model. However, it led to the falling of the results. The problem arises due to the fact that LightGBM and GridSearchCV start too many threads i.e. more than available on the machine.If there are too many threads they clash and LightGBM stops execution"
+      ],
+      "metadata": {
+        "id": "lBIJvvhnPcMh"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## <h1 align=\"center\"> Confusion Matrix </h1>"
+      ],
+      "metadata": {
+        "id": "vd9-LYVSPkR6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = y_pred_test\n",
+        "cm = confusion_matrix(y_test, y_pred)"
+      ],
+      "metadata": {
+        "id": "D_-yRxdDPmet"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Confusion matrix\\n', cm)\n",
+        "\n",
+        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
+        "\n",
+        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
+        "\n",
+        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
+        "\n",
+        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CKWi4KipPoMZ",
+        "outputId": "8e3ec329-cb35-4ece-af21-ba04c0ee4970"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Confusion matrix\n",
+            " [[36  2]\n",
+            " [ 4 40]]\n",
+            "\n",
+            "True Positives(TP) =  36\n",
+            "\n",
+            "True Negatives(TN) =  40\n",
+            "\n",
+            "False Positives(FP) =  2\n",
+            "\n",
+            "False Negatives(FN) =  4\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
+        "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
+        "\n",
+        "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGn')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 290
+        },
+        "id": "ClX2BeGWPpvN",
+        "outputId": "96c21637-c5a9-4bd9-9250-41b3a938fd7a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c1cbfddd0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 153
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zM7SIM-6PrpJ",
+        "outputId": "83fcfd11-7232-4676-d223-b5ca3001d3df"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.90      0.95      0.92        38\n",
+            "           1       0.95      0.91      0.93        44\n",
+            "\n",
+            "    accuracy                           0.93        82\n",
+            "   macro avg       0.93      0.93      0.93        82\n",
+            "weighted avg       0.93      0.93      0.93        82\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "TP = cm[0,0]\n",
+        "TN = cm[1,1]\n",
+        "FP = cm[0,1]\n",
+        "FN = cm[1,0]\n",
+        "\n",
+        "#Classification error\n",
+        "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
+        "print('Classification error : {0:0.4f}'.format(classification_error))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FjTyYtQ6PuAe",
+        "outputId": "24df3136-54cc-4132-c140-b7e85407b410"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification error : 0.0732\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "f1score = f1_score(y_test, y_pred)\n",
+        "accuracy= accuracy_score(y_test, y_pred)\n",
+        "precision= precision_score(y_test, y_pred)\n",
+        "recall= recall_score(y_test, y_pred)\n",
+        "model_comp['LightGBM']=[f1score , accuracy, precision, recall, classification_error]\n",
+        "\n",
+        "print('f1 Score :',f1score)\n",
+        "print('Accuracy:',accuracy)\n",
+        "print('Recall :',recall)\n",
+        "print('Precision :',precision)\n",
+        "\n",
+        "'''f1 Score : 0.9555555555555557\n",
+        "Accuracy: 0.9512195121951219\n",
+        "Recall : 0.9772727272727273\n",
+        "Precision : 0.9347826086956522'''"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 108
+        },
+        "id": "1qtT0XxNPv9C",
+        "outputId": "73eb5d84-8a18-47cc-e6c2-371e861b098c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "f1 Score : 0.9302325581395349\n",
+            "Accuracy: 0.926829268292683\n",
+            "Recall : 0.9090909090909091\n",
+            "Precision : 0.9523809523809523\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'f1 Score : 0.9555555555555557\\nAccuracy: 0.9512195121951219\\nRecall : 0.9772727272727273\\nPrecision : 0.9347826086956522'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 156
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <h1 align=\"center\"> XGBoost <h1>"
+      ],
+      "metadata": {
+        "id": "N9h4wz_VP0X0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "XGBoost is one of the most widely used gradient boosting algorithms in recent time.  XGBoost yields the best results if right parameters are served. Finding the best parameters is called hyper parameter tuning. We will use GridSearchCV to tune the parameters to get the best result from XGBoost."
