Delete Jupyter-notebook/Machine learning/Health prediction model directory

author: Pratik-Nemane 2024-08-13 17:31:47 +0530
committer: GitHub 2024-08-13 17:31:47 +0530
commit: 33beb5610cf9c63ee5a9016376735baf480a45a5 (patch)
tree: 11847fd87615e8fe227e63046f40e43f24d15c51
parent: 56255cbc2ed57416e273596c32922aa35a3bc2ee (diff)
download: FLOSS-Arduino-Book-33beb5610cf9c63ee5a9016376735baf480a45a5.tar.gz
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12 files changed, 0 insertions, 6245 deletions
diff --git a/Jupyter-notebook/Machine learning/Health prediction model/Arduino/deployment code/DecisionTree.h b/Jupyter-notebook/Machine learning/Health prediction model/Arduino/deployment code/DecisionTree.h
deleted file mode 100644
index 5fc3b53..0000000
--- a/Jupyter-notebook/Machine learning/Health prediction model/Arduino/deployment code/DecisionTree.h
+++ /dev/null
@@ -1,43 +0,0 @@
-#pragma once
-// #include <cstdarg>
-namespace Eloquent {
-    namespace ML {
-        namespace Port {
-            class DecisionTree {
-                public:
-                    /**
-                    * Predict class for features vector
-                    */
-                    int predict(float *x) {
-                        if (x[1] <= 37.25) {
-                            if (x[2] <= 94.0) {
-                                return 1;
-                            }
-
-                            else {
-                                if (x[0] <= 100.0) {
-                                    if (x[1] <= 36.25) {
-                                        return 1;
-                                    }
-
-                                    else {
-                                        return 0;
-                                    }
-                                }
-
-                                else {
-                                    return 1;
-                                }
-                            }
-                        }
-
-                        else {
-                            return 1;
-                        }
-                    }
-
-                protected:
-                };
-            }
-        }
-    }
-\ No newline at end of file
diff --git a/Jupyter-notebook/Machine learning/Health prediction model/Arduino/deployment code/code.ino b/Jupyter-notebook/Machine learning/Health prediction model/Arduino/deployment code/code.ino
deleted file mode 100644
index 6b15678..0000000
--- a/Jupyter-notebook/Machine learning/Health prediction model/Arduino/deployment code/code.ino
+++ /dev/null
@@ -1,99 +0,0 @@
-#include <Wire.h>
-#include "MAX30100_PulseOximeter.h"
-#include "DecisionTree.h"
-#include <OneWire.h>
-#include <DallasTemperature.h>
-
-Eloquent::ML::Port::DecisionTree health_Classifier;
-
-#define DS18B20 4
-
-// Initialize the PulseOximeter library
-PulseOximeter pox;
-#define REPORTING_PERIOD_MS 1000
-uint32_t tsLastReport = 0;
-
-// Variable Declaration
-float bodytemperature;
-int BPM;
-float SpO2;
-int prediction;
-
-// Callback (registered below) fired when a pulse is detected
-void onBeatDetected() {
-  Serial.println("Beat!");
-}
-
-// Define the struct for holding heart rate and SpO2 values
-struct HeartRateData {
-    float BPM;
-    float SpO2;
-};
-
-// Declare the heart_rate function
-HeartRateData heart_rate();
-
-HeartRateData heart_rate() {
-    pox.update();
-    
-    HeartRateData data;
-    data.BPM = pox.getHeartRate();
-    data.SpO2 = pox.getSpO2();
-
-    return data;
-}
-
-OneWire oneWire(DS18B20);
-DallasTemperature sensors(&oneWire);
-
-void setup() {
-  Serial.begin(115200);
-  Serial.print("Initializing pulse oximeter..");
-  if (!pox.begin()) {
-    Serial.println("FAILED");
-    for (;;);
-  } 
-  else {
-    Serial.println("SUCCESS");
-  }
-  pox.setOnBeatDetectedCallback(onBeatDetected);
-}
-
-void loop() {
-  delay(500);
-  pox.update();
-  sensors.requestTemperatures();
-  HeartRateData data = heart_rate();
-  if (millis() - tsLastReport > REPORTING_PERIOD_MS) {
-    Serial.print("Heart rate: ");
-    Serial.print(data.BPM);
-    Serial.print(" bpm / SpO2: ");
-    Serial.print(data.SpO2);
-    Serial.println(" %");
-  }
-  bodytemperature = sensors.getTempCByIndex(0);
-  Serial.print(bodytemperature);
-  // Predict the health condition
-  float input[3] = { data.BPM, (float)bodytemperature, data.SpO2 };
-  prediction = health_Classifier.predict(input);
-
-  // Checking Condition
-  Serial.print("Prediction: ");
-  switch (prediction) {
-    case 0:
-      Serial.println("Normal");
-      break;
-    case 1:
-      Serial.println("Serious");
-      break;
-    case 2:
-      Serial.println("Dischargeable");
-      break;
-    default:
-      Serial.println("Unknown");
-      break;
-  }
-  tsLastReport = millis();
-  Serial.println();
-  delay(1000);
-}
diff --git a/Jupyter-notebook/Machine learning/Health prediction model/Arduino/heart_function/heart_function.ino b/Jupyter-notebook/Machine learning/Health prediction model/Arduino/heart_function/heart_function.ino
deleted file mode 100644
index 4948f40..0000000
--- a/Jupyter-notebook/Machine learning/Health prediction model/Arduino/heart_function/heart_function.ino
+++ /dev/null
@@ -1,57 +0,0 @@
-#include <Wire.h>
-#include "MAX30100_PulseOximeter.h"
-
-#define REPORTING_PERIOD_MS 1000
-
-PulseOximeter pox;
-uint32_t tsLastReport = 0;
-
-void onBeatDetected() {
-    Serial.println("Beat!");
-}
-
-// Define the struct for holding heart rate and SpO2 values
-struct HeartRateData {
-    float BPM;
-    float SpO2;
-};
-
-// Declare the heart_rate function
-HeartRateData heart_rate();
-
-void setup() {
-    Serial.begin(115200);
-    Serial.print("Initializing pulse oximeter..");
-
-    if (!pox.begin()) {
-        Serial.println("FAILED");
-        for (;;);
-    } else {
-        Serial.println("SUCCESS");
-    }
-    
-    pox.setOnBeatDetectedCallback(onBeatDetected);
-}
-
-void loop() {
-    HeartRateData data = heart_rate();
-
-    if (millis() - tsLastReport > REPORTING_PERIOD_MS) {
-        Serial.print("Heart rate: ");
-        Serial.print(data.BPM);
-        Serial.print(" bpm / SpO2: ");
-        Serial.print(data.SpO2);
-        Serial.println(" %");
-        tsLastReport = millis();
-    }
-}
-
-HeartRateData heart_rate() {
-    pox.update();
-    
-    HeartRateData data;
-    data.BPM = pox.getHeartRate();
-    data.SpO2 = pox.getSpO2();
-
-    return data;
-}
diff --git a/Jupyter-notebook/Machine learning/Health prediction model/Health prediction model.ipynb b/Jupyter-notebook/Machine learning/Health prediction model/Health prediction model.ipynb
deleted file mode 100644
index edb3d33..0000000
--- a/Jupyter-notebook/Machine learning/Health prediction model/Health prediction model.ipynb
+++ /dev/null
@@ -1,3543 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "8acafd5a-7574-4f05-aab7-e9dae67deac9",
-   "metadata": {},
-   "source": [
-    "\n",
-    "<center>\n",
-    "\n",
-    "# <span style=\"font-size: 1.5em; color: #ff6347;\">Health prediction model</span>\n",
-    "\n",
-    "</center>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hrPMSSu2taoh",
-   "metadata": {
-    "id": "hrPMSSu2taoh"
-   },
-   "source": [
-    "# **Introduction to Machine Learning with Jupyter Notebooks**\n",
-    "Welcome to the world of Machine Learning. This instructional materials will guide you to learn machine learning using Jupyter Notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hmsg3MWmvNjq",
-   "metadata": {
-    "id": "hmsg3MWmvNjq",
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "## **Getting Started**\n",
-    "### **1. Setting Up Your Environment**\n",
-    "Before we go deep into machine learning, you'll need to set up your environment.\n",
-    "follow these steps to set up your environment.\n",
-    "\n",
-    "**Install Jupyter Notebook**:\n",
-    "\n",
-    "Install the classic Jupyter Notebook by writting the below command in your terminal:\n",
-    "\n",
-    "`pip install notebook`\n",
-    "\n",
-    "### **2. Launching Jupyter Notebook**\n",
-    "\n",
-    "To run the notebook:\n",
-    "\n",
-    "`jupyter notebook`\n",
-    "\n",
-    "or if this doesnt work, write this insteade.\n",
-    "\n",
-    "`python -m notebook`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1z9COW_-v4_2",
-   "metadata": {
-    "id": "1z9COW_-v4_2",
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "## **Basics of Machain Learning (ML)**\n",
-    "### **What is Machine Learning?**\n",
-    "It is a subset of Artificial Inteligence (AI). It focuses on making a model capable of learning from data to make predictions or decisions.\n",
-    "\n",
-    "### **Types of Machaine Learning:**\n",
-    "1. *Supervised Learning*: Learning from labeled data (e.g., classification, regression).\n",
-    "2. *Unsupervised Learning*: Finding patterns in unlabeled data (e.g., clustering, dimensionality reduction).\n",
-    "3. *Reinforcement Learning*: Learning through rewards and punishments."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07hppMV2tpwd",
-   "metadata": {
-    "id": "07hppMV2tpwd"
-   },
-   "source": [
-    "## **Building your first ML Model**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "IBzg9QTEDEeI",
-   "metadata": {
-    "id": "IBzg9QTEDEeI"
-   },
-   "source": [
-    "**Tools and Technologies**:\n",
-    "\n",
-    "**1**.\tJupyter Notebook: For data preprocessing, model development, and training.\n",
-    "\n",
-    "**2**.\tProgramming Language: Python - Widely used for its simplicity, extensive libraries, and ecosystem support.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "lVtdjpVNDQdj",
-   "metadata": {
-    "id": "lVtdjpVNDQdj"
-   },
-   "source": [
-    "## **Task Steps:**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "GmqCkoG9Juf5",
-   "metadata": {
-    "id": "GmqCkoG9Juf5"
-   },
-   "source": [
-    "### **1. Import libraries:**\n",
-    "We will import the necessary libraries first"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cxf2RSmXJhoG",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 8851,
-     "status": "ok",
-     "timestamp": 1719500901538,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "cxf2RSmXJhoG"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-07-15 12:33:13.494270: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
-      "2024-07-15 12:33:21.179884: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
-      "2024-07-15 12:34:25.060866: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd     # for data acquisition\n",
-    "import numpy as np      # for data processing\n",
-    "import tensorflow as tf # for making the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "H-aQmCZePpJ2",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 10986,
-     "status": "ok",
-     "timestamp": 1719500989962,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "H-aQmCZePpJ2",
-    "outputId": "b7d9c455-c8ef-4531-a2dc-90398110f253"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Defaulting to user installation because normal site-packages is not writeable\n",
-      "Requirement already satisfied: micromlgen in /home/user/.local/lib/python3.10/site-packages (1.1.28)\n",
-      "Requirement already satisfied: jinja2 in /home/user/.local/lib/python3.10/site-packages (from micromlgen) (3.0.3)\n",
-      "Requirement already satisfied: MarkupSafe>=2.0 in /home/user/.local/lib/python3.10/site-packages (from jinja2->micromlgen) (2.1.5)\n",
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install micromlgen"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "966398fd-64a6-4e87-a59f-7f7efe754613",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 1598,
-     "status": "ok",
-     "timestamp": 1719507588725,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "966398fd-64a6-4e87-a59f-7f7efe754613"
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from micromlgen import port\n",
-    "from sklearn import metrics\n",
-    "import seaborn as sns\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da6EwHUXBLr2",
-   "metadata": {
-    "id": "da6EwHUXBLr2"
-   },
-   "source": [
-    "### **2. Dataset Acquisition:**\n",
-    "We will use the provided medical dataset for this project.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "26760d37-0b57-41dc-888b-4f96562a4296",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 625,
-     "status": "ok",
-     "timestamp": 1719507600895,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "26760d37-0b57-41dc-888b-4f96562a4296"
-   },
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv('dataset.csv')  # Load the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "b2mNI5xxLvSF",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 206
-    },
-    "executionInfo": {
-     "elapsed": 6,
-     "status": "ok",
-     "timestamp": 1719507603351,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "b2mNI5xxLvSF",
-    "outputId": "68b4ab82-e2e3-47ed-dc96-e1cf4c12ce6b"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>gender</th>\n",
-       "      <th>age</th>\n",
-       "      <th>heart_rate</th>\n",
-       "      <th>temperature</th>\n",
-       "      <th>SpO2_saturation</th>\n",
-       "      <th>bpm</th>\n",
-       "      <th>Health_status</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>male</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>38.6</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>75</td>\n",
-       "      <td>Infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>female</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>74</td>\n",
-       "      <td>39.6</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>Infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>male</td>\n",
-       "      <td>46.0</td>\n",
-       "      <td>82</td>\n",
-       "      <td>37.2</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>83</td>\n",
-       "      <td>Non Infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>female</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>90</td>\n",
-       "      <td>38.6</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>75</td>\n",
-       "      <td>Non Infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>male</td>\n",
-       "      <td>58.0</td>\n",
-       "      <td>72</td>\n",
-       "      <td>39.6</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>78</td>\n",
-       "      <td>Infected</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   id  gender   age  heart_rate  temperature  SpO2_saturation  bpm  \\\n",
-       "0   1    male  66.0          70         38.6             88.0   75   \n",
-       "1   2  female  56.0          74         39.6             88.0   70   \n",
-       "2   3    male  46.0          82         37.2             98.0   83   \n",
-       "3   4  female  60.0          90         38.6             98.0   75   \n",
-       "4   5    male  58.0          72         39.6             93.0   78   \n",
-       "\n",
-       "  Health_status  \n",
-       "0      Infected  \n",
-       "1      Infected  \n",
-       "2  Non Infected  \n",
-       "3  Non Infected  \n",
-       "4      Infected  "
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head(5)  # Display the first few rows of the dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3d9a693-bdcd-42f0-8b99-3691ce96af7f",
-   "metadata": {
-    "id": "e3d9a693-bdcd-42f0-8b99-3691ce96af7f"
-   },
-   "source": [
-    "## Determine Medical Condition"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "ff7a52c2-7d93-4a21-bab4-bb3c230f67ab",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 780,
-     "status": "ok",
-     "timestamp": 1719507606744,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "ff7a52c2-7d93-4a21-bab4-bb3c230f67ab"
-   },
-   "outputs": [],
-   "source": [
-    "def determine_medical_condition(row):\n",
-    "    heart_rate = row['heart_rate']\n",
-    "    temperature = row['temperature']\n",
-    "    spo2 = row['SpO2_saturation']\n",
-    "\n",
-    "    if 60 <= heart_rate <= 100 and 36.1 <= temperature <= 37.2 and 95 <= spo2 <= 100:\n",
-    "        return 0 #\"Normal\"\n",
-    "    elif heart_rate > 100 or heart_rate < 60 or temperature > 37.2 or temperature < 36.1 or spo2 < 95:\n",
-    "        return 1 #\"Serious\"\n",
-    "    else:\n",
-    "        return 2 #\"Dischargeable\"\n",
-    "# refer data website"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "7acdb25d-d6fa-43f5-9293-aba2a66d3fde",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 533,
-     "status": "ok",
-     "timestamp": 1719507609351,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "7acdb25d-d6fa-43f5-9293-aba2a66d3fde"
-   },
-   "outputs": [],
-   "source": [
-    "# Apply the function to the DataFrame to create the 'health_condition' column\n",
-    "data['health_condition'] = data.apply(determine_medical_condition, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "bfe276d7-0ea8-41e8-962d-94075819d91a",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719507610495,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "bfe276d7-0ea8-41e8-962d-94075819d91a",
-    "outputId": "72cf99b9-4723-4ccd-d44a-0eb0f2ba97db"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    heart_rate  temperature  SpO2_saturation Health_status  health_condition\n",
-      "0           70         38.6             88.0      Infected                 1\n",
-      "1           74         39.6             88.0      Infected                 1\n",
-      "2           82         37.2             98.0  Non Infected                 0\n",
-      "3           90         38.6             98.0  Non Infected                 1\n",
-      "4           72         39.6             93.0      Infected                 1\n",
-      "5           72         37.2             93.0      Infected                 1\n",
-      "6           67         38.6             98.0  Non Infected                 1\n",
-      "7           72         39.6             97.0  Non Infected                 1\n",
-      "8           65         37.2             96.0  Non Infected                 0\n",
-      "9           76         38.0             92.0      Infected                 1\n",
-      "10          73         38.6             50.0      Infected                 1\n",
-      "11          64         39.6             97.0  Non Infected                 1\n",
-      "12          63         37.2             70.0      Infected                 1\n",
-      "13          94         38.6             70.0      Infected                 1\n",
-      "14          73         39.6             70.0      Infected                 1\n",
-      "15          75         37.2             70.0      Infected                 1\n",
-      "16          78         38.6             70.0      Infected                 1\n",
-      "17          68         39.6             95.0  Non Infected                 1\n",
-      "18          71         37.2             95.0  Non Infected                 0\n",
-      "19          70         38.6             95.0  Non Infected                 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Display the updated DataFrame\n",
-    "print(data.head(20))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4jcYMC2SERMm",
-   "metadata": {
-    "id": "4jcYMC2SERMm"
-   },
-   "source": [
-    "### **Data Preprocessing & Visualization**:\n",
-    "\n",
-    "Handle missing values and encode categorical variables."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "wbtz2oV9KY5_",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 3,
-     "status": "ok",
-     "timestamp": 1719507611265,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "wbtz2oV9KY5_"
-   },
-   "outputs": [],
-   "source": [
-    "# Dataset Preprocessing\n",
-    "X = data[['heart_rate', 'temperature', 'SpO2_saturation']] #features\n",
-    "y = data['health_condition'] #target\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "64b978b6-dd9b-46db-a3dd-d8ed0cef6727",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Load the dataset\n",
-    "file_path = 'dataset.csv'\n",
-    "df = pd.read_csv(file_path)\n",
-    "\n",
-    "# Define the binning and labeling for each parameter\n",
-    "parameters = {\n",
-    "    'heart_rate': {\n",
-    "        'bins': [60, 70, 80, 90, 100, 110],\n",
-    "        'labels': ['60-70', '70-80', '80-90', '90-100', '100-110']\n",
-    "    },\n",
-    "    'SpO2_saturation': {\n",
-    "        'bins': [80, 85, 90, 95, 100],\n",
-    "        'labels': ['80-85', '85-90', '90-95', '95-100']\n",
-    "    },\n",
-    "    'temperature': {\n",
-    "        'bins': [35, 36, 37, 38, 39, 40],\n",
-    "        'labels': ['35-36', '36-37', '37-38', '38-39', '39-40']\n",
-    "    }\n",
-    "}\n",
-    "\n",
-    "# Function to create a pie chart\n",
-    "def plot_pie_chart(df, column, bins, labels, title):\n",
-    "    df[f'{column}_category'] = pd.cut(df[column], bins=bins, labels=labels, include_lowest=True)\n",
-    "    category_counts = df[f'{column}_category'].value_counts().sort_index()\n",
-    "    category_counts.plot(kind='pie', autopct='%1.1f%%')\n",
-    "    plt.title(title)\n",
-    "    plt.ylabel('')  # Hide the y-label\n",
-    "    plt.show()\n",
-    "\n",
-    "# Plot pie charts for each parameter\n",
-    "for param, config in parameters.items():\n",
-    "    plot_pie_chart(df, param, config['bins'], config['labels'], f'{param.replace(\"_\", \" \").title()} Distribution')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "e2bddac5-4d2c-4199-b63c-011ae9bce559",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 448
-    },
-    "executionInfo": {
-     "elapsed": 1571,
-     "status": "ok",
-     "timestamp": 1719507615505,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "e2bddac5-4d2c-4199-b63c-011ae9bce559",
-    "outputId": "a0f5d229-e4d6-4fda-f339-213958f5cd25"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X[['heart_rate']].plot()  # plot of heart_rate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "a2b2272a-b25c-4954-8d34-5797f053a0fa",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 448
-    },
-    "executionInfo": {
-     "elapsed": 862,
-     "status": "ok",
-     "timestamp": 1719507616360,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "a2b2272a-b25c-4954-8d34-5797f053a0fa",
-    "outputId": "3102da69-d54e-43f2-ee6f-9b3f8a7f6587"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X[['temperature']].plot() # plot of temperature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "a92df9c2-60f8-43f8-9c41-f81772755b63",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 448
-    },
-    "executionInfo": {
-     "elapsed": 1578,
-     "status": "ok",
-     "timestamp": 1719507621818,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "a92df9c2-60f8-43f8-9c41-f81772755b63",
-    "outputId": "28bd5d2f-a587-4368-ab60-849c7461dab7"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X[['SpO2_saturation']].plot()  # plot of SpO2_saturation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "7d1bd566-ef1c-4ddb-9d46-9863d413ac9a",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 6,
-     "status": "ok",
-     "timestamp": 1719507621819,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "7d1bd566-ef1c-4ddb-9d46-9863d413ac9a",
-    "outputId": "c973a253-7734-4fca-9ceb-1ec07b4758cb"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "heart_rate          69\n",
-       "temperature         29\n",
-       "SpO2_saturation     39\n",
-       "Health_status        4\n",
-       "health_condition     2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.nunique()  # finding for the count of unique values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "5e5828fb-19f6-407d-9b77-790da197a092",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 509,
-     "status": "ok",
-     "timestamp": 1719507625924,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "5e5828fb-19f6-407d-9b77-790da197a092",
-    "outputId": "f42e4931-28af-476b-dcab-ceaf9acd1077"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(579, 5)"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.shape # gives the shape of data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "5f27f2c0-c9ef-4a15-afe9-749a52866eb8",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 595,
-     "status": "ok",
-     "timestamp": 1719507628862,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "5f27f2c0-c9ef-4a15-afe9-749a52866eb8",
-    "outputId": "21d3d2c8-2994-4e3a-9053-3e3e5c698b34"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['heart_rate', 'temperature', 'SpO2_saturation', 'Health_status',\n",
-       "       'health_condition'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.columns # gives the columns of data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "9e77009e-deaf-4457-a240-13d5813950b0",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 3,
-     "status": "ok",
-     "timestamp": 1719507630419,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "9e77009e-deaf-4457-a240-13d5813950b0",
-    "outputId": "1c5494af-1ff6-4532-c64f-caed93892a68"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 579 entries, 0 to 1077\n",
-      "Data columns (total 5 columns):\n",
-      " #   Column            Non-Null Count  Dtype  \n",
-      "---  ------            --------------  -----  \n",
-      " 0   heart_rate        579 non-null    int64  \n",
-      " 1   temperature       579 non-null    float64\n",
-      " 2   SpO2_saturation   579 non-null    float64\n",
-      " 3   Health_status     579 non-null    object \n",
-      " 4   health_condition  579 non-null    int64  \n",
-      "dtypes: float64(2), int64(2), object(1)\n",
-      "memory usage: 27.1+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "data.info() # All information about data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "27265752-7453-4e5d-b26b-3012eb44e0c2",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 512,
-     "status": "ok",
-     "timestamp": 1719507633553,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "27265752-7453-4e5d-b26b-3012eb44e0c2"
-   },
-   "outputs": [],
-   "source": [
-    "data= data[[\"heart_rate\",\"temperature\",\"SpO2_saturation\",\"health_condition\"]] # taking only the required data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "9fc570ff-263a-40b4-a651-b0e94a2d3e9a",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 206
-    },
-    "executionInfo": {
-     "elapsed": 656,
-     "status": "ok",
-     "timestamp": 1719507636645,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "9fc570ff-263a-40b4-a651-b0e94a2d3e9a",
-    "outputId": "6a3ff95b-09d6-449e-a476-0b35f74e4928"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>heart_rate</th>\n",
-       "      <th>temperature</th>\n",
-       "      <th>SpO2_saturation</th>\n",
-       "      <th>health_condition</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>70</td>\n",
-       "      <td>38.6</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>74</td>\n",
-       "      <td>39.6</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>82</td>\n",
-       "      <td>37.2</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.6</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>72</td>\n",
-       "      <td>39.6</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   heart_rate  temperature  SpO2_saturation  health_condition\n",
-       "0          70         38.6             88.0                 1\n",
-       "1          74         39.6             88.0                 1\n",