+      ],
+      "metadata": {
+        "id": "AWSphel9P4Fz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def algorithm_pipeline(X_train_data, X_test_data, y_train_data, y_test_data, \n",
+        "                       model, param_grid, cv=10, scoring_fit='neg_mean_squared_error',\n",
+        "                       do_probabilities = False):\n",
+        "    gs = GridSearchCV(\n",
+        "        estimator=model,\n",
+        "        param_grid=param_grid, \n",
+        "        cv=cv, \n",
+        "        n_jobs=-1, \n",
+        "        scoring=scoring_fit,\n",
+        "        verbose=2\n",
+        "    )\n",
+        "    fitted_model = gs.fit(X_train_data, y_train_data)\n",
+        "    \n",
+        "    if do_probabilities:\n",
+        "      pred = fitted_model.predict_proba(X_test_data)\n",
+        "    else:\n",
+        "      pred = fitted_model.predict(X_test_data)\n",
+        "    \n",
+        "    return fitted_model, pred"
+      ],
+      "metadata": {
+        "id": "uudPJbpgP5AP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from xgboost.sklearn import XGBClassifier\n",
+        "model = XGBClassifier()\n",
+        "param_grid = {\n",
+        "    'n_estimators': [400, 700, 1000],\n",
+        "    'colsample_bytree': [0.7, 0.8],\n",
+        "    'max_depth': [15,20,25],\n",
+        "    'reg_alpha': [1.1, 1.2, 1.3],\n",
+        "    'reg_lambda': [1.1, 1.2, 1.3],\n",
+        "    'subsample': [0.7, 0.8, 0.9]\n",
+        "}\n",
+        "\n",
+        "model, pred = algorithm_pipeline(x_train, x_test, y_train, y_test, model, \n",
+        "                                 param_grid, cv=5)\n",
+        "\n",
+        "# Root Mean Squared Error\n",
+        "print(np.sqrt(-model.best_score_))\n",
+        "print(model.best_params_)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iq8gOpsBQA0Z",
+        "outputId": "6663579c-bcc5-45f6-e5de-6c74fb96eac0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Fitting 5 folds for each of 486 candidates, totalling 2430 fits\n",
+            "0.3039009200887178\n",
+            "{'colsample_bytree': 0.7, 'max_depth': 15, 'n_estimators': 400, 'reg_alpha': 1.3, 'reg_lambda': 1.2, 'subsample': 0.7}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "xg = XGBClassifier(colsample_bytree=0.7, max_depth=15, n_estimators=400, reg_alpha=1.3, reg_lambda=1.2, subsample=0.7)\n",
+        "xg.fit(x_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GVFsMbl4QCod",
+        "outputId": "12c66a80-c13a-43fc-904c-b484b3f99934"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "XGBClassifier(colsample_bytree=0.7, max_depth=15, n_estimators=400,\n",
+              "              reg_alpha=1.3, reg_lambda=1.2, subsample=0.7)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 159
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_train = xg.predict(x_train)\n",
+        "#convert into binary values\n",
+        "for i in range(0, len(y_pred_train)):\n",
+        "    if y_pred_train[i]>= 0.5:       # setting threshold to .5\n",
+        "       y_pred_train[i]=1\n",
+        "    else:  \n",
+        "       y_pred_train[i]=0"
+      ],
+      "metadata": {
+        "id": "udCGKzygQEJU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_test = xg.predict(x_test)\n",
+        "for i in range(0, len(y_pred_test)):\n",
+        "    if y_pred_test[i]>= 0.5:       # setting threshold to .5\n",
+        "       y_pred_test[i]=1\n",
+        "    else:  \n",
+        "       y_pred_test[i]=0"
+      ],
+      "metadata": {
+        "id": "ZPl4pM7VQF5m"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Training accuracy:\")\n",
+        "print( accuracy_score(y_train, y_pred_train)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YkOUpFt0QH6j",
+        "outputId": "7a54db3c-62f5-42cb-b996-10a42cdd7884"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training accuracy:\n",
+            "99.69135802469135\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Testing accuracy:\")\n",
+        "print( accuracy_score(y_test, y_pred_test)*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aGUpC3ssQJXj",
+        "outputId": "738b97a0-4b19-4252-b039-4dac21b4abff"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Testing accuracy:\n",
+            "95.1219512195122\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## <h1 align=\"center\"> Confusion Matrix </h1>"
+      ],
+      "metadata": {