-       "2          82         37.2             98.0                 0\n",
-       "3          90         38.6             98.0                 1\n",
-       "4          72         39.6             93.0                 1"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head()  # shows some data from starting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "757db057-87e1-4b0d-b6db-8d1314fea1ff",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719507637160,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "757db057-87e1-4b0d-b6db-8d1314fea1ff",
-    "outputId": "2d69f1be-a7e1-4360-c8c6-443fc356aa27"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 579 entries, 0 to 1077\n",
-      "Data columns (total 4 columns):\n",
-      " #   Column            Non-Null Count  Dtype  \n",
-      "---  ------            --------------  -----  \n",
-      " 0   heart_rate        579 non-null    int64  \n",
-      " 1   temperature       579 non-null    float64\n",
-      " 2   SpO2_saturation   579 non-null    float64\n",
-      " 3   health_condition  579 non-null    int64  \n",
-      "dtypes: float64(2), int64(2)\n",
-      "memory usage: 22.6 KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "data.info()  # again checking the data info"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "2615bc26-feb7-49cb-ab9b-b91ba60289af",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 507,
-     "status": "ok",
-     "timestamp": 1719507640103,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "2615bc26-feb7-49cb-ab9b-b91ba60289af",
-    "outputId": "2296ebc3-6a64-41c8-90b0-779030cffe8e"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "heart_rate          0\n",
-       "temperature         0\n",
-       "SpO2_saturation     0\n",
-       "health_condition    0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.isna().sum() # searching for null values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "e5ea2699-6047-4202-91b0-0b8c48aa49a1",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 492,
-     "status": "ok",
-     "timestamp": 1719507641379,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "e5ea2699-6047-4202-91b0-0b8c48aa49a1",
-    "outputId": "06294d58-9250-4b9a-f362-98e4ece3d1b7"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.duplicated().sum() # searching for the duplicate values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "00502630-e742-4528-b743-90ddd7820823",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719507644264,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "00502630-e742-4528-b743-90ddd7820823",
-    "outputId": "2efc14bf-4fde-4ccd-f886-a334aa3f3d54"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(569, 4)"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.drop_duplicates(inplace=True)  # deleting the duplicate values\n",
-    "data.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "2a4d0a7c-f8da-46e6-be1b-c92802f045c1",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 1074,
-     "status": "ok",
-     "timestamp": 1719507645872,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "2a4d0a7c-f8da-46e6-be1b-c92802f045c1",
-    "outputId": "e42ccb67-3909-4349-fb40-a7a78ed641a1"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 0])"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[\"health_condition\"].unique() # gives the unique values of health_condition"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "36b10ad8-e3e2-40fa-880a-5afce71c6acf",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 462
-    },
-    "executionInfo": {
-     "elapsed": 6,
-     "status": "ok",
-     "timestamp": 1719507647450,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "36b10ad8-e3e2-40fa-880a-5afce71c6acf",
-    "outputId": "04968365-9618-43e6-ef3b-618a94c044b0"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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UR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFGdYgbJ+/fr8+q//eqZMmZKZM2fm9ttvz/79+4esOX78eDo7OzN9+vRMnjw5S5YsSW9v75A1Bw4cSEdHRyZOnJiZM2fmvvvuy6lTpy78bACAS8KwAmXXrl3p7OzMc889lx07duTkyZNZtGhRjh07Vl+zatWqPPXUU3nyySeza9euHDp0KHfccUf9+OnTp9PR0ZETJ05k9+7defzxx7N169asW7du5M4KABjVmgYHBwfP986vvfZaZs6cmV27duXmm29OX19fZsyYkW3btuXOO+9Mkrzyyiu59tpr09XVlQULFuSZZ57JBz7wgRw6dCgtLS1Jki1btuT+++/Pa6+9lgkTJvzM5+3v70+1Wk1fX18qlcr5jj8qXf3ppxs9AhfRDx/qaPQIACNmOL+/L+galL6+viTJtGnTkiTd3d05efJkFi5cWF8zd+7czJkzJ11dXUmSrq6uXHfddfU4SZL29vb09/dn3759FzIOAHCJGHe+dzxz5kzuvffe/NZv/Vbe8573JEl6enoyYcKETJ06dcjalpaW9PT01Nf83zg5e/zssXMZGBjIwMBA/XZ/f//5jg0AjALn/QpKZ2dn/u3f/i1PPPHESM5zTuvXr0+1Wq1vs2fPftufEwBonPMKlJUrV2b79u35x3/8x1x11VX1/a2trTlx4kSOHDkyZH1vb29aW1vra378Uz1nb59d8+PWrFmTvr6++nbw4MHzGRsAGCWGFSiDg4NZuXJlvva1r+Xb3/52rrnmmiHH582bl/Hjx2fnzp31ffv378+BAwdSq9WSJLVaLXv37s3hw4fra3bs2JFKpZK2trZzPm9zc3MqlcqQDQC4dA3rGpTOzs5s27Ytf//3f58pU6bUrxmpVqu5/PLLU61Ws3z58qxevTrTpk1LpVLJPffck1qtlgULFiRJFi1alLa2ttx1113ZsGFDenp6snbt2nR2dqa5uXnkzxAAGHWGFSiPPvpokuR3f/d3h+x/7LHH8rGPfSxJsnHjxowZMyZLlizJwMBA2tvbs3nz5vrasWPHZvv27VmxYkVqtVomTZqUZcuW5cEHH7ywMwEALhkX9D0ojeJ7UHin8D0owKXkon0PCgDA20GgAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxRl2oHznO9/JBz/4wcyaNStNTU35+te/PuT44OBg1q1blyuvvDKXX355Fi5cmO9///tD1rzxxhtZunRpKpVKpk6dmuXLl+fNN9+8oBMBAC4dww6UY8eO5YYbbsgjjzxyzuMbNmzIpk2bsmXLljz//POZNGlS2tvbc/z48fqapUuXZt++fdmxY0e2b9+e73znO7n77rvP/ywAgEvKuOHeYfHixVm8ePE5jw0ODubhhx/O2rVrc9tttyVJ/vqv/zotLS35+te/ng9/+MP593//9zz77LPZs2dPbrrppiTJX/7lX+YP/uAP8vnPfz6zZs26gNMBAC4FI3oNyquvvpqenp4sXLiwvq9arWb+/Pnp6upKknR1dWXq1Kn1OEmShQsXZsyYMXn++edHchwAYJQa9isoP01PT0+SpKWlZcj+lpaW+rGenp7MnDlz6BDjxmXatGn1NT9uYGAgAwMD9dv9/f0jOTYAUJhR8Sme9evXp1qt1rfZs2c3eiQA4G00ooHS2tqaJOnt7R2yv7e3t36stbU1hw8fHnL81KlTeeONN+prftyaNWvS19dX3w4ePDiSYwMAhRnRQLnmmmvS2tqanTt31vf19/fn+eefT61WS5LUarUcOXIk3d3d9TXf/va3c+bMmcyfP/+cj9vc3JxKpTJkAwAuXcO+BuXNN9/MD37wg/rtV199NS+99FKmTZuWOXPm5N57782f/dmf5Zd/+ZdzzTXX5E//9E8za9as3H777UmSa6+9Nu9///vziU98Ilu2bMnJkyezcuXKfPjDH/YJHgAgyXkEyosvvpjf+73fq99evXp1kmTZsmXZunVr/uRP/iTHjh3L3XffnSNHjuR973tfnn322Vx22WX1+3z1q1/NypUrc+utt2bMmDFZsmRJNm3aNAKnAwBcCpoGBwcHGz3EcPX396daraavr+8d93bP1Z9+utEjcBH98KGORo8AMGKG8/t7VHyKBwB4ZxEoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAccY1egAA/sfVn3660SNwEf3woY5Gj1A0r6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUJyGBsojjzySq6++Opdddlnmz5+fF154oZHjAACFaFig/O3f/m1Wr16dBx54IN/97ndzww03pL29PYcPH27USABAIRoWKF/4whfyiU98Ih//+MfT1taWLVu2ZOLEifnKV77SqJEAgEKMa8STnjhxIt3d3VmzZk1935gxY7Jw4cJ0dXW9Zf3AwEAGBgbqt/v6+pIk/f39b/+whTkz8P8aPQIX0Tvxv/F3Mj/f7yzvxJ/vs+c8ODj4M9c2JFBef/31nD59Oi0tLUP2t7S05JVXXnnL+vXr1+ezn/3sW/bPnj37bZsRSlB9uNETAG+Xd/LP99GjR1OtVn/qmoYEynCtWbMmq1evrt8+c+ZM3njjjUyfPj1NTU0NnIyLob+/P7Nnz87BgwdTqVQaPQ4wgvx8v7MMDg7m6NGjmTVr1s9c25BAueKKKzJ27Nj09vYO2d/b25vW1ta3rG9ubk5zc/OQfVOnTn07R6RAlUrF/8DgEuXn+53jZ71yclZDLpKdMGFC5s2bl507d9b3nTlzJjt37kytVmvESABAQRr2Fs/q1auzbNmy3HTTTfmN3/iNPPzwwzl27Fg+/vGPN2okAKAQDQuUD33oQ3nttdeybt269PT05MYbb8yzzz77lgtnobm5OQ888MBb3uYDRj8/3/wkTYM/z2d9AAAuIn+LBwAojkABAIojUACA4ggUAKA4AgUAKM6o+Kp7AC4Nr7/+er7yla+kq6srPT09SZLW1tb85m/+Zj72sY9lxowZDZ6QUngFhVHn4MGD+aM/+qNGjwEM0549e/Lud787mzZtSrVazc0335ybb7451Wo1mzZtyty5c/Piiy82ekwK4XtQGHW+973v5b3vfW9Onz7d6FGAYViwYEFuuOGGbNmy5S1/6HVwcDCf/OQn86//+q/p6upq0ISUxFs8FOcf/uEffurx//zP/7xIkwAj6Xvf+162bt16zr9C39TUlFWrVuXXfu3XGjAZJRIoFOf2229PU1NTftqLe+f6HxxQttbW1rzwwguZO3fuOY+/8MIL/twJdQKF4lx55ZXZvHlzbrvttnMef+mllzJv3ryLPBVwoT71qU/l7rvvTnd3d2699dZ6jPT29mbnzp350pe+lM9//vMNnpJSCBSKM2/evHR3d//EQPlZr64AZers7MwVV1yRjRs3ZvPmzfXryMaOHZt58+Zl69at+cM//MMGT0kpXCRLcf75n/85x44dy/vf//5zHj927FhefPHF/M7v/M5FngwYKSdPnszrr7+eJLniiisyfvz4Bk9EaQQKAFAc34MCABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFCc/w+ce4sy5aP/5wAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data[\"health_condition\"].value_counts().plot.bar()  # plot of health_condition"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "0917a262-f412-448c-a85f-8b29e7163370",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 515,
-     "status": "ok",
-     "timestamp": 1719507648723,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "0917a262-f412-448c-a85f-8b29e7163370",
-    "outputId": "2851e94e-2302-443d-a0d1-406ecf798ee7"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1    543\n",
-       "0     26\n",
-       "Name: health_condition, dtype: int64"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[\"health_condition\"].value_counts()  # gives the value count of every unique health conditions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "-s6FjDfTiWXC",
-   "metadata": {
-    "id": "-s6FjDfTiWXC"
-   },
-   "source": [
-    "### A Seaborn heatmap is a data visualization tool that displays a matrix of values as a grid of colors, making it easy to identify patterns, correlations, and variations in the data. It is particularly useful for visualizing correlation matrices, confusion matrices, and other types of matrix data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "b0639500-5651-454e-8326-80e0b38c325d",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 555
-    },
-    "executionInfo": {
-     "elapsed": 920,
-     "status": "ok",
-     "timestamp": 1719507653514,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "b0639500-5651-454e-8326-80e0b38c325d",
-    "outputId": "0163947c-6051-4a2d-b19d-11385d6512a8"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.heatmap(data.corr())  # plot the heat map of the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "_YsaJoHziqPC",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 4,
-     "status": "ok",
-     "timestamp": 1719507653514,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "_YsaJoHziqPC"
-   },
-   "outputs": [],
-   "source": [
-    "# Fill missing values\n",
-    "data.fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "6NpNPZOiivE4",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 518,
-     "status": "ok",
-     "timestamp": 1719507657726,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "6NpNPZOiivE4"
-   },
-   "outputs": [],
-   "source": [
-    "# Convert all feature columns to numeric\n",
-    "for col in data.columns:\n",
-    "    data[col] = pd.to_numeric(data[col], errors='coerce')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "fthKrROZiu33",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719507658823,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "fthKrROZiu33"
-   },
-   "outputs": [],
-   "source": [
-    "# Fill any remaining NaNs that might have been introduced\n",
-    "data.fillna(0, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "scg8GT-siukT",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 3,
-     "status": "ok",
-     "timestamp": 1719507659543,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "scg8GT-siukT"
-   },
-   "outputs": [],
-   "source": [
-    "# Encode the target variable\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "label_encoder = LabelEncoder()\n",
-    "data['health_condition'] = label_encoder.fit_transform(data['health_condition'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "6ff31ff3-13bc-419a-aa3b-b68b4866299c",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719507660918,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "6ff31ff3-13bc-419a-aa3b-b68b4866299c"
-   },
-   "outputs": [],
-   "source": [
-    "# Define features and target\n",
-    "X = data.drop('health_condition', axis=1)\n",
-    "y = data['health_condition']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "JXHwmjcQFEug",
-   "metadata": {
-    "id": "JXHwmjcQFEug"
-   },
-   "source": [
-    "### **3. Model Development:**\n",
-    "Split the dataset into training and testing sets, and implement a Decision Tree Classifier using TensorFlow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "6oaWSvJtjMjX",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719507661607,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "6oaWSvJtjMjX"
-   },
-   "outputs": [],
-   "source": [
-    "# Split the dataset into training and testing sets\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "S-4Y0tTHjN36",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 514,
-     "status": "ok",
-     "timestamp": 1719507664773,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "S-4Y0tTHjN36"
-   },
-   "outputs": [],
-   "source": [
-    "# Build the Decision Tree Classifier model\n",
-    "model = tf.keras.Sequential([\n",
-    "    tf.keras.layers.Input(shape=(X_train.shape[1],)),\n",
-    "    tf.keras.layers.Dense(128, activation='relu'),\n",
-    "    tf.keras.layers.Dense(64, activation='relu'),\n",
-    "    tf.keras.layers.Dense(len(label_encoder.classes_), activation='softmax')\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "XFWm3eccKkzK",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 1,
-     "status": "ok",
-     "timestamp": 1719507665440,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "XFWm3eccKkzK"
-   },
-   "outputs": [],
-   "source": [
-    "# Compile the model\n",
-    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "XAEHkelZFhfZ",
-   "metadata": {
-    "id": "XAEHkelZFhfZ"
-   },
-   "source": [
-    "### **4. Model Training**\n",
-    "Train the model using the training data and monitor its performance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "3a198884-0e31-4304-b56c-56a2cdb9de92",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719507667017,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "3a198884-0e31-4304-b56c-56a2cdb9de92"
-   },
-   "outputs": [],
-   "source": [
-    "X = data.loc[:,[\"heart_rate\",\"temperature\",\"SpO2_saturation\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "686e0661-2ed9-4ba8-80a0-ee1389fa098b",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 424
-    },
-    "executionInfo": {
-     "elapsed": 5,
-     "status": "ok",
-     "timestamp": 1719507667870,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "686e0661-2ed9-4ba8-80a0-ee1389fa098b",