+        "id": "7-3YhOE7QM4N"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = y_pred_test\n",
+        "cm = confusion_matrix(y_test, y_pred)"
+      ],
+      "metadata": {
+        "id": "4-R3pwXvQds5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Confusion matrix\\n', cm)\n",
+        "\n",
+        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
+        "\n",
+        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
+        "\n",
+        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
+        "\n",
+        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7t3UCY3lQfO7",
+        "outputId": "008e2e3b-ef5f-423c-b55f-bb1121235bf2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Confusion matrix\n",
+            " [[35  3]\n",
+            " [ 1 43]]\n",
+            "\n",
+            "True Positives(TP) =  35\n",
+            "\n",
+            "True Negatives(TN) =  43\n",
+            "\n",
+            "False Positives(FP) =  3\n",
+            "\n",
+            "False Negatives(FN) =  1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
+        "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
+        "\n",
+        "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGn')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 286
+        },
+        "id": "wDwE-fUSQg9D",
+        "outputId": "607f8ed3-cb44-4677-fdc5-4d40a0f3b895"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c1917efd0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 166
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OgpErAJ-QjXi",
+        "outputId": "682f396f-f765-4b38-a437-24c18f5f095b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.97      0.92      0.95        38\n",
+            "           1       0.93      0.98      0.96        44\n",
+            "\n",
+            "    accuracy                           0.95        82\n",
+            "   macro avg       0.95      0.95      0.95        82\n",
+            "weighted avg       0.95      0.95      0.95        82\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "TP = cm[0,0]\n",
+        "TN = cm[1,1]\n",
+        "FP = cm[0,1]\n",
+        "FN = cm[1,0]\n",
+        "\n",
+        "#Classification error\n",
+        "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
+        "print('Classification error : {0:0.4f}'.format(classification_error))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bx6j4a5_QnBF",
+        "outputId": "a20c5a6a-2dce-4965-9461-2699dda9ed0b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification error : 0.0488\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "f1score = f1_score(y_test, y_pred)\n",
+        "accuracy= accuracy_score(y_test, y_pred)\n",
+        "precision= precision_score(y_test, y_pred)\n",
+        "recall= recall_score(y_test, y_pred)\n",
+        "model_comp['XGBoost']=[f1score , accuracy, precision, recall, classification_error]\n",
+        "\n",
+        "print('f1 Score :',f1score)\n",
+        "print('Accuracy:',accuracy)\n",
+        "print('Recall :',recall)\n",
+        "print('Precision :',precision)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mOZTlYGoQpXr",
+        "outputId": "3ee23fe2-f3af-461f-a246-00dca455e65a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "f1 Score : 0.9555555555555557\n",
+            "Accuracy: 0.9512195121951219\n",
+            "Recall : 0.9772727272727273\n",
+            "Precision : 0.9347826086956522\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <b> <h1 align=\"center\"> CONCLUSION </h1> </b>"
+      ],
+      "metadata": {
+        "id": "42LoTHS0QsH1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Plotting the hypertunes prediction values of the top 3 models\n",
+        "model_comp"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "u6bf5b4tQvvx",
+        "outputId": "e7595da7-a01d-4d70-9e81-3c45e54465ea"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'LightGBM': [0.9302325581395349,\n",
+              "  0.926829268292683,\n",
+              "  0.9523809523809523,\n",
+              "  0.9090909090909091,\n",
+              "  0.07317073170731707],\n",
+              " 'Random Forest': [0.9302325581395349,\n",
+              "  0.926829268292683,\n",
+              "  0.9523809523809523,\n",
+              "  0.9090909090909091,\n",
+              "  0.07317073170731707],\n",
+              " 'XGBoost': [0.9555555555555557,\n",
+              "  0.9512195121951219,\n",
+              "  0.9347826086956522,\n",
+              "  0.9772727272727273,\n",
+              "  0.04878048780487805]}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 170
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Mapping the F! score, accuracy, precision, recall and classification error of the top 3 models\n",
+        "\n",
+        "model_comp_df = pd.DataFrame.from_dict(model_comp).T\n",
+        "model_comp_df.columns = ['F1 Score','Accuracy','Precision','Recall','Classification Error']\n",
+        "model_comp_df = model_comp_df.sort_values('F1 Score', ascending=True)\n",
+        "model_comp_df.style.background_gradient(cmap='magma')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 143
+        },
+        "id": "jttFwF1jQzWW",
+        "outputId": "eeb45e45-27c7-460d-b80e-db88c462d34d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<pandas.io.formats.style.Styler at 0x7f0c19f69890>"
+            ],
+            "text/html": [
+              "<style type=\"text/css\">\n",
+              "#T_9a530_row0_col0, #T_9a530_row0_col1, #T_9a530_row0_col3, #T_9a530_row1_col0, #T_9a530_row1_col1, #T_9a530_row1_col3, #T_9a530_row2_col2, #T_9a530_row2_col4 {\n",
+              "  background-color: #000004;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_9a530_row0_col2, #T_9a530_row0_col4, #T_9a530_row1_col2, #T_9a530_row1_col4, #T_9a530_row2_col0, #T_9a530_row2_col1, #T_9a530_row2_col3 {\n",
+              "  background-color: #fcfdbf;\n",
+              "  color: #000000;\n",
+              "}\n",
+              "</style>\n",
+              "<table id=\"T_9a530_\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th class=\"blank level0\" >&nbsp;</th>\n",
+              "      <th class=\"col_heading level0 col0\" >F1 Score</th>\n",
+              "      <th class=\"col_heading level0 col1\" >Accuracy</th>\n",
+              "      <th class=\"col_heading level0 col2\" >Precision</th>\n",
+              "      <th class=\"col_heading level0 col3\" >Recall</th>\n",
+              "      <th class=\"col_heading level0 col4\" >Classification Error</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th id=\"T_9a530_level0_row0\" class=\"row_heading level0 row0\" >Random Forest</th>\n",
+              "      <td id=\"T_9a530_row0_col0\" class=\"data row0 col0\" >0.930233</td>\n",
+              "      <td id=\"T_9a530_row0_col1\" class=\"data row0 col1\" >0.926829</td>\n",
+              "      <td id=\"T_9a530_row0_col2\" class=\"data row0 col2\" >0.952381</td>\n",
+              "      <td id=\"T_9a530_row0_col3\" class=\"data row0 col3\" >0.909091</td>\n",
+              "      <td id=\"T_9a530_row0_col4\" class=\"data row0 col4\" >0.073171</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_9a530_level0_row1\" class=\"row_heading level0 row1\" >LightGBM</th>\n",
+              "      <td id=\"T_9a530_row1_col0\" class=\"data row1 col0\" >0.930233</td>\n",
+              "      <td id=\"T_9a530_row1_col1\" class=\"data row1 col1\" >0.926829</td>\n",
+              "      <td id=\"T_9a530_row1_col2\" class=\"data row1 col2\" >0.952381</td>\n",
+              "      <td id=\"T_9a530_row1_col3\" class=\"data row1 col3\" >0.909091</td>\n",
+              "      <td id=\"T_9a530_row1_col4\" class=\"data row1 col4\" >0.073171</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_9a530_level0_row2\" class=\"row_heading level0 row2\" >XGBoost</th>\n",
+              "      <td id=\"T_9a530_row2_col0\" class=\"data row2 col0\" >0.955556</td>\n",
+              "      <td id=\"T_9a530_row2_col1\" class=\"data row2 col1\" >0.951220</td>\n",
+              "      <td id=\"T_9a530_row2_col2\" class=\"data row2 col2\" >0.934783</td>\n",
+              "      <td id=\"T_9a530_row2_col3\" class=\"data row2 col3\" >0.977273</td>\n",
+              "      <td id=\"T_9a530_row2_col4\" class=\"data row2 col4\" >0.048780</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 171
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Plotting the above map\n",
+        "fig = go.Figure(data=[\n",
+        "    go.Bar(name='F1 Score', y=model_comp_df.index, x=model_comp_df['F1 Score'], orientation='h'),\n",
+        "    go.Bar(name='Accuracy', y=model_comp_df.index, x=model_comp_df['Accuracy'], orientation='h'),\n",
+        "    go.Bar(name='Precision', y=model_comp_df.index, x=model_comp_df['Precision'], orientation='h'),\n",