-    "outputId": "3298b68c-14c3-400b-a134-ff6dc2796fad"
-   },
-   "outputs": [
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-       "      <th>SpO2_saturation</th>\n",
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-       "      heart_rate  temperature  SpO2_saturation\n",
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-       "2             82         37.2             98.0\n",
-       "3             90         38.6             98.0\n",
-       "4             72         39.6             93.0\n",
-       "...          ...          ...              ...\n",
-       "970          111         38.0             30.0\n",
-       "1019         109         38.0             30.0\n",
-       "1020         110         38.0             30.0\n",
-       "1031         108         38.0             30.0\n",
-       "1052         107         38.0             30.0\n",
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-       "[569 rows x 3 columns]"
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-     "execution_count": 80,
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-   "source": [
-    "X"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "1c574e38-29a7-44fc-8983-e24a3f264f6b",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 1,
-     "status": "ok",
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-     "user": {
-      "displayName": "Harsh Raj",
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-    "id": "1c574e38-29a7-44fc-8983-e24a3f264f6b"
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-   "outputs": [],
-   "source": [
-    "y = data.iloc[:,3].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "51060de4-0256-4e3e-915f-488cc6f28cb7",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
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-    "executionInfo": {
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-     "status": "ok",
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-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
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-    "outputId": "9656998f-a774-47ad-b380-9b5f8e656c7a"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0       1\n",
-       "1       1\n",
-       "2       0\n",
-       "3       1\n",
-       "4       1\n",
-       "       ..\n",
-       "970     1\n",
-       "1019    1\n",
-       "1020    1\n",
-       "1031    1\n",
-       "1052    1\n",
-       "Name: health_condition, Length: 569, dtype: int64"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "fbe2290c-0f0d-4331-ad3a-ecb9242f3c9e",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719507673170,
-     "user": {
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-      "userId": "13475918194215025488"
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-     "user_tz": -330
-    },
-    "id": "fbe2290c-0f0d-4331-ad3a-ecb9242f3c9e"
-   },
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "dd998b57-56c1-4e20-ab77-b565e45693f6",
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-       " [107.0, 38.5, 85.0],\n",
-       " [56.0, 38.1, 55.0],\n",
-       " [69.0, 39.0, 30.0],\n",
-       " [90.0, 38.2, 87.0],\n",
-       " [55.0, 38.1, 55.0],\n",
-       " [67.0, 38.1, 70.0],\n",
-       " [58.0, 38.1, 45.0],\n",
-       " [81.0, 37.6, 84.0],\n",
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-       " [82.0, 37.7, 95.0],\n",
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-       " [77.0, 38.5, 79.0],\n",
-       " [81.0, 39.0, 30.0],\n",
-       " [73.0, 39.0, 30.0],\n",
-       " [71.0, 38.9, 75.0],\n",
-       " [117.0, 37.2, 82.0],\n",
-       " [71.0, 38.1, 98.0],\n",
-       " [105.0, 38.0, 90.0],\n",
-       " [92.0, 38.9, 75.0],\n",
-       " [63.0, 38.1, 20.0],\n",
-       " [62.0, 37.5, 82.0],\n",
-       " [81.0, 38.9, 75.0],\n",
-       " [72.0, 38.6, 82.0],\n",
-       " [62.0, 38.0, 85.0],\n",
-       " [73.0, 38.1, 70.0],\n",
-       " [84.0, 37.9, 89.0],\n",
-       " [110.0, 38.0, 90.0],\n",
-       " [83.0, 38.0, 97.0],\n",
-       " [79.0, 38.0, 97.0],\n",
-       " [68.0, 37.3, 97.0],\n",
-       " [66.0, 38.1, 130.0],\n",
-       " [84.0, 38.0, 98.0],\n",
-       " [97.0, 39.0, 85.0],\n",
-       " [64.0, 39.0, 30.0],\n",
-       " [54.0, 38.1, 20.0],\n",
-       " [83.0, 38.0, 84.0],\n",
-       " [114.0, 38.0, 30.0],\n",
-       " [85.0, 39.0, 85.0],\n",
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-       " [47.0, 95.0, 92.0],\n",
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-       " [78.0, 39.0, 30.0],\n",
-       " [65.0, 38.6, 82.0],\n",
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-       " [88.0, 38.0, 89.0],\n",
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-       " [91.0, 36.1, 66.0],\n",
-       " [69.0, 37.2, 82.0],\n",
-       " [63.0, 37.2, 70.0],\n",
-       " [75.0, 39.6, 96.0],\n",
-       " [75.0, 37.2, 96.0],\n",
-       " [64.0, 38.0, 85.0],\n",
-       " [98.0, 39.6, 82.0],\n",
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-       " [90.0, 40.0, 88.0],\n",
-       " [75.0, 38.0, 90.0],\n",
-       " [81.0, 37.9, 89.0],\n",
-       " [66.0, 38.1, 165.0],\n",
-       " [119.0, 39.0, 85.0],\n",
-       " [76.0, 37.6, 30.0],\n",
-       " [61.0, 37.2, 82.0],\n",
-       " [60.0, 38.1, 35.0],\n",
-       " [111.0, 38.5, 85.0],\n",
-       " [93.0, 37.2, 82.0],\n",
-       " [72.0, 37.8, 91.0],\n",
-       " [81.0, 37.2, 82.0],\n",
-       " [90.0, 38.0, 82.0],\n",
-       " [116.0, 38.5, 30.0],\n",
-       " [65.0, 37.2, 96.0],\n",
-       " [106.0, 38.9, 75.0],\n",
-       " [58.0, 38.1, 40.0],\n",
-       " [66.0, 38.0, 91.0],\n",
-       " [77.0, 38.9, 75.0],\n",
-       " [73.0, 39.6, 70.0],\n",
-       " [65.0, 37.2, 82.0],\n",
-       " [79.0, 37.5, 91.0],\n",
-       " [67.0, 38.1, 35.0],\n",
-       " [64.0, 36.4, 85.0],\n",
-       " [96.0, 39.0, 85.0],\n",
-       " [87.0, 38.0, 98.0],\n",
-       " [66.0, 39.0, 30.0],\n",
-       " [68.0, 39.0, 85.0],\n",
-       " [78.0, 37.2, 91.0],\n",
-       " [108.0, 38.5, 175.0],\n",
-       " [67.0, 38.1, 75.0],\n",
-       " [72.0, 38.0, 90.0],\n",
-       " [59.0, 39.0, 70.0],\n",
-       " [81.0, 36.9, 98.0],\n",
-       " [80.0, 37.1, 96.0],\n",
-       " [110.0, 38.5, 87.3],\n",
-       " [96.0, 38.0, 89.0],\n",
-       " [112.0, 38.0, 30.0],\n",
-       " [90.0, 38.5, 79.0],\n",
-       " [66.0, 39.6, 80.0],\n",
-       " [110.0, 38.0, 30.0],\n",
-       " [87.0, 38.9, 75.0],\n",
-       " [55.0, 38.1, 130.0],\n",
-       " [91.0, 38.1, 85.0],\n",
-       " [92.0, 38.5, 88.0],\n",
-       " [64.0, 38.1, 98.0],\n",
-       " [61.0, 38.0, 70.0],\n",
-       " [59.0, 37.2, 82.0],\n",
-       " [95.0, 37.8, 91.0],\n",
-       " [78.0, 38.0, 98.0],\n",
-       " [72.0, 39.0, 30.0],\n",
-       " [60.0, 38.0, 85.0],\n",
-       " [93.0, 36.8, 97.0],\n",
-       " [67.0, 38.9, 75.0],\n",
-       " [76.0, 37.9, 89.0],\n",
-       " [92.0, 95.0, 92.0],\n",
-       " [57.0, 38.1, 20.0],\n",
-       " [74.0, 39.6, 88.0],\n",
-       " [108.0, 38.5, 75.0],\n",
-       " [70.0, 36.5, 61.3],\n",
-       " [63.0, 39.0, 70.0],\n",
-       " [80.0, 37.2, 96.0],\n",
-       " [89.0, 38.5, 85.0],\n",
-       " [66.0, 37.6, 30.0],\n",
-       " [72.0, 38.0, 95.0],\n",
-       " [62.0, 38.1, 20.0],\n",
-       " [68.0, 36.5, 96.0],\n",
-       " [65.0, 38.1, 55.0],\n",
-       " [58.0, 38.1, 20.0],\n",
-       " [89.0, 38.0, 89.0],\n",
-       " [94.0, 38.6, 70.0],\n",
-       " [73.0, 38.9, 75.0],\n",
-       " [56.0, 38.1, 30.0],\n",
-       " [88.0, 38.9, 85.0],\n",
-       " [65.0, 37.8, 89.0],\n",
-       " [113.0, 38.0, 30.0],\n",
-       " [95.0, 38.5, 88.0],\n",
-       " [116.0, 39.1, 30.0],\n",
-       " [93.0, 38.5, 85.0],\n",
-       " [85.0, 38.0, 89.0],\n",
-       " [64.0, 37.8, 85.0],\n",
-       " [91.0, 39.1, 97.0],\n",
-       " [98.0, 38.5, 85.0],\n",
-       " [92.0, 36.4, 91.0],\n",
-       " [92.0, 36.0, 97.0],\n",
-       " [90.0, 38.0, 89.0],\n",
-       " [72.0, 37.2, 97.0],\n",
-       " [74.0, 38.9, 75.0],\n",
-       " [62.0, 38.5, 79.0],\n",
-       " [68.0, 39.6, 82.0],\n",
-       " [94.0, 39.0, 85.0],\n",
-       " [66.0, 37.8, 85.0],\n",
-       " [65.0, 38.1, 40.0],\n",
-       " [60.0, 39.0, 30.0],\n",
-       " [96.0, 39.0, 95.0],\n",
-       " [59.0, 38.1, 40.0],\n",
-       " [80.0, 38.9, 75.0],\n",
-       " [84.0, 38.6, 82.0],\n",
-       " [64.0, 36.5, 70.0],\n",
-       " [66.0, 38.1, 80.0],\n",
-       " [74.0, 38.0, 93.0],\n",
-       " [108.0, 38.5, 79.0],\n",
-       " [86.0, 38.5, 75.0],\n",
-       " [76.0, 36.9, 98.0],\n",
-       " [85.0, 38.2, 66.0],\n",
-       " [87.0, 38.0, 97.0],\n",
-       " [70.0, 39.6, 97.0],\n",
-       " [68.0, 37.5, 91.0],\n",
-       " [86.0, 95.0, 92.0],\n",
-       " [80.0, 38.1, 85.0],\n",
-       " [67.0, 38.3, 70.0],\n",
-       " [92.0, 39.0, 95.0]]"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train.values.tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "Sc6NkbVfjYJt",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 520,
-     "status": "ok",
-     "timestamp": 1719507679640,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "Sc6NkbVfjYJt"
-   },
-   "outputs": [],
-   "source": [
-    "# Convert to Pandas Series if y_train is a NumPy array\n",
-    "if isinstance(y_train, np.ndarray):\n",
-    "    y_train = pd.Series(y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "437734ae-7a8b-4634-9679-1c440212bd7f",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 7,
-     "status": "ok",
-     "timestamp": 1719507680530,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "437734ae-7a8b-4634-9679-1c440212bd7f",
-    "outputId": "45bebca5-2e7f-4de5-acc9-bb71a575d12f"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Now you can use y_train.values.tolist() if necessary\n",
-    "y_train_list = y_train.values.tolist()\n",
-    "print(y_train_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "01aa50dd-9ffe-430a-9325-78fb8a3dec0e",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 75
-    },
-    "executionInfo": {
-     "elapsed": 733,
-     "status": "ok",
-     "timestamp": 1719508799898,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "01aa50dd-9ffe-430a-9325-78fb8a3dec0e",
-    "outputId": "181e802f-995c-4f97-f3bb-ec9bc3edcc72"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(random_state=0)</pre></div> </div></div></div></div>"
-      ],
-      "text/plain": [
-       "DecisionTreeClassifier(random_state=0)"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "health_classifier = DecisionTreeClassifier(random_state = 0)\n",
-    "health_classifier.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "82c869a1-bb28-48ee-996f-52ab26ec0cbc",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 221
-    },
-    "executionInfo": {
-     "elapsed": 6,
-     "status": "ok",
-     "timestamp": 1719508800736,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "82c869a1-bb28-48ee-996f-52ab26ec0cbc",
-    "outputId": "4cd70c42-c2d5-4258-e223-24d6e3e849a5"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "sklearn.tree._classes.DecisionTreeClassifier"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(health_classifier)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "vULqmNvVF4q4",
-   "metadata": {
-    "id": "vULqmNvVF4q4"
-   },
-   "source": [
-    "### **5. Model Evaluation**\n",
-    "Evaluate the model's performance using metrics such as accuracy, precision, recall, and F1-score."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "lYjAaucHjeij",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719508805313,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "lYjAaucHjeij"
-   },
-   "outputs": [],
-   "source": [
-    "# Make predictions\n",
-    "y_pred_array = health_classifier.predict(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "68E1R0hGjee_",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719508806205,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "68E1R0hGjee_"
-   },
-   "outputs": [],
-   "source": [
-    "# Ensure y_test is a Pandas Series to access its index\n",
-    "if isinstance(y_test, np.ndarray):\n",
-    "    y_test = pd.Series(y_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "UL0yIOXcjeYi",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 3,
-     "status": "ok",
-     "timestamp": 1719508809372,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "UL0yIOXcjeYi"
-   },
-   "outputs": [],
-   "source": [
-    "# Create DataFrame from the predictions\n",
-    "y_pred = pd.DataFrame(y_pred_array, columns=[\"health_condition\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "chg9gMVRjkH9",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719508810355,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "chg9gMVRjkH9"
-   },
-   "outputs": [],
-   "source": [
-    "# Set the index of the DataFrame to match y_test index\n",
-    "y_pred.index = y_test.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "acb6ff0f-e48c-4184-9cd7-75281b5a5c9b",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719508810939,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "acb6ff0f-e48c-4184-9cd7-75281b5a5c9b"
-   },
-   "outputs": [],
-   "source": [
-    "# Ensure the data type is int\n",
-    "y_pred = y_pred.astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "rHo5wG80jpeR",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719508813431,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "rHo5wG80jpeR",
-    "outputId": "d6f6fee2-a6d5-4a82-b44b-910608930c5a"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     health_condition\n",
-      "239                 1\n",
-      "71                  1\n",
-      "141                 1\n",
-      "570                 1\n",
-      "763                 1\n",
-      "..                ...\n",
-      "70                  1\n",
-      "769                 1\n",
-      "193                 1\n",
-      "663                 1\n",
-      "292                 1\n",
-      "\n",
-      "[171 rows x 1 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(y_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "65aab513-c6cf-4489-96f9-7d750754233e",
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 3,
-     "status": "ok",
-     "timestamp": 1719508814275,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "65aab513-c6cf-4489-96f9-7d750754233e"
-   },
-   "outputs": [],
-   "source": [
-    "y_test = np.array(y_test,dtype = int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "05ca175a-a41b-42c3-92bc-97375c6aab73",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 666,
-     "status": "ok",
-     "timestamp": 1719508818442,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "05ca175a-a41b-42c3-92bc-97375c6aab73",
-    "outputId": "53cc1ca9-3b27-4909-aa6c-c67803cd30f7"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(y_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "c1e80c44-e162-4ca1-83a5-13f64b036b19",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719508819229,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "c1e80c44-e162-4ca1-83a5-13f64b036b19",
-    "outputId": "dd6fa28c-2934-47bc-ce9c-1a7d24cec492"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
-       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "42c4582e-1a9e-4ca5-9133-d4ff0bc7ca92",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 892,
-     "status": "ok",
-     "timestamp": 1719508822627,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "42c4582e-1a9e-4ca5-9133-d4ff0bc7ca92",
-    "outputId": "1e341c9f-1958-463b-86c1-a9c3b7e4937b"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.9941520467836257"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Measure Accuracy\n",
-    "metrics.accuracy_score(y_test,y_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "181bd3c2-6026-4092-ac0b-4a51c928d6b6",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 2,
-     "status": "ok",
-     "timestamp": 1719508823442,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "181bd3c2-6026-4092-ac0b-4a51c928d6b6",
-    "outputId": "b0da7a65-b3ca-4d5b-aa77-cd8d82ca20ab"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.83      1.00      0.91         5\n",
-      "           1       1.00      0.99      1.00       166\n",
-      "\n",
-      "    accuracy                           0.99       171\n",
-      "   macro avg       0.92      1.00      0.95       171\n",
-      "weighted avg       1.00      0.99      0.99       171\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(metrics.classification_report(y_test, y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "566afdb7-c26f-46b0-9ae2-0e0ca73ef037",
-   "metadata": {
-    "id": "566afdb7-c26f-46b0-9ae2-0e0ca73ef037"
-   },
-   "source": [
-    "### **6. Model Deployment**\n",
-    "Save the trained model for future use and deploying it in real-world applications."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "2f755dff-26ee-407e-8c0b-2a3056d0c1cb",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 573,
-     "status": "ok",
-     "timestamp": 1719508828055,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "2f755dff-26ee-407e-8c0b-2a3056d0c1cb",
-    "outputId": "133408ae-de84-493d-f297-19de8afedb20"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "#pragma once\n",
-      "#include <cstdarg>\n",
-      "namespace Eloquent {\n",
-      "    namespace ML {\n",
-      "        namespace Port {\n",
-      "            class DecisionTree {\n",
-      "                public:\n",
-      "                    /**\n",
-      "                    * Predict class for features vector\n",
-      "                    */\n",
-      "                    int predict(float *x) {\n",
-      "                        if (x[1] <= 37.25) {\n",
-      "                            if (x[2] <= 94.0) {\n",
-      "                                return 1;\n",
-      "                            }\n",
-      "\n",
-      "                            else {\n",
-      "                                if (x[0] <= 100.0) {\n",
-      "                                    if (x[1] <= 36.25) {\n",
-      "                                        return 1;\n",
-      "                                    }\n",
-      "\n",
-      "                                    else {\n",
-      "                                        return 0;\n",
-      "                                    }\n",
-      "                                }\n",
-      "\n",
-      "                                else {\n",
-      "                                    return 1;\n",
-      "                                }\n",
-      "                            }\n",
-      "                        }\n",
-      "\n",
-      "                        else {\n",
-      "                            return 1;\n",
-      "                        }\n",
-      "                    }\n",
-      "\n",
-      "                protected:\n",
-      "                };\n",
-      "            }\n",
-      "        }\n",
-      "    }\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(port(health_classifier))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ScKF5VgskBQq",
-   "metadata": {
-    "id": "ScKF5VgskBQq"
-   },
-   "source": [
-    "Now copy the above Decision Tree classifier code and save in any text file with name\n",
-    "\n",
-    "```\n",
-    "DecisionTree.h\n",
-    "```\n",
-    "\n",
-    "Then copy the below arduino code and save it named as\n",
-    "\n",
-    "```\n",
-    "health_determining_model.ino\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "q_eDDNaljuW6",
-   "metadata": {
-    "id": "q_eDDNaljuW6"
-   },
-   "source": [
-    "\n",
-    "\n",
-    "```\n",
-    "#include <Wire.h>  \n",
-    "#include \"MAX30100_PulseOximeter.h\"\n",
-    "#include \"DecisionTree.h\"\n",
-    "\n",
-    "Eloquent::ML::Port::DecisionTree health_Classifier;\n",
-    "\n",
-    "#define TEMPERATURE_SENSOR_PIN 32  // Analog pin for the temperature sensor (replace with the actual pin number)\n",
-    "#define HEART_RATE_SENSOR_PIN 35   // Analog pin for the heart rate sensor (replace with the actual pin number)\n",
-    "\n",
-    "// Initialize the PulseOximeter library\n",
-    "PulseOximeter pox;\n",
-    "\n",
-    "// Variable Declaration\n",
-    "float t;\n",
-    "int h;\n",
-    "int s;\n",
-    "int prediction;\n",
-    "\n",
-    "void setup() {\n",
-    "  Serial.begin(9600);\n",
-    "  Serial.println(\"Welcome!\");\n",
-    "\n",
-    "  pinMode(TEMPERATURE_SENSOR_PIN, INPUT);  \n",
-    "  pinMode(HEART_RATE_SENSOR_PIN, INPUT);   \n",
-    "\n",
-    "  // Initialize the Pulse Oximeter\n",
-    "  if (!pox.begin()) {\n",
-    "    Serial.println(\"Failed to initialize pulse oximeter!\");\n",
-    "    while (1);\n",
-    "  } else {\n",
-    "    Serial.println(\"Pulse oximeter initialized successfully!\");\n",
-    "  }\n",
-    "}\n",
-    "\n",
-    "void loop() {\n",
-    "  delay(2000);\n",
-    "\n",
-    "  // Sensor data Collection part\n",
-    "\n",
-    "  // Read temperature value\n",
-    "  int temperatureValue = analogRead(TEMPERATURE_SENSOR_PIN);\n",
-    "  t = convertToCelsius(temperatureValue);\n",
-    "  Serial.print(\"Temperature (°C): \");\n",
-    "  Serial.println(t);\n",
-    "\n",
-    "  // Read heart rate value\n",
-    "  h = analogRead(HEART_RATE_SENSOR_PIN);\n",
-    "  Serial.print(\"Heart Rate (bpm): \");\n",
-    "  Serial.println(h);\n",
-    "\n",
-    "  // Read SpO2 value\n",
-    "  int s = pox.getSpO2();\n",
-    "  Serial.print(\"SpO2 (%): \");\n",
-    "  Serial.println(s);\n",
-    "\n",
-    "  // Print values of heart_rate, temperature, and SpO2_saturation to serial monitor\n",
-    "  Serial.print(F(\"Heart_rate: \"));\n",
-    "  Serial.print(h);\n",
-    "  Serial.print(F(\"  Temperature: \"));\n",
-    "  Serial.print(t);\n",
-    "  Serial.print(F(\"  SpO2: \"));\n",
-    "  Serial.println(s);\n",
-    "\n",
-    "  float input[3] = {h, t, s};\n",
-    "  // Giving input values to predict Function\n",
-    "  int prediction = health_Classifier.predict(input);\n",
-    "\n",
-    "  // Checking Condition\n",
-    "  Serial.print(\"Prediction: \");\n",
-    "  switch (prediction) {\n",
-    "    case 0:\n",
-    "      Serial.println(\"Normal\");\n",
-    "      break;\n",
-    "    case 1:\n",
-    "      Serial.println(\"Serious\");\n",
-    "      break;\n",
-    "    case 2:\n",
-    "      Serial.println(\"Dischargeable\");\n",
-    "      break;\n",
-    "    default:\n",
-    "      Serial.println(\"Unknown\");\n",
-    "      break;\n",
-    "  }\n",
-    "\n",
-    "  Serial.println();\n",
-    "  delay(1000);\n",
-    "}\n",
-    "\n",
-    "float convertToCelsius(int adcValue) {\n",
-    "  float voltage = adcValue * 3.3 / 4095.0;  // Convert ADC value to voltage for ESP32\n",
-    "  float temperatureCelsius = (voltage - 0.5) * 100;  // Convert voltage to temperature in Celsius\n",
-    "  return temperatureCelsius;\n",
-    "}\n",
-    "\n",
-    "\n",
-    "```\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3ab7a75f-a119-40b0-a116-4c8e8a7d9d44",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 397
-    },
-    "executionInfo": {
-     "elapsed": 1054,
-     "status": "error",
-     "timestamp": 1719508850073,
-     "user": {
-      "displayName": "Harsh Raj",
-      "userId": "13475918194215025488"
-     },
-     "user_tz": -330
-    },
-    "id": "3ab7a75f-a119-40b0-a116-4c8e8a7d9d44",
-    "outputId": "071eb97e-448c-4e17-ed8b-54b8cad5b443"
-   },
-   "outputs": [],
-   "source": [
-    "import serial\n",
-    "import time\n",
-    "\n",
-    "# Replace 'COM10' with the appropriate serial port for your system\n",
-    "SERIAL_PORT = 'COM13' #replace with your actual usb port\n",
-    "BAUD_RATE = 9600  # the baud rate set on your ESP32\n",
-    "\n",
-    "def read_from_esp32():\n",
-    "    # Open the serial connection\n",
-    "    with serial.Serial(SERIAL_PORT, BAUD_RATE, timeout=1) as ser:\n",
-    "        # Give some time for the serial connection to establish\n",
-    "        time.sleep(2)\n",
-    "\n",
-    "        # Clear any existing input in the buffer\n",
-    "        ser.flushInput()\n",
-    "\n",
-    "        try:\n",
-    "            while True:\n",
-    "                # Read data from the ESP32\n",
-    "                if ser.in_waiting > 0:\n",
-    "                    line = ser.readline().decode('utf-8').rstrip()\n",
-    "                    print(f\"{line}\")\n",
-    "                time.sleep(0.1)\n",
-    "\n",
-    "        except KeyboardInterrupt:\n",
-    "            print(\"Serial read stopped.\")\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    read_from_esp32()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ecTNmC1xmal_",
-   "metadata": {
-    "id": "ecTNmC1xmal_"
-   },
-   "source": [
-    "#### Now move these both files in same folder then open the \"health_determining_model.ino\" file in your arduino IDE and upload the code to your Esp32 microcontroller.\n",
-    "\n",
-    "#### Before uploading the code to the board, do verify that the \"DecisionTree.h\" file is also opened in the same arduino IDE beside the .ino file.\n",
-    "\n",
-    "#### If any error occurs then open the \"DecisionTree.h\" file and comment the line containing this code\n",
-    "\n",
-    "```\n",
-    "#include <cstdarg> // comment this line\n",
-    "```\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8d2d1a42-aca1-4369-867d-8203479aad9b",
-   "metadata": {
-    "id": "8d2d1a42-aca1-4369-867d-8203479aad9b"
-   },
-   "source": [
-    "# Happy Learning"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Jupyter-notebook/Machine learning/Health prediction model/dataset.csv b/Jupyter-notebook/Machine learning/Health prediction model/dataset.csv
deleted file mode 100644
index 069adcd..0000000
--- a/Jupyter-notebook/Machine learning/Health prediction model/dataset.csv
+++ /dev/null
@@ -1,1086 +0,0 @@
-id,gender,age,heart_rate,temperature,SpO2_saturation,bpm,Health_status
-1,male,66,70,38.6,88,75,Infected
-2,female,56,74,39.6,88,70,Infected
-3,male,46,82,37.2,98,83,Non Infected
-4,female,60,90,38.6,98,75,Non Infected
-5,male,58,72,39.6,93,78,Infected
-6,female,44,72,37.2,93,69,Infected
-7,male,34,67,38.6,98,73,Non Infected
-8,male,37,72,39.6,97,73,Non Infected
-9,male,39,65,37.2,96,84,Non Infected
-10,male,56,76,38,92,68,Infected
-11,female,18,73,38.6,50,73,Infected
-12,female,32,64,39.6,97,79,Non Infected
-13,male,37,63,37.2,70,81,Infected
-14,male,51,94,38.6,70,69,Infected
-15,male,57,73,39.6,70,72,Infected
-16,male,56,75,37.2,70,74,Infected
-17,male,50,78,38.6,70,66,Infected
-18,female,52,68,39.6,95,74,Non Infected
-19,male,33,71,37.2,95,82,Non Infected
-20,male,40,70,38.6,95,90,Non Infected
-21,male,70,70,39.6,97,72,Non Infected
-22,male,51,72,37.2,97,72,Non Infected
-23,male,NA,68,37.2,96,67,Non Infected
-24,female,28,88,39.6,96,72,Non Infected
-25,male,37,83,37.2,96,65,Non Infected
-26,male,19,94,37.2,96,73,Non Infected
-27,male,29,78,39.6,96,94,Non Infected
-28,female,66,89,37.2,96,73,Non Infected
-29,male,46,75,37.2,96,75,Non Infected
-30,female,32,101,39.6,96,78,Non Infected
-31,male,28,86,37.2,96,68,Non Infected
-32,male,55,81,37.2,96,71,Non Infected
-33,male,68,80,39.6,96,70,Non Infected
-34,male,38,79,37.2,96,70,Non Infected
-35,male,72,80,37.2,96,72,Non Infected
-36,male,45,75,39.6,96,68,Non Infected
-37,male,42,94,37.2,96,88,Non Infected
-38,female,33,87,37.2,96,83,Non Infected
-39,female,33,76,37.2,96,94,Non Infected
-40,male,37,97,37.2,96,89,Non Infected
-41,male,69,88,36.4,91,75,Infected