+        "    go.Bar(name='Recall', y=model_comp_df.index, x=model_comp_df['Recall'], orientation='h'),\n",
+        "    go.Bar(name='Classification Error', y=model_comp_df.index, x=model_comp_df['Classification Error'], orientation='h')\n",
+        "])\n",
+        "fig.update_layout(autosize=False, width=1100,\n",
+        "    height=600,barmode='group')\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 617
+        },
+        "id": "9Hxg-eBxQ1Yp",
+        "outputId": "d13fc9c3-3c73-4922-a6e6-b9e90499611b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
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+              "</body>\n",
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+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<b> Explanation: </b>\n",
+        "<ul>\n",
+        "<li> XGBoost:\n",
+        "<br>\n",
+        "<table align=\"left\">\n",
+        "<tr>\n",
+        "<th> Parameters </th>\n",
+        "<th> Original </th>\n",
+        "<th> Hypertuned </th>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> F1 Score </td>\n",
+        "<td>91.95%\n",
+        "<td> 95.55%\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> Accuracy </td>\n",
+        "<td>91.46%\n",
+        "<td> 95.12%\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> Precision </td>\n",
+        "<td>93.02%\n",
+        "<td> 93.47%\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> Recall </td>\n",
+        "<td>90.90%\n",
+        "<td> 97.72%\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> Classification error</td>\n",
+        "<td>8.535\n",
+        "<td> 4.87%\n",
+        "</tr>\n",
+        "</table>\n",
+        "</li>\n",
+        "<br>\n",
+        "<br>"
+      ],
+      "metadata": {
+        "id": "S-EqVvKWQ6FS"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<ul>\n",
+        "<li>LightGBM:\n",
+        "<br>\n",
+        "<table align=\"left\">\n",
+        "<tr>\n",
+        "<th> Parameters </th>\n",
+        "<th> Original </th>\n",
+        "<th> Hypertuned </th>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> F1 Score </td>\n",
+        "<td>95.55%\n",
+        "<td> 94.11%\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> Accuracy </td>\n",
+        "<td>95.12%\n",
+        "<td> 93.90%\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> Precision </td>\n",
+        "<td>93.47%\n",
+        "<td> 97.56%\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> Recall </td>\n",
+        "<td>97.72%\n",
+        "<td> 90.90%\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> Classification error</td>\n",
+        "<td>4.87%\n",
+        "<td> 6.09%\n",
+        "</tr>\n",
+        "</table>\n"
+      ],
+      "metadata": {
+        "id": "ebal85SsQ7A0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<ul>\n",
+        "<li>Random Forest:\n",
+        "<br>\n",
+        "<table align=\"left\">\n",
+        "<tr>\n",
+        "<th> Parameters </th>\n",
+        "<th> Original </th>\n",
+        "<th> Hypertuned </th>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> F1 Score </td>\n",
+        "<td>91.76%\n",
+        "<td> 93.02%\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> Accuracy </td>\n",
+        "<td>91.46%\n",
+        "<td> 92.68%\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> Precision </td>\n",
+        "<td>95.12%\n",
+        "<td> 95.23%\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> Recall </td>\n",
+        "<td>88.63%\n",
+        "<td> 90.90%\n",
+        "</tr>\n",
+        "<tr>\n",
+        "<td> Classification error</td>\n",
+        "<td>8.53%\n",
+        "<td> 7.31%\n",
+        "</tr>\n",
+        "</table>"
+      ],
+      "metadata": {
+        "id": "ykdPrxkmRDNo"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<b>Now we have predictions of both models. We can clearly realize the difference that GridSearchCV predictions are better. Also, we used very less parameters in tuning process."
+      ],
+      "metadata": {
+        "id": "vokrOSg9RHPw"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<h1 aign=\"center\" > <b> BEST MODEL: </b> </h1>\n",
+        "<ul>\n",
+        "<li> <b>XGBoost is the best model algorithm for our problem: Heart Failure Prediction. </b>\n",
+        "<li> As it is clearly visible from our output, XGBoost has the maximum F1 score, maximum accuracy and maximum recall value.\n",
+        "<li> XGBoost shows the lowest classification error as well."
+      ],
+      "metadata": {
+        "id": "A0DEDgVARJdw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.tail(20)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 720
+        },
+        "id": "Q1riJmM-RLVd",
+        "outputId": "de3de67f-687d-4409-ec1d-592abb48145c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
+              "279  55.0        0                        84         1                 38   \n",
+              "280  70.0        0                      2695         1                 40   \n",
+              "281  70.0        0                       582         0                 40   \n",
+              "282  42.0        0                        64         0                 30   \n",
+              "283  65.0        0                      1688         0                 38   \n",
+              "284  50.0        1                        54         0                 40   \n",
+              "285  55.0        1                       170         1                 40   \n",
+              "286  60.0        0                       253         0                 35   \n",
+              "287  45.0        0                       582         1                 55   \n",
+              "288  65.0        0                       892         1                 35   \n",
+              "289  90.0        1                       337         0                 38   \n",
+              "290  45.0        0                       615         1                 55   \n",
+              "291  60.0        0                       320         0                 35   \n",
+              "292  52.0        0                       190         1                 38   \n",
+              "293  63.0        1                       103         1                 35   \n",
+              "294  62.0        0                        61         1                 38   \n",
+              "295  55.0        0                      1820         0                 38   \n",
+              "296  45.0        0                      2060         1                 60   \n",
+              "297  45.0        0                      2413         0                 38   \n",
+              "298  50.0        0                       196         0                 45   \n",
+              "\n",
+              "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
+              "279                    0  451000.00               1.3           136    0   \n",
+              "280                    0  241000.00               1.0           137    1   \n",
+              "281                    0   51000.00               2.7           136    1   \n",
+              "282                    0  215000.00               3.8           128    1   \n",
+              "283                    0  263358.03               1.1           138    1   \n",
+              "284                    0  279000.00               0.8           141    1   \n",
+              "285                    0  336000.00               1.2           135    1   \n",
+              "286                    0  279000.00               1.7           140    1   \n",
+              "287                    0  543000.00               1.0           132    0   \n",
+              "288                    0  263358.03               1.1           142    0   \n",
+              "289                    0  390000.00               0.9           144    0   \n",
+              "290                    0  222000.00               0.8           141    0   \n",
+              "291                    0  133000.00               1.4           139    1   \n",
+              "292                    0  382000.00               1.0           140    1   \n",
+              "293                    0  179000.00               0.9           136    1   \n",
+              "294                    1  155000.00               1.1           143    1   \n",
+              "295                    0  270000.00               1.2           139    0   \n",
+              "296                    0  742000.00               0.8           138    0   \n",
+              "297                    0  140000.00               1.4           140    1   \n",
+              "298                    0  395000.00               1.6           136    1   \n",
+              "\n",
+              "     smoking  time  DEATH_EVENT  \n",
+              "279        0   246            0  \n",
+              "280        0   247            0  \n",
+              "281        1   250            0  \n",
+              "282        1   250            0  \n",
+              "283        1   250            0  \n",
+              "284        0   250            0  \n",
+              "285        0   250            0  \n",
+              "286        0   250            0  \n",
+              "287        0   250            0  \n",
+              "288        0   256            0  \n",
+              "289        0   256            0  \n",
+              "290        0   257            0  \n",
+              "291        0   258            0  \n",
+              "292        1   258            0  \n",
+              "293        1   270            0  \n",
+              "294        1   270            0  \n",
+              "295        0   271            0  \n",
+              "296        0   278            0  \n",
+              "297        1   280            0  \n",
+              "298        1   285            0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-96654d64-f287-4837-95f0-4450ec3641ee\">\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>anaemia</th>\n",
+              "      <th>creatinine_phosphokinase</th>\n",
+              "      <th>diabetes</th>\n",
+              "      <th>ejection_fraction</th>\n",
+              "      <th>high_blood_pressure</th>\n",
+              "      <th>platelets</th>\n",
+              "      <th>serum_creatinine</th>\n",
+              "      <th>serum_sodium</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>smoking</th>\n",