-42,male,63,79,37.5,91,101,Non infected 
-43,female,62,95,37.5,91,86,Infected
-44,male,49,90,37.8,91,81,Infected
-45,male,50,95,37.8,91,80,Infected
-46,male,48,77,38,91,79,Infected
-47,male,36,68,38,91,94,Infected
-48,female,36,92,38,91,87,Infected
-49,male,61,85,37.8,91,76,Infected
-50,male,69,67,36.5,91,97,Infected
-51,male,89,87,37.2,91,88,Infected
-52,male,89,77,39.6,91,79,Infected
-53,male,66,100,37.2,91,95,Infected
-54,male,75,78,37.2,91,90,Infected
-55,female,48,68,39.6,91,95,Infected
-56,male,82,89,37.2,91,77,Infected
-57,male,66,79,37.2,91,68,Infected
-58,male,81,96,37.2,91,92,Infected
-59,female,82,86,37.2,91,85,Infected
-60,male,65,92,36.4,91,87,Infected
-61,female,80,86,37.5,91,68,Infected
-62,male,53,80,37.5,91,89,Infected
-63,male,86,89,37.8,91,79,Infected
-64,female,70,72,37.8,91,96,Infected
-65,male,84,69,38,91,86,Infected
-66,female,50,66,38,91,92,Infected
-67,male,40,67,38,91,86,Infected
-68,male,45,79,37.8,91,80,Infected
-69,female,66,65,36.5,91,89,Infected
-70,male,59,66,37.2,91,72,Infected
-71,female,23,70,37.2,91,66,Infected
-72,female,50,84,36.4,91,67,Infected
-73,male,43,68,37.5,91,66,Infected
-74,male,49,69,37.5,91,84,Infected
-75,male,42,76,37.8,91,69,Infected
-76,female,32,77,37.8,91,79,Infected
-77,male,22,79,38,91,83,Infected
-78,male,47,83,38,91,75,Infected
-79,male,52,75,38,91,74,Infected
-80,male,53,74,37.8,91,69,Infected
-81,male,46,69,36.5,91,74,Infected
-82,female,85,70,36.5,61.3,77,Infected
-83,female,69,69,36.5,61.3,70,Infected
-84,male,36,74,36.5,61.3,74,Infected
-85,male,73,76,37.5,88,87,Infected
-86,female,70,77,37.5,88,68,Infected
-87,male,81,77,37.8,98,66,Non Infected
-88,female,65,70,37.8,98,68,Non Infected
-89,male,42,74,38,93,72,Infected
-90,male,30,87,38,93,66,Infected
-91,female,29,68,38,98,72,Non Infected
-92,female,NA,66,37.8,97,68,Non Infected
-93,male,49,68,36.5,96,71,Non Infected
-94,male,23,72,37.8,92,64,Infected
-95,female,56,66,38,50,60,Infected
-96,male,39,71,38,97,87,Non Infected
-97,female,39,73,38,70,82,Infected
-98,female,34,72,37.8,70,85,Infected
-99,male,49,68,36.5,70,78,Infected
-100,male,70,71,36.5,70,69,Infected
-101,female,76,64,36.5,70,72,Infected
-102,male,72,90,39,95,75,Non Infected
-103,male,79,87,39,95,72,Non Infected
-104,male,55,92,39,95,70,Non Infected
-105,male,87,92,39,95,75,Non Infected
-106,female,66,92,36.9,92,89,Infected
-107,male,58,92,36.9,92,65,Infected
-108,male,66,89,36.9,92,63,Infected
-109,male,78,92,95,92,76,Infected
-110,male,67,86,95,92,62,Infected
-111,male,65,47,95,92,87,Infected
-112,male,58,99,95,92,78,Infected
-113,female,67,71,95,92,92,Infected
-114,female,82,68,95,92,62,Infected
-115,female,49,71,95,92,63,Infected
-116,female,2,72,38,97,84,Non Infected
-117,male,NA,68,38,97,59,Non Infected
-118,female,59,77,38,97,73,Non Infected
-119,male,57,78,38,97,63,Non Infected
-120,female,68,83,38,97,64,Non Infected
-121,male,40,80,38,97,68,Non Infected
-122,male,46,82,38,97,82,Non Infected
-123,male,56,77,38,97,66,Non Infected
-124,female,29,84,38,97,67,Non Infected
-125,male,29,89,38,97,73,Non Infected
-126,male,57,79,38,97,69,Non Infected
-127,male,30,83,38,97,77,Non Infected
-128,female,33,84,38,97,78,Non Infected
-129,female,20,87,38,97,81,Non Infected
-130,female,24,85,38,97,65,Non Infected
-131,male,28,78,38,97,80,Non Infected
-132,male,41,88,38,97,72,Non Infected
-133,male,46,91,38,97,80,Non Infected
-134,female,45,87,38,97,83,Non Infected
-135,female,9,86,38,97,72,Non Infected
-136,male,40,83,38,97,64,Non Infected
-137,male,28,77,38,84,60,Infected
-138,male,27,83,38,84,63,Infected
-139,male,15,90,38,82,66,Infected
-140,female,42,80,38,82,82,Infected
-141,male,24,84,38.6,82,95,Infected
-142,female,32,86,39.6,82,95,Infected
-143,male,34,89,37.2,82,80,Infected
-144,male,63,65,38.6,82,88,Infected
-145,male,58,77,39.6,82,84,Infected
-146,male,NA,81,37.2,82,93,Infected
-147,male,NA,72,38.6,82,94,Infected
-148,male,NA,82,39.6,82,82,Infected
-149,male,49,93,37.2,82,76,Infected
-150,male,33,113,38,82,86,Infected
-151,female,55,84,38.6,82,68,Infected
-152,female,79,86,39.6,82,79,Infected
-153,female,19,91,37.2,82,73,Infected
-154,female,58,95,38.6,82,77,Infected
-155,female,39,98,39.6,82,88,Infected
-156,female,21,79,37.2,82,93,Infected
-157,female,30,85,38.6,82,79,Infected
-158,female,64,82,39.6,82,85,Infected
-159,male,44,85,37.2,82,68,Infected
-160,female,70,118,38.6,82,66,Infected
-161,female,52,94,39.6,82,66,Infected
-162,female,32,85,37.2,82,86,Infected
-163,male,33,78,37.2,82,89,Infected
-164,male,71,105,39.6,82,82,Infected
-165,male,31,103,37.2,82,84,Infected
-166,female,48,117,37.2,82,80,Infected
-167,female,19,87,39.6,82,72,Infected
-168,female,68,82,37.2,82,81,Infected
-169,male,71,101,37.2,82,88,Infected
-170,male,35,82,39.6,82,84,Infected
-171,female,4,63,37.2,82,84,Infected
-172,male,38,65,37.2,82,61,Infected
-173,female,69,63,39.6,82,75,Infected
-174,male,50,62,37.2,82,79,Infected
-175,male,78,69,37.2,82,84,Infected
-176,male,49,68,39.6,82,85,Infected
-177,male,34,65,37.2,82,80,Infected
-178,male,NA,59,37.2,82,78,Infected
-179,female,55,59,37.2,82,80,Infected
-180,female,36,61,37.2,82,85,Infected
-181,male,65,61,36.4,82,80,Infected
-182,male,38,62,37.5,82,78,Infected
-183,female,36,63,37.5,89,92,Infected
-184,male,18,65,37.8,89,94,Infected
-185,male,50,64,37.8,89,79,Infected
-186,female,40,59,38,85,81,Infected
-187,female,63,65,38,85,82,Infected
-188,female,47,64,38,85,86,Infected
-189,female,42,65,37.8,85,86,Infected
-190,male,48,60,36.5,85,77,Infected
-191,male,39,63,37.2,85,74,Infected
-192,female,41,62,39.6,85,76,Infected
-193,male,31,60,37.2,85,79,Infected
-194,male,27,62,37.2,85,81,Infected
-195,female,28,61,39.6,85,65,Infected
-196,female,51,61,37.2,85,74,Infected
-197,male,37,64,37.2,85,82,Infected
-198,male,48,61,37.2,85,79,Infected
-199,male,31,61,37.2,85,74,Infected
-200,female,30,60,36.4,85,70,Infected
-201,male,80,58,37.5,85,83,Infected
-202,female,NA,59,37.5,85,86,Infected
-203,NA,NA,60,37.8,85,62,Infected
-204,NA,9,61,37.8,85,63,Infected
-205,NA,NA,61,38,85,89,Infected
-206,NA,NA,59,38,85,89,Infected
-207,NA,NA,59,38,85,93,Infected
-208,NA,NA,60,37.8,85,76,Infected
-209,NA,NA,61,36.5,85,78,Infected
-210,female,33,63,37.2,85,73,Infected
-211,male,63,63,37.2,85,66,Infected
-212,male,55,64,36.4,85,83,Infected
-213,male,36,60,37.5,85,79,Infected
-214,male,60,59,37.5,85,84,Infected
-215,female,60,61,37.8,85,83,Infected
-216,NA,NA,64,37.8,85,81,Infected
-217,NA,NA,60,38,85,87,Infected
-218,NA,NA,60,38,85,83,Infected
-219,NA,NA,64,38,85,85,Infected
-220,NA,NA,66,37.8,85,96,Infected
-221,NA,NA,60,36.5,85,78,Infected
-222,NA,NA,63,36.5,85,75,Infected
-223,NA,NA,58,36.5,85,95,Infected
-224,NA,NA,66,36.5,85,88,Infected
-225,NA,NA,61,37.5,85,80,Infected
-226,male,NA,60,37.5,85,84,Infected
-227,female,58,61,37.8,85,70,Infected
-228,male,31,61,37.8,85,74,Infected
-229,NA,NA,63,38,85,81,Infected
-230,male,72,61,38,85,87,Infected
-231,male,NA,62,38,85,83,Infected
-232,male,50,63,37.8,85,88,Infected
-233,NA,65,62,36.5,85,88,Infected
-234,female,35,59,37.8,85,71,Infected
-235,male,NA,61,38,70,79,Infected
-236,female,23,61,38,70,80,Infected
-237,male,35,61,38,70,62,Infected
-238,male,45,63,37.8,70,74,Infected
-239,female,35,63,36.5,70,90,Infected
-240,male,45,67,36.5,70,76,Infected
-241,male,45,63,36.5,70,87,Infected
-242,male,65,63,39,70,81,Infected
-243,female,45,61,39,70,68,Infected
-244,female,45,59,39,70,87,Infected
-245,male,55,63,39,70,73,Infected
-246,male,55,62,36.9,70,83,Infected
-247,female,35,87,36.9,70,93,Infected
-248,female,25,110,36.9,92,77,Infected
-249,female,25,57,36.9,96,72,Non Infected
-250,male,45,81,36.9,98,75,Non Infected
-251,male,45,78,36.9,98,65,Non Infected
-252,male,35,78,36.9,98,70,Non Infected
-253,female,35,78,36.9,98,67,Non Infected
-254,female,55,76,36.9,98,74,Non Infected
-255,male,55,83,36.9,98,84,Non Infected
-256,male,45,87,38,98,76,Non Infected
-257,male,25,78,38,98,79,Non Infected
-258,male,55,87,38,98,81,Non Infected
-259,male,25,80,38,98,77,Non Infected
-260,male,45,79,38,98,59,Non Infected
-261,male,55,79,38,98,69,Non Infected
-262,NA,NA,99,38,98,90,Non Infected
-263,female,85,77,38,98,66,Non Infected
-264,male,75,105,38,98,69,Non Infected
-265,male,55,89,38,98,57,Non Infected
-266,male,25,84,38,98,66,Non Infected
-267,male,75,86,38,98,77,Non Infected
-268,female,65,79,38,90,58,Infected
-269,female,55,86,38,90,68,Infected
-270,male,75,87,38,90,68,Infected
-271,male,65,80,38,90,83,Infected
-272,male,55,75,38,90,81,Infected
-273,male,35,85,38,90,70,Infected
-274,male,55,110,38,90,67,Infected
-275,female,55,105,38,90,71,Infected
-276,male,65,76,38,90,89,Infected
-277,female,65,109,38,90,82,Infected
-278,female,65,79,38,90,85,Infected
-279,male,45,76,38,90,87,Infected
-280,male,65,72,38,90,75,Infected
-281,male,35,93,38,90,79,Infected
-282,male,65,82,38,90,106,Infected
-283,male,65,94,38,90,101,Infected
-284,male,65,83,38,89,80,Infected
-285,female,85,89,38,89,77,Infected
-286,female,55,88,38,89,65,Infected
-287,male,55,85,38,89,65,Infected
-288,male,55,91,38,89,66,Infected
-289,female,45,86,38,89,84,Infected
-290,male,65,85,38,89,46,Infected
-291,male,35,88,38,89,80,Infected
-292,male,85,97,38,89,96,Infected
-293,male,25,115,38,89,92,Infected
-294,male,55,91,38,89,90,Infected
-295,male,65,94,38,89,83,Infected
-296,male,85,80,38,89,96,Infected
-297,male,75,83,38,89,84,Infected
-298,male,65,92,38,89,88,Infected
-299,male,45,96,38,89,81,Infected
-300,male,65,90,38,89,78,Infected
-301,male,65,87,37.9,89,79,Infected
-302,male,75,48,37.9,89,59,Infected
-303,female,75,95,37.9,89,54,Infected
-304,female,75,87,37.9,89,86,Infected
-305,female,55,79,37.9,89,70,Infected
-306,male,55,75,37.9,89,58,Infected
-307,male,65,84,37.9,89,62,Infected
-308,male,45,84,37.9,89,69,Infected
-309,female,75,76,37.9,89,72,Infected
-310,male,85,81,37.9,89,79,Infected
-311,NA,NA,74,37.9,89,67,Infected
-312,NA,NA,84,37.9,67,66,Infected
-313,male,85,82,37.9,67,71,Infected
-314,male,85,80,37.9,85,61,Infected
-315,female,65,84,37.9,85,65,Infected
-316,male,5,78,37.9,85,74,Infected
-317,male,15,70,37.9,85,77,Infected
-318,female,45,80,37.9,85,63,Infected
-319,male,55,78,37.9,85,62,Infected
-320,NA,5,71,38.1,85,74,Infected
-321,female,55,91,38.1,85,58,Infected
-322,female,75,91,38.1,85,76,Infected
-323,female,55,80,38.1,85,68,Infected
-324,male,65,91,38.1,85,58,Infected
-325,male,85,91,38.9,85,58,Infected
-326,male,75,95,38.9,85,68,Infected
-327,female,25,88,38.9,85,84,Infected
-328,female,25,88,38.9,85,68,Infected
-329,male,55,94,38.9,85,81,Infected
-330,male,65,90,38.9,85,94,Infected
-331,male,45,94,39,85,82,Infected
-332,female,65,99,39,85,90,Infected
-333,female,55,93,39,85,67,Infected
-334,female,75,68,39,85,78,Infected
-335,male,85,75,39,85,70,Infected
-336,male,75,72,39,85,69,Infected
-337,female,55,78,39,85,73,Infected
-338,male,65,76,39,85,71,Infected
-339,female,55,78,39,85,65,Infected
-340,female,15,80,39,85,68,Infected
-341,female,55,85,39,85,81,Infected
-342,male,65,116,39,85,75,Infected
-343,female,65,78,39,85,85,Infected
-344,female,46,78,39,85,84,Infected
-345,male,65,80,39,85,87,Infected
-346,male,65,78,39,85,94,Infected
-347,female,65,80,39,85,81,Infected
-348,male,55,105,39,85,79,Infected
-349,male,55,119,39,85,71,Infected
-350,male,75,112,39,85,61,Infected
-351,female,75,100,37.6,84,59,Infected
-352,male,75,104,37.6,84,58,Infected
-353,male,45,93,37.6,84,57,Infected
-354,male,35,82,37.6,84,56,Infected
-355,female,65,81,37.6,84,58,Infected
-356,male,25,80,37.1,96,59,Non Infected
-357,male,75,82,37.1,96,58,Non Infected
-358,female,35,81,38.9,92,55,Infected
-359,male,85,109,38.9,75,58,Infected
-360,female,45,85,38.9,75,64,Infected
-361,female,25,77,38.9,75,72,Infected
-362,female,55,84,38.9,75,69,Infected
-363,male,55,88,38.9,75,78,Infected
-364,male,65,87,38.9,75,73,Infected
-365,female,75,90,38.9,75,73,Infected
-366,male,55,86,38.9,75,57,Infected
-367,female,25,92,38.9,75,56,Infected
-368,male,55,96,38.9,75,54,Infected
-369,male,55,96,38.9,75,61,Infected
-370,male,45,80,38.9,75,76,Infected
-371,male,55,67,38.9,75,68,Infected
-372,male,35,76,38.9,75,74,Infected
-373,female,55,82,38.9,75,72,Infected
-374,female,25,76,38.9,75,62,Infected
-375,female,25,69,38.9,75,62,Infected
-376,male,65,74,38.9,75,73,Infected
-377,male,55,71,38.9,75,71,Infected
-378,female,65,71,38.9,75,67,Infected
-379,female,65,73,38.9,75,58,Infected
-380,female,55,95,38.9,75,79,Infected
-381,female,75,81,38.9,75,79,Infected
-382,female,75,79,38.9,75,76,Infected
-383,female,45,77,38.9,75,80,Infected
-384,male,55,79,38.9,75,76,Infected
-385,male,65,84,38.9,75,76,Infected
-386,male,75,74,38.9,75,63,Infected
-387,male,25,83,38.9,75,54,Infected
-388,female,25,94,38.9,75,84,Infected
-389,male,75,91,38.9,75,77,Infected
-390,NA,NA,93,38.9,75,64,Infected
-391,female,75,99,38.9,75,57,Infected
-392,female,75,97,38.9,75,59,Infected
-393,male,45,107,38.9,75,55,Infected
-394,male,65,106,38.9,75,66,Infected
-395,female,45,104,38.9,75,72,Infected
-396,male,85,99,38.9,75,63,Infected
-397,male,75,90,38.9,75,72,Infected
-398,male,75,90,38.9,75,53,Infected
-399,male,55,97,38.9,75,69,Infected
-400,male,65,88,38.9,75,62,Infected
-401,male,75,91,38.9,75,55,Infected
-402,male,75,91,38.9,75,65,Infected
-403,female,75,88,38.9,75,57,Infected
-404,male,55,88,38.9,75,55,Infected
-405,female,55,87,38.9,75,80,Infected
-406,male,55,87,38.9,75,76,Infected
-407,male,55,93,38.5,75,55,Infected
-408,female,60,77,38.5,75,74,Infected
-409,male,45,86,38.5,75,56,Infected
-410,male,55,79,38.5,75,54,Infected
-411,male,55,83,38.5,75,55,Infected
-412,male,35,87,38.5,75,61,Infected
-413,male,75,90,38.5,75,59,Infected
-414,male,65,69,38.1,75,54,Infected
-415,male,45,68,38.1,75,55,Infected
-416,male,45,67,38.1,75,59,Infected
-417,male,5,67,38.1,75,59,Infected
-418,male,35,67,38.1,55,59,Infected
-419,male,5,65,38.1,55,55,Infected
-420,male,85,70,38.1,55,55,Infected
-421,male,35,61,38.1,55,59,Infected
-422,male,85,60,38.1,55,63,Infected