+              "      <th>time</th>\n",
+              "      <th>DEATH_EVENT</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>279</th>\n",
+              "      <td>55.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>84</td>\n",
+              "      <td>1</td>\n",
+              "      <td>38</td>\n",
+              "      <td>0</td>\n",
+              "      <td>451000.00</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>136</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>246</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>280</th>\n",
+              "      <td>70.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2695</td>\n",
+              "      <td>1</td>\n",
+              "      <td>40</td>\n",
+              "      <td>0</td>\n",
+              "      <td>241000.00</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>137</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>247</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>281</th>\n",
+              "      <td>70.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>582</td>\n",
+              "      <td>0</td>\n",
+              "      <td>40</td>\n",
+              "      <td>0</td>\n",
+              "      <td>51000.00</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>136</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>250</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>282</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
+              "      <td>30</td>\n",
+              "      <td>0</td>\n",
+              "      <td>215000.00</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>128</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>250</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>283</th>\n",
+              "      <td>65.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1688</td>\n",
+              "      <td>0</td>\n",
+              "      <td>38</td>\n",
+              "      <td>0</td>\n",
+              "      <td>263358.03</td>\n",
+              "      <td>1.1</td>\n",
+              "      <td>138</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>250</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>284</th>\n",
+              "      <td>50.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>54</td>\n",
+              "      <td>0</td>\n",
+              "      <td>40</td>\n",
+              "      <td>0</td>\n",
+              "      <td>279000.00</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>141</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>250</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>285</th>\n",
+              "      <td>55.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>170</td>\n",
+              "      <td>1</td>\n",
+              "      <td>40</td>\n",
+              "      <td>0</td>\n",
+              "      <td>336000.00</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>135</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>250</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>286</th>\n",
+              "      <td>60.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>253</td>\n",
+              "      <td>0</td>\n",
+              "      <td>35</td>\n",
+              "      <td>0</td>\n",
+              "      <td>279000.00</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>140</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>250</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>287</th>\n",
+              "      <td>45.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>582</td>\n",
+              "      <td>1</td>\n",
+              "      <td>55</td>\n",
+              "      <td>0</td>\n",
+              "      <td>543000.00</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>132</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>250</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>288</th>\n",
+              "      <td>65.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>892</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35</td>\n",
+              "      <td>0</td>\n",
+              "      <td>263358.03</td>\n",
+              "      <td>1.1</td>\n",
+              "      <td>142</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>256</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>289</th>\n",
+              "      <td>90.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>337</td>\n",
+              "      <td>0</td>\n",
+              "      <td>38</td>\n",
+              "      <td>0</td>\n",
+              "      <td>390000.00</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>144</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>256</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>290</th>\n",
+              "      <td>45.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>615</td>\n",
+              "      <td>1</td>\n",
+              "      <td>55</td>\n",
+              "      <td>0</td>\n",
+              "      <td>222000.00</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>141</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>257</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>291</th>\n",
+              "      <td>60.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>320</td>\n",
+              "      <td>0</td>\n",
+              "      <td>35</td>\n",
+              "      <td>0</td>\n",
+              "      <td>133000.00</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>139</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>258</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>292</th>\n",
+              "      <td>52.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>190</td>\n",
+              "      <td>1</td>\n",
+              "      <td>38</td>\n",
+              "      <td>0</td>\n",