-423,male,65,59,38.1,40,63,Infected
-424,female,45,58,38.1,40,52,Infected
-425,female,65,58,38.1,20,54,Infected
-426,male,65,58,38.1,20,51,Infected
-427,male,40,56,38.1,20,50,Infected
-428,female,65,54,38.1,20,53,Infected
-429,male,11,54,38.1,70,56,Infected
-430,male,2,55,38.1,80,50,Infected
-431,female,4,55,38.1,130,51,Non Infected
-432,female,36,55,38.1,45,52,Infected
-433,male,52,56,38.1,60,52,Infected
-434,female,49,58,38.1,25,53,Infected
-435,male,41,56,38.1,55,54,Infected
-436,male,63,57,38.1,55,52,Infected
-437,male,45,55,38.1,55,53,Infected
-438,male,9,57,38.1,55,54,Infected
-439,female,40,56,38.1,25,53,Infected
-440,female,37,58,38.1,25,54,Infected
-441,female,59,59,38.1,25,55,Infected
-442,female,67,60,38.1,90,55,Infected
-443,female,65,60,38.1,45,52,Infected
-444,male,31,58,38.1,45,67,Infected
-445,female,39,58,38.1,25,56,Infected
-446,male,27,59,38.1,65,54,Infected
-447,female,32,60,38.1,30,55,Infected
-448,female,83,61,38.1,25,57,Infected
-449,female,53,60,38.1,20,55,Infected
-450,male,32,62,38.1,20,55,Infected
-451,male,66,62,38.1,20,54,Infected
-452,female,53,61,38.1,40,56,Infected
-453,male,37,60,38.1,340,57,Non Infected
-454,male,36,59,38.1,80,56,Infected
-455,female,56,60,38.1,80,57,Infected
-456,male,56,60,38.1,80,58,Infected
-457,male,35,63,38.1,80,56,Infected
-458,female,56,66,38.1,80,54,Infected
-459,male,56,63,38.1,80,57,Infected
-460,male,56,57,38.1,40,64,Infected
-461,female,31,61,38.1,80,53,Infected
-462,female,37,64,38.1,80,59,Infected
-463,female,73,66,38.1,130,55,Non Infected
-464,male,31,62,38.1,70,52,Infected
-465,female,47,66,38.1,165,53,Non Infected
-466,male,38,67,38.1,35,55,Infected
-467,female,47,66,38.1,35,56,Infected
-468,female,31,65,38.1,35,54,Infected
-469,female,28,65,38.1,35,58,Infected
-470,female,48,65,38.1,35,51,Infected
-471,female,44,64,38.1,35,51,Infected
-472,NA,NA,65,38.1,35,52,Infected
-473,NA,NA,64,38.1,35,56,Infected
-474,female,32,63,38.1,20,57,Infected
-475,male,NA,62,38.1,40,56,Infected
-476,female,42,65,38.1,40,55,Infected
-477,male,0.5,57,38.1,40,56,Infected
-478,male,45,58,38.1,40,56,Infected
-479,male,41,57,38.1,40,54,Infected
-480,male,27,58,38.1,40,54,Infected
-481,male,53,58,38.1,30,55,Infected
-482,female,42,58,38.1,30,54,Infected
-483,female,39,56,38.1,30,53,Infected
-484,female,40,55,38.1,40,52,Infected
-485,male,64,56,38.1,20,56,Infected
-486,female,38,60,38.1,20,55,Infected
-487,male,53,57,38.1,20,51,Infected
-488,female,52,57,38.1,20,56,Infected
-489,male,51,56,38.1,25,56,Infected
-490,male,36,56,38.1,40,53,Infected
-491,male,71,60,38.1,35,56,Infected
-492,male,39,59,38.1,55,54,Infected
-493,male,54,63,38.1,55,53,Infected
-494,male,37,62,38.1,55,56,Infected
-495,female,2,61,38.1,30,50,Infected
-496,male,35,57,38.1,30,57,Infected
-497,male,39,58,38.1,30,56,Infected
-498,male,34,95,38.1,30,52,Infected
-499,male,46,78,38.1,30,51,Infected
-500,male,62,83,39,30,63,Infected
-501,male,48,78,39,30,52,Infected
-502,male,37,72,39,30,54,Infected
-503,male,54,61,39,30,54,Infected
-504,female,55,69,39,30,54,Infected
-505,male,30,72,39,30,51,Infected
-506,male,30,74,39,30,54,Infected
-507,male,26,73,39,30,51,Infected
-508,male,55,70,39,30,53,Infected
-509,male,61,73,39,30,53,Infected
-510,female,51,71,39,30,50,Infected
-511,male,57,68,39,30,51,Infected
-512,female,44,66,39,30,53,Infected
-513,female,54,69,39,30,53,Infected
-514,male,50,78,39,30,53,Infected
-515,female,61,77,39,30,52,Infected
-516,male,28,74,39,30,53,Infected
-517,female,56,69,39,30,54,Infected
-518,female,79,67,39,30,53,Infected
-519,male,26,66,39,30,59,Infected
-520,female,27,64,39,30,53,Infected
-521,male,25,72,39,30,53,Infected
-522,male,40,66,39,30,56,Infected
-523,male,43,60,39,30,55,Infected
-524,male,29,67,39,30,53,Infected
-525,female,71,69,39,30,52,Infected
-526,male,1,73,39,30,52,Infected
-527,male,35,72,39,30,52,Infected
-528,female,57,64,39,30,52,Infected
-529,female,35,61,39,30,54,Infected
-530,female,38,71,39,30,51,Infected
-531,male,50,72,39,30,54,Infected
-532,female,57,68,39,30,55,Infected
-533,male,54,75,39,30,55,Infected
-534,female,35,66,39,30,54,Infected
-535,male,36,70,39,30,52,Infected
-536,male,24,77,39,30,50,Infected
-537,male,32,73,39,30,50,Infected
-538,female,30,69,39,30,53,Infected
-539,male,41,65,39,30,51,Infected
-540,female,75,68,39,30,50,Infected
-541,female,58,67,39,30,58,Infected
-542,male,47,72,39,30,57,Infected
-543,male,38,73,39,30,55,Infected
-544,female,35,68,39,30,54,Infected
-545,male,55,71,39,30,52,Infected
-546,male,54,81,39,30,52,Infected
-547,male,55,83,39,30,52,Infected
-548,male,32,67,37.6,30,55,Infected
-549,male,56,76,37.6,30,56,Infected
-550,male,28,74,37.6,30,56,Infected
-551,female,62,56,37.6,30,58,Infected
-552,female,28,68,37.6,30,57,Infected
-553,female,52,75,37.6,30,54,Infected
-554,male,25,77,37.6,30,78,Infected
-555,male,48,73,37.6,30,76,Infected
-556,male,28,70,37.6,30,58,Infected
-557,female,40,80,37.6,30,52,Infected
-558,male,43,66,37.6,30,54,Infected
-559,female,42,67,37.6,30,54,Infected
-560,male,37,70,37.6,30,57,Infected
-561,female,21,76,37.6,30,61,Infected
-562,male,36,77,37.6,30,59,Infected
-563,female,41,67,37.6,30,60,Infected
-564,female,59,67,37.6,30,94,Infected
-565,male,46,73,37.6,30,57,Infected
-566,female,58,72,37.6,30,53,Infected
-567,male,28,68,37.6,30,54,Infected
-568,female,73,70,37.6,30,57,Infected
-569,male,51,68,37.3,97,88,Non Infected
-570,female,37,69,37.3,97,88,Non Infected
-571,female,30,64,37.3,97,91,Non Infected
-572,male,82,72,37.3,97,86,Non Infected
-573,female,68,73,37.3,97,83,Non Infected
-574,female,61,64,37.3,97,78,Non Infected
-575,female,11,71,38.3,70,70,Infected
-576,female,40,67,38.3,70,60,Infected
-577,male,24,69,38.3,70,58,Infected
-578,female,26,73,38.1,70,67,Infected
-579,female,48,67,38.1,70,56,Infected
-580,male,49,70,38.1,70,57,Infected
-581,female,57,74,38.1,70,58,Infected
-582,female,61,70,38.1,70,59,Infected
-583,male,77,69,38.1,70,56,Infected
-584,female,69,69,38.1,70,53,Infected
-585,female,29,68,38.1,70,55,Infected
-586,female,58,71,38.1,98,55,Non Infected
-587,female,46,60,38.1,98,55,Non Infected
-588,female,54,64,38.1,98,55,Non Infected
-589,male,28,62,38.1,98,51,Non Infected
-590,female,63,64,40,93,51,Infected
-591,female,72,62,38.5,93,49,Infected
-592,male,58,80,38.5,93,49,Infected
-593,male,76,69,37.7,95,51,Non Infected
-594,female,61,82,37.7,95,54,Non Infected
-595,male,24,89,37.7,95,52,Non Infected
-596,female,38,75,38,90,52,Infected
-597,male,57,69,38,90,52,Infected
-598,male,59,72,38,95,56,Non Infected
-599,male,75,66,39.6,80,55,Infected
-600,male,30,113,38.5,79,54,Infected
-601,female,26,102,38.5,79,61,Infected
-602,female,34,108,38.5,79,62,Infected
-603,female,59,62,38.5,79,62,Infected
-604,female,58,77,38.5,79,63,Infected
-605,male,60,77,38.5,79,69,Infected
-606,female,52,90,38.5,79,54,Infected
-607,female,59,87,38.5,79,53,Infected
-608,female,50,87,38.5,79,57,Infected
-609,male,22,88,38.5,79,52,Infected
-610,female,30,88,38.5,79,52,Infected
-611,female,56,88,38.5,79,55,Infected
-612,male,29,90,38,79,50,Infected
-613,female,48,90,38.2,87,55,Infected
-614,female,56,85,38.2,66,56,Infected
-615,female,39,83,38.2,66,55,Infected
-616,female,20,93,38.2,66,56,Infected
-617,male,49,94,36.1,66,58,Infected
-618,female,23,93,36.1,66,59,Infected
-619,male,36,92,36.1,66,57,Infected
-620,female,57,91,36.1,66,59,Infected
-621,male,55,92,37.5,66,60,Infected
-622,female,22,92,36,97,60,Non Infected
-623,female,34,91,39.1,97,58,Non Infected
-624,female,65,93,36.8,97,58,Non Infected
-625,male,64,89,38.3,88,57,Infected
-626,NA,NA,90,40,88,57,Infected
-627,NA,NA,90,37,88,57,Infected
-628,NA,NA,91,36.7,88,60,Infected
-629,NA,NA,92,36.8,88,62,Infected
-630,NA,NA,93,38.5,88,61,Infected
-631,NA,NA,92,38.5,88,69,Infected
-632,NA,NA,95,38.5,88,60,Infected
-633,NA,NA,96,39.4,88,60,Infected
-634,NA,NA,95,39.4,90,60,Infected
-635,NA,NA,96,39.4,90,55,Infected
-636,NA,NA,97,36.8,95,55,Non Infected
-637,NA,NA,101,36.8,95,61,Non Infected
-638,NA,NA,99,36.8,95,60,Non Infected
-639,NA,NA,99,36.8,95,93,Non Infected
-640,NA,NA,97,36.8,95,90,Non Infected
-641,NA,NA,96,39,95,83,Non Infected
-642,NA,NA,96,39,95,98,Non Infected
-643,NA,NA,97,39,85,75,Infected
-644,NA,NA,96,39,85,89,Infected
-645,NA,NA,95,39,85,94,Infected
-646,NA,NA,96,39,85,86,Infected
-647,NA,NA,97,38.2,92,88,Infected
-648,male,67,96,38.2,92,96,Infected
-649,male,74,96,38.2,92,83,Infected
-650,male,35,96,38.2,92,100,Infected
-651,male,62,96,38.2,92,88,Infected
-652,male,58,96,38.2,92,72,Infected
-653,male,62,97,38.2,92,50,Infected
-654,male,41,96,38.2,92,52,Infected
-655,male,36,97,38.2,92,48,Infected
-656,female,69,96,38.2,92,55,Infected
-657,male,75,96,38.5,85,50,Infected
-658,female,55,96,38.5,85,53,Infected
-659,female,55,97,38.5,85,66,Infected
-660,male,55,97,38.5,85,68,Infected
-661,female,50,97,38.5,85,64,Infected
-662,female,50,100,38.5,85,60,Infected
-663,female,70,101,38.5,85,72,Infected
-664,female,70,99,38.5,85,63,Infected
-665,male,55,96,38.5,85,70,Infected
-666,male,NA,96,38.5,85,71,Infected
-667,female,NA,96,38.5,85,72,Infected
-668,male,55,96,38.5,85,63,Infected
-669,male,45,95,38.5,85,62,Infected
-670,female,25,94,38.5,85,82,Infected
-671,male,55,93,38.5,85,80,Infected
-672,female,55,92,38.5,85,78,Infected
-673,female,45,91,38.5,85,67,Infected
-674,male,25,90,38.5,85,70,Infected
-675,male,25,90,38.5,85,61,Infected
-676,male,65,90,38.5,85,64,Infected
-677,male,55,89,38.5,85,77,Infected
-678,female,85,89,38.5,85,72,Infected
-679,male,35,88,38.5,85,71,Infected
-680,female,65,90,38.5,85,68,Infected
-681,female,60,91,38.5,85,76,Infected
-682,female,45,93,38.5,85,55,Infected
-683,female,25,96,38.5,85,84,Infected
-684,male,85,96,38.5,85,87,Infected
-685,male,55,95,38.5,85,94,Infected
-686,male,45,95,38.5,85,65,Infected
-687,female,75,97,38.5,85,66,Infected
-688,male,NA,98,38.5,85,64,Infected
-689,female,35,99,38.5,85,55,Infected
-690,male,35,100,38.5,85,49,Infected
-691,female,55,102,38.5,85,52,Infected
-692,female,61,102,38.5,85,58,Infected
-693,female,74,101,38.5,85,53,Infected
-694,male,68,103,38.5,85,54,Infected
-695,female,73,99,38.5,85,51,Infected
-696,male,NA,97,38.5,85,53,Infected
-697,female,33,97,38.5,85,61,Infected
-698,female,57,98,38.5,85,56,Infected
-699,female,73,98,38.5,85,62,Infected
-700,NA,6,98,38.5,85,55,Infected
-701,NA,60,99,38.5,85,57,Infected
-702,NA,NA,99,38.5,85,68,Infected
-703,NA,NA,100,38.5,85,75,Infected
-704,NA,NA,102,38.5,85,68,Infected
-705,NA,NA,103,38.5,85,53,Infected
-706,NA,NA,103,38.5,85,47,Infected
-707,NA,NA,102,38.5,85,53,Infected
-708,NA,NA,102,38.5,85,50,Infected
-709,NA,NA,103,38.5,85,69,Infected
-710,NA,NA,103,38.5,85,58,Infected
-711,male,50,106,38.5,85,54,Infected
-712,NA,NA,106,38.5,85,66,Infected
-713,NA,NA,106,38.5,85,74,Infected
-714,NA,NA,107,38.5,85,91,Infected
-715,male,70,106,38.5,85,51,Infected
-716,NA,NA,107,38.5,85,59,Infected
-717,NA,NA,106,38.5,85,65,Infected
-718,NA,NA,106,38.5,85,79,Infected
-719,NA,NA,107,38.5,85,103,Infected
-720,NA,NA,107,38.5,85,56,Infected
-721,NA,NA,107,38.5,85,55,Infected
-722,NA,NA,106,38.5,85,72,Infected
-723,NA,NA,106,38.5,85,49,Infected
-724,female,54,106,38.5,85,78,Infected
-725,female,35,107,38.5,85,71,Infected
-726,female,60,109,38.5,85,55,Infected
-727,female,31,111,38.5,85,59,Infected
-728,male,29,110,38.5,85,52,Infected
-729,male,NA,109,38.5,85,54,Infected
-730,female,NA,110,38.5,85,73,Infected
-731,male,8,106,38.5,65,65,Infected
-732,male,25,106,38.5,65,73,Infected
-733,male,35,106,38.5,175,55,Non Infected
-734,female,61,107,38.5,35,75,Infected
-735,male,NA,107,38.5,35,75,Infected
-736,NA,NA,109,38.5,175,71,Non Infected
-737,NA,NA,109,38.5,175,45,Non Infected
-738,male,65,109,38.5,175,45,Non Infected
-739,male,NA,108,38.5,175,51,Non Infected
-740,male,25,108,38.5,175,73,Non Infected
-741,male,57,107,38.5,175,79,Non Infected
-742,female,57,107,38.5,175,77,Non Infected
-743,female,NA,107,38.5,100,78,Non Infected
-744,NA,NA,106,38.5,85,91,Infected
-745,NA,NA,106,38.5,105,86,Non Infected
-746,NA,NA,105,38.5,105,74,Non Infected
-747,NA,NA,107,38.5,105,85,Non Infected
-748,NA,NA,108,38.5,105,77,Non Infected
-749,NA,NA,108,38.5,75,96,Infected
-750,NA,NA,109,38.5,40,75,Infected
-751,male,66,109,38.5,65,78,Infected
-752,male,28,109,38.5,45,73,Infected
-753,NA,NA,110,38.5,45,75,Infected
-754,NA,NA,110,38.5,99,64,Non Infected
-755,NA,NA,108,38.5,99,94,Non Infected
-756,NA,NA,109,38.5,99,75,Non Infected
-757,NA,NA,110,38.5,99,100,Non Infected
-758,NA,NA,110,38.5,87.3,100,Non infected
-759,male,30,111,38.5,87.3,68,Infected
-760,female,42,112,38.5,87.3,83,Infected
-761,female,16,110,38.5,87.3,105,Non Infected
-762,NA,49,110,38.5,87.3,104,Non Infected
-763,female,29,110,38.5,87.3,94,Infected
-764,female,55,109,38.5,87.3,82,Infected
-765,NA,0.25,111,38.5,87.3,87,Infected
-766,male,50,111,38.5,87.3,76,Infected
-767,male,35,110,38.5,87.3,83,Infected
-768,male,43,112,38.5,87.3,78,Infected
-769,male,53,113,38.5,87.3,69,Infected
-770,male,55,114,38.5,87.3,72,Infected
-771,female,21,114,38.5,87.3,74,Infected
-772,male,44,114,38.5,87.3,63,Infected
-773,male,65,113,38.5,87.3,84,Infected
-774,female,45,111,38.5,87.3,84,Infected
-775,female,42,112,38.5,87.3,69,Infected
-776,male,60,112,38.5,87.3,68,Infected
-777,female,60,111,38.5,87.3,82,Infected
-778,female,25,112,38.5,87.3,78,Infected
-779,male,8,112,38.5,87.3,72,Infected
-780,male,37,110,38.5,87.3,75,Infected
-781,female,37,109,38.5,87.3,68,Infected
-782,male,56,109,38.5,87.3,79,Infected
-783,female,55,111,38.5,87.3,74,Infected
-784,male,45,111,38.5,87.3,74,Infected
-785,female,25,111,38.5,87.3,67,Infected
-786,female,55,111,38.5,87.3,65,Infected
-787,male,35,111,38.5,87.3,71,Infected
-788,female,35,111,38.5,87.3,73,Infected
-789,female,35,111,38.5,87.3,63,Infected
-790,female,35,108,38.5,87.3,66,Infected
-791,female,25,110,38.5,87.3,74,Infected
-792,male,45,112,38.5,87.3,68,Infected
-793,female,65,113,38.5,87.3,91,Infected
-794,male,60,113,38.5,87.3,94,Infected