+              "      <td>382000.00</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>140</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>258</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>293</th>\n",
+              "      <td>63.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35</td>\n",
+              "      <td>0</td>\n",
+              "      <td>179000.00</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>136</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>270</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>294</th>\n",
+              "      <td>62.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>61</td>\n",
+              "      <td>1</td>\n",
+              "      <td>38</td>\n",
+              "      <td>1</td>\n",
+              "      <td>155000.00</td>\n",
+              "      <td>1.1</td>\n",
+              "      <td>143</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>270</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>295</th>\n",
+              "      <td>55.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1820</td>\n",
+              "      <td>0</td>\n",
+              "      <td>38</td>\n",
+              "      <td>0</td>\n",
+              "      <td>270000.00</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>139</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>271</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>296</th>\n",
+              "      <td>45.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2060</td>\n",
+              "      <td>1</td>\n",
+              "      <td>60</td>\n",
+              "      <td>0</td>\n",
+              "      <td>742000.00</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>138</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>278</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>297</th>\n",
+              "      <td>45.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2413</td>\n",
+              "      <td>0</td>\n",
+              "      <td>38</td>\n",
+              "      <td>0</td>\n",
+              "      <td>140000.00</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>140</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>280</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>298</th>\n",
+              "      <td>50.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>196</td>\n",
+              "      <td>0</td>\n",
+              "      <td>45</td>\n",
+              "      <td>0</td>\n",
+              "      <td>395000.00</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>136</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>285</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96654d64-f287-4837-95f0-4450ec3641ee')\"\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-96654d64-f287-4837-95f0-4450ec3641ee 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-96654d64-f287-4837-95f0-4450ec3641ee');\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": 173
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "xg = XGBClassifier(colsample_bytree=0.7, max_depth=15, n_estimators=400, reg_alpha=1.3, reg_lambda=1.2, subsample=0.7)\n",
+        "xg.fit(x_train, y_train)\n",
+        "def XGmodel(x):\n",
+        "    y = xg.predict(x)\n",
+        "    return y\n",
+        "\n",
+        "print(\"Death Event Original for [62.0,0,61,1,38,1,155000.0,1.1,143,1,1,270] :\", 0)\n",
+        "print(\"Death Event Predicted for [62.0,0,61,1,38,1,155000.0,1.1,143,1,1,270] :\", XGmodel([62.0,0,61,1,38,1,155000.0,1.1,143,1,1,270]))\n",
+        "print()\n",
+        "print(\"Death Event Original for [45.0,0,2413,0,38,0,140000.0,1.4,140,1,1,280] :\", 0)\n",
+        "print(\"Death Event Predicted for [45.0,0,2413,0,38,0,140000.0,1.4,140,1,1,280] :\", XGmodel([45.0,0,2413,0,38,0,140000.0,1.4,140,1,1,280]))\n",
+        "print()\n",
+        "print(\"Death Event Original for [70.0,0,2695,1,40,0,241000.0,1.0,137,1,0,247]:\", 0)\n",
+        "print(\"Death Event Predicted for [70.0,0,2695,1,40,0,241000.0,1.0,137,1,0,247] :\", XGmodel([70.0,0,2695,1,40,0,241000.0,1.0,137,1,0,247]))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bgJy1p8DRNYQ",
+        "outputId": "8c7d4001-a4a8-4043-fd84-ea707302fe77"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Death Event Original for [62.0,0,61,1,38,1,155000.0,1.1,143,1,1,270] : 0\n",
+            "Death Event Predicted for [62.0,0,61,1,38,1,155000.0,1.1,143,1,1,270] : [0]\n",
+            "\n",
+            "Death Event Original for [45.0,0,2413,0,38,0,140000.0,1.4,140,1,1,280] : 0\n",
+            "Death Event Predicted for [45.0,0,2413,0,38,0,140000.0,1.4,140,1,1,280] : [0]\n",
+            "\n",
+            "Death Event Original for [70.0,0,2695,1,40,0,241000.0,1.0,137,1,0,247]: 0\n",
+            "Death Event Predicted for [70.0,0,2695,1,40,0,241000.0,1.0,137,1,0,247] : [0]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<h3> References: </h3>\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "1.   https://www.cdc.gov/heartdisease/heart_failure.htm\n",
+        "2.   Epidemiology of heart failure- https://onlinelibrary.wiley.com/doi/full/10.1002/ejhf.1858#.YsYuepL1Q38.twitter \n",
+        "3. https://towardsdatascience.com/heart-disease-prediction-73468d630cfc\n",
+        "4. Citation-\n",
+        "Davide Chicco, Giuseppe Jurman: Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making 20, 16 (2020). (link)\n",
+        "5. License-\n",
+        "CC BY 4.0\n",
+        "6. Splash icon-\n",
+        "Icon by Freepik, available on Flaticon.\n",
+        "7. Splash banner-\n",
+        "Wallpaper by jcomp, available on Freepik.\n"
+      ],
+      "metadata": {
+        "id": "rF3IWjfXRVVo"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Jyotsnakoul_loandefaultprediction b/Jyotsnakoul_loandefaultprediction
new file mode 100644
index 0000000..ee5fbe0
--- /dev/null
+++ b/Jyotsnakoul_loandefaultprediction
@@ -0,0 +1 @@
+collab file- https://colab.research.google.com/drive/11VZPmMY0UwLtoOh64NGm5_IWcYXErYgq#scrollTo=16dnlHOi8YfL