-795,female,40,112,38.5,87.3,81,Infected
-796,male,33,112,38.5,87.3,68,Infected
-797,NA,NA,112,38.5,87.3,63,Infected
-798,NA,NA,108,38.5,87.3,80,Infected
-799,NA,NA,108,38.5,87.3,77,Infected
-800,male,NA,109,38.5,87.3,72,Infected
-801,NA,NA,109,38.5,87.3,105,Non infected
-802,male,52,108,38.5,87.3,99,Non infected
-803,male,33,108,38.5,87.3,71,Infected
-804,NA,NA,111,38.5,87.3,67,Infected
-805,NA,NA,112,38.5,87.3,74,Infected
-806,NA,NA,112,38.5,87.3,64,Infected
-807,male,58,112,38.5,87.3,73,Infected
-808,female,NA,111,38.5,87.3,75,Infected
-809,female,NA,110,38.5,87.3,70,Infected
-810,male,49,109,38.5,87.3,80,Infected
-811,NA,NA,110,38.5,87.3,73,Infected
-812,male,25,111,38.5,87.3,79,Infected
-813,male,60,111,38.5,87.3,69,Infected
-814,female,24,108,38.5,87.3,84,Infected
-815,male,32,109,38.5,87.3,81,Infected
-816,male,47,109,38.5,87.3,72,Infected
-817,female,46,111,38.5,87.3,77,Infected
-818,male,41,114,38.5,87.3,76,Infected
-819,NA,NA,114,38.5,87.3,86,Infected
-820,NA,NA,113,38.5,87.3,87,Infected
-821,NA,NA,114,38.5,87.3,83,Infected
-822,NA,NA,114,38.5,87.3,80,Infected
-823,NA,NA,115,38.5,87.3,93,Infected
-824,NA,NA,112,38.5,87.3,83,Infected
-825,NA,NA,112,38.5,87.3,77,Infected
-826,NA,NA,112,38.5,87.3,91,Infected
-827,NA,NA,112,38.5,30,79,Infected
-828,NA,NA,112,38.5,30,77,Infected
-829,NA,NA,111,38.5,30,75,Infected
-830,NA,NA,110,38.5,30,87,Infected
-831,NA,NA,112,38.5,30,88,Infected
-832,NA,NA,114,38.5,30,82,Infected
-833,NA,NA,112,38.5,30,77,Infected
-834,NA,NA,112,38.5,30,76,Infected
-835,NA,NA,111,38.5,30,75,Infected
-836,male,NA,112,38.5,30,77,Infected
-837,female,NA,114,38.5,30,71,Infected
-838,female,NA,114,38.5,30,78,Infected
-839,male,NA,113,38.5,30,82,Infected
-840,male,NA,113,38.5,30,79,Infected
-841,NA,NA,112,38.5,30,89,Infected
-842,male,NA,112,38.5,30,80,Infected
-843,male,31,112,38.5,30,74,Infected
-844,male,NA,111,38.5,30,81,Infected
-845,male,NA,112,38.5,30,82,Infected
-846,male,NA,113,38.5,30,87,Infected
-847,male,NA,110,38.5,30,75,Infected
-848,male,32,110,38.5,30,80,Infected
-849,female,NA,110,38.5,30,69,Infected
-850,female,73,111,38.5,30,63,Infected
-851,female,36,111,38.5,30,70,Infected
-852,male,41,110,38.5,30,74,Infected
-853,male,8,113,38.5,30,82,Infected
-854,NA,NA,113,38.5,30,77,Infected
-855,NA,NA,113,38.5,30,79,Infected
-856,NA,NA,113,38.5,30,85,Infected
-857,NA,NA,112,38.5,30,76,Infected
-858,male,37,109,38.5,30,59,Infected
-859,NA,34,109,38.5,30,74,Infected
-860,NA,39,110,38.5,30,75,Infected
-861,male,70,113,38.5,30,78,Infected
-862,female,64,113,38.5,30,71,Infected
-863,NA,NA,113,38.5,30,67,Infected
-864,NA,NA,112,38.5,30,83,Infected
-865,NA,NA,112,38.5,30,74,Infected
-866,NA,NA,111,38.5,30,87,Infected
-867,NA,NA,111,38.5,30,86,Infected
-868,NA,NA,111,38.5,30,78,Infected
-869,NA,NA,112,38.5,30,66,Infected
-870,NA,NA,113,38.5,30,65,Infected
-871,male,39,114,38.5,30,89,Infected
-872,male,56,115,38.5,30,72,Infected
-873,female,62,115,38.5,30,72,Infected
-874,female,62,114,38.5,30,63,Infected
-875,male,63,111,38.5,30,86,Infected
-876,male,47,111,38.5,30,66,Infected
-877,female,68,110,38.5,30,64,Infected
-878,male,64,109,38.5,30,64,Infected
-879,female,73,109,38.5,30,64,Infected
-880,male,72,110,38.5,30,69,Infected
-881,female,37,111,38.5,30,71,Infected
-882,male,75,113,38.5,30,77,Infected
-883,male,39,111,38.5,30,89,Infected
-884,male,80,111,38.5,30,82,Infected
-885,female,72,110,38.5,30,87,Infected
-886,female,64,112,38.5,30,74,Infected
-887,male,60,112,38.5,30,83,Infected
-888,male,25,109,38.5,30,87,Infected
-889,female,56,111,38.5,30,88,Infected
-890,female,28,110,38.5,30,90,Infected
-891,male,56,111,38.5,30,92,Infected
-892,female,55,111,38.5,30,78,Infected
-893,female,63,110,38.5,30,82,Infected
-894,female,65,110,38.5,30,73,Infected
-895,male,58,109,38.5,30,83,Infected
-896,male,42,110,38.5,30,82,Infected
-897,male,24,111,38.5,30,86,Infected
-898,male,70,111,38.5,30,76,Infected
-899,male,68,112,38.5,30,87,Infected
-900,female,57,112,38.5,30,74,Infected
-901,female,91,116,38.5,30,70,Infected
-902,female,51,115,38.5,30,83,Infected
-903,female,25,115,38.5,30,80,Infected
-904,male,22,115,38.5,30,78,Infected
-905,female,50,115,38.5,30,75,Infected
-906,male,23,115,38.5,30,91,Infected
-907,female,55,115,38.5,30,88,Infected
-908,male,52,114,38.5,30,83,Infected
-909,male,69,113,38.5,30,75,Infected
-910,female,63,113,38.5,30,89,Infected
-911,female,86,114,38.5,30,78,Infected
-912,female,62,113,38.5,30,88,Infected
-913,male,59,113,38.5,30,76,Infected
-914,female,60,114,38.5,30,82,Infected
-915,male,71,115,38.5,30,66,Infected
-916,male,37,115,38.5,30,66,Infected
-917,male,66,113,38.5,30,81,Infected
-918,female,37,113,38.5,30,97,Infected
-919,male,75,113,38.5,30,75,Infected
-920,male,51,113,38.5,30,69,Infected
-921,male,43,112,38.5,30,88,Infected
-922,female,67,111,38.5,30,82,Infected
-923,male,37,111,38.5,30,72,Infected
-924,female,41,112,38.5,30,85,Infected
-925,male,70,112,38.5,30,87,Infected
-926,female,NA,115,38.5,30,84,Infected
-927,male,54,116,38.5,30,70,Infected
-928,male,69,116,38.5,30,66,Infected
-929,male,45,114,38.5,30,91,Infected
-930,female,46,114,38.5,30,79,Infected
-931,female,32,115,38.5,30,86,Infected
-932,male,58,114,38.5,30,96,Infected
-933,female,83,114,38.5,30,104,Non infected
-934,female,83,114,38.5,30,68,Infected
-935,male,68,115,37.9,30,71,Infected
-936,female,70,116,39.1,30,82,Infected
-937,male,75,117,39.1,30,85,Infected
-938,female,38,120,39.1,30,78,Infected
-939,male,58,120,39.1,30,85,Infected
-940,male,48,116,38,30,82,Infected
-941,female,96,115,38,30,68,Infected
-942,male,68,114,38,30,103,Non infected
-943,male,62,112,38,30,78,Infected
-944,female,80,112,38,30,73,Infected
-945,female,76,112,38,30,82,Infected
-946,male,35,114,38,30,89,Infected
-947,female,57,112,38,30,82,Infected
-948,female,68,112,38,30,87,Infected
-949,female,59,114,38,30,76,Infected
-950,female,58,114,38,30,85,Infected
-951,male,57,116,38,30,84,Infected
-952,female,57,116,38,30,82,Infected
-953,male,33,117,38,30,79,Infected
-954,female,55,115,38,30,77,Infected
-955,male,24,115,38,30,85,Infected
-956,female,60,115,38,30,89,Infected
-957,female,49,116,38,30,60,Infected
-958,female,21,117,38,30,67,Infected
-959,male,16,118,38,30,86,Infected
-960,male,80,116,38,30,70,Infected
-961,female,29,113,38,30,59,Infected
-962,male,26,113,38,30,61,Infected
-963,female,70,114,38,30,51,Infected
-964,female,89,114,38,30,44,Infected
-965,male,NA,113,38,30,49,Infected
-966,female,NA,113,38,30,105,Non infected
-967,male,35,114,38,30,86,Infected
-968,female,NA,113,38,30,81,Infected
-969,male,NA,113,38,30,77,Infected
-970,NA,NA,113,38,30,93,Infected
-971,NA,NA,111,38,30,93,Infected
-972,male,53,112,38,30,77,Infected
-973,NA,NA,113,38,30,98,Infected
-974,male,NA,113,38,30,95,Infected
-975,male,NA,115,38,30,93,Infected
-976,male,NA,117,38,30,107,Non infected
-977,female,NA,113,38,30,105,Non infected
-978,female,NA,112,38,30,104,Non infected
-979,NA,NA,113,38,30,94,Infected
-980,NA,NA,113,38,30,63,Infected
-981,NA,NA,116,38,30,65,Infected
-982,NA,NA,117,38,30,68,Infected
-983,NA,NA,117,38,30,71,Infected
-984,NA,NA,115,38,30,74,Infected
-985,NA,NA,113,38,30,63,Infected
-986,NA,NA,113,38,30,52,Infected
-987,NA,NA,114,38,30,70,Infected
-988,female,NA,115,38,30,82,Infected
-989,male,NA,117,38,30,58,Infected
-990,female,NA,117,38,30,72,Infected
-991,NA,NA,115,38,30,64,Infected
-992,NA,NA,116,38,30,79,Infected
-993,male,44,117,38,30,79,Infected
-994,female,38,118,38,30,61,Infected
-995,female,60,116,38,30,66,Infected
-996,female,32,115,38,30,75,Infected
-997,male,NA,114,38,30,70,Infected
-998,male,NA,114,38,30,59,Infected
-999,male,69,115,38,30,65,Infected
-1000,female,NA,114,38,30,64,Infected
-1001,female,36,114,38,30,60,Infected
-1002,NA,24,115,38,30,87,Infected
-1003,male,31,113,38,30,67,Infected
-1004,NA,NA,113,38,30,77,Infected
-1005,NA,NA,112,38,30,62,Infected
-1006,NA,NA,111,38,30,99,Infected
-1007,male,22,111,38,30,71,Infected
-1008,male,62,112,38,30,63,Infected
-1009,female,NA,112,38,30,65,Infected
-1010,male,44,115,38,30,79,Infected
-1011,NA,77,114,38,30,73,Infected
-1012,NA,50,114,38,30,68,Infected
-1013,female,22,114,38,30,89,Infected
-1014,male,30,114,38,30,75,Infected
-1015,NA,18,115,38,30,70,Infected
-1016,male,NA,113,38,30,70,Infected
-1017,male,NA,112,38,30,60,Infected
-1018,male,NA,112,38,30,69,Infected
-1019,male,NA,112,38,30,70,Infected
-1020,female,NA,109,38,30,87,Infected
-1021,male,NA,110,38,30,66,Infected
-1022,male,28,109,38,30,80,Infected
-1023,male,42,109,38,30,78,Infected
-1024,male,53,110,38,30,73,Infected
-1025,NA,81,109,38,30,73,Infected
-1026,male,55,109,38,30,80,Infected
-1027,female,25,111,38,30,81,Infected
-1028,male,58,110,38,30,72,Infected
-1029,NA,66,110,38,30,66,Infected
-1030,female,27,109,38,30,64,Infected
-1031,female,25,109,38,30,60,Infected
-1032,NA,NA,108,38,30,74,Infected
-1033,NA,NA,110,38,30,68,Infected
-1034,NA,NA,110,38,30,75,Infected
-1035,NA,NA,112,38,30,83,Infected
-1036,NA,NA,112,38,30,78,Infected
-1037,NA,NA,112,38,30,61,Infected
-1038,NA,NA,113,38,30,79,Infected
-1039,NA,NA,113,38,30,78,Infected
-1040,NA,NA,114,38,30,79,Infected
-1041,NA,NA,113,38,30,81,Infected
-1042,NA,NA,113,38,30,89,Infected
-1043,NA,NA,111,38,30,70,Infected
-1044,NA,NA,110,38,30,74,Infected
-1045,NA,NA,109,38,30,74,Infected
-1046,NA,NA,109,38,30,78,Infected
-1047,NA,NA,109,38,30,74,Infected
-1048,NA,NA,108,38,30,71,Infected
-1049,NA,NA,108,38,30,66,Infected
-1050,NA,NA,108,38,30,54,Infected
-1051,NA,NA,108,38,30,58,Infected
-1052,female,NA,108,38,30,57,Infected
-1053,female,45,107,38,30,68,Infected
-1054,NA,NA,108,38,30,62,Infected
-1055,NA,NA,108,38,30,66,Infected
-1056,NA,NA,108,38,30,56,Infected
-1057,NA,NA,109,38,30,57,Infected
-1058,NA,NA,110,38,30,60,Infected
-1059,NA,NA,110,38,30,57,Infected
-1060,NA,NA,110,38,30,60,Infected
-1061,NA,NA,110,38,30,97,Infected
-1062,female,NA,110,38,30,80,Infected
-1063,NA,NA,110,38,30,93,Infected
-1064,NA,NA,110,38,30,83,Infected
-1065,male,NA,111,38,30,81,Infected
-1066,female,NA,114,38,30,78,Infected
-1067,female,NA,117,38,30,87,Infected
-1068,female,NA,116,38,30,96,Infected
-1069,female,NA,115,38,30,83,Infected
-1070,female,NA,118,38,30,86,Infected
-1071,female,NA,114,38,30,83,Infected
-1072,female,NA,114,38,30,80,Infected
-1073,female,NA,113,38,30,69,Infected
-1074,female,NA,113,38,30,73,Infected
-1075,male,NA,112,38,30,78,Infected
-1076,male,NA,112,38,30,83,Infected
-1077,male,NA,112,38,30,109,Non infected
-1078,female,NA,111,38,30,99,Non infected
-1079,female,NA,111,38,30,80,Infected
-1080,NA,24,109,38,30,84,Infected
-1081,NA,24,110,38,30,72,Infected
-1082,NA,35,110,38,30,74,Infected
-1083,male,NA,110,38,30,68,Infected
-1084,male,NA,110,38,30,67,Infected
-1085,male,70,110,38,30,70,Infected
diff --git a/Jupyter-notebook/Machine learning/Health prediction model/dataset/dataset.csv b/Jupyter-notebook/Machine learning/Health prediction model/dataset/dataset.csv
deleted file mode 100644
index 069adcd..0000000
--- a/Jupyter-notebook/Machine learning/Health prediction model/dataset/dataset.csv
+++ /dev/null
@@ -1,1086 +0,0 @@
-id,gender,age,heart_rate,temperature,SpO2_saturation,bpm,Health_status
-1,male,66,70,38.6,88,75,Infected
-2,female,56,74,39.6,88,70,Infected
-3,male,46,82,37.2,98,83,Non Infected
-4,female,60,90,38.6,98,75,Non Infected
-5,male,58,72,39.6,93,78,Infected
-6,female,44,72,37.2,93,69,Infected
-7,male,34,67,38.6,98,73,Non Infected
-8,male,37,72,39.6,97,73,Non Infected
-9,male,39,65,37.2,96,84,Non Infected
-10,male,56,76,38,92,68,Infected
-11,female,18,73,38.6,50,73,Infected
-12,female,32,64,39.6,97,79,Non Infected
-13,male,37,63,37.2,70,81,Infected
-14,male,51,94,38.6,70,69,Infected
-15,male,57,73,39.6,70,72,Infected
-16,male,56,75,37.2,70,74,Infected
-17,male,50,78,38.6,70,66,Infected
-18,female,52,68,39.6,95,74,Non Infected
-19,male,33,71,37.2,95,82,Non Infected
-20,male,40,70,38.6,95,90,Non Infected
-21,male,70,70,39.6,97,72,Non Infected
-22,male,51,72,37.2,97,72,Non Infected
-23,male,NA,68,37.2,96,67,Non Infected
-24,female,28,88,39.6,96,72,Non Infected
-25,male,37,83,37.2,96,65,Non Infected
-26,male,19,94,37.2,96,73,Non Infected
-27,male,29,78,39.6,96,94,Non Infected
-28,female,66,89,37.2,96,73,Non Infected
-29,male,46,75,37.2,96,75,Non Infected
-30,female,32,101,39.6,96,78,Non Infected
-31,male,28,86,37.2,96,68,Non Infected
-32,male,55,81,37.2,96,71,Non Infected
-33,male,68,80,39.6,96,70,Non Infected
-34,male,38,79,37.2,96,70,Non Infected
-35,male,72,80,37.2,96,72,Non Infected
-36,male,45,75,39.6,96,68,Non Infected
-37,male,42,94,37.2,96,88,Non Infected
-38,female,33,87,37.2,96,83,Non Infected
-39,female,33,76,37.2,96,94,Non Infected
-40,male,37,97,37.2,96,89,Non Infected
-41,male,69,88,36.4,91,75,Infected
-42,male,63,79,37.5,91,101,Non infected 
-43,female,62,95,37.5,91,86,Infected
-44,male,49,90,37.8,91,81,Infected
-45,male,50,95,37.8,91,80,Infected
-46,male,48,77,38,91,79,Infected
-47,male,36,68,38,91,94,Infected
-48,female,36,92,38,91,87,Infected
-49,male,61,85,37.8,91,76,Infected
-50,male,69,67,36.5,91,97,Infected
-51,male,89,87,37.2,91,88,Infected
-52,male,89,77,39.6,91,79,Infected
-53,male,66,100,37.2,91,95,Infected
-54,male,75,78,37.2,91,90,Infected
-55,female,48,68,39.6,91,95,Infected
-56,male,82,89,37.2,91,77,Infected
-57,male,66,79,37.2,91,68,Infected
-58,male,81,96,37.2,91,92,Infected
-59,female,82,86,37.2,91,85,Infected
-60,male,65,92,36.4,91,87,Infected
-61,female,80,86,37.5,91,68,Infected
-62,male,53,80,37.5,91,89,Infected
-63,male,86,89,37.8,91,79,Infected
-64,female,70,72,37.8,91,96,Infected
-65,male,84,69,38,91,86,Infected
-66,female,50,66,38,91,92,Infected
-67,male,40,67,38,91,86,Infected
-68,male,45,79,37.8,91,80,Infected
-69,female,66,65,36.5,91,89,Infected
-70,male,59,66,37.2,91,72,Infected
-71,female,23,70,37.2,91,66,Infected
-72,female,50,84,36.4,91,67,Infected
-73,male,43,68,37.5,91,66,Infected
-74,male,49,69,37.5,91,84,Infected
-75,male,42,76,37.8,91,69,Infected
-76,female,32,77,37.8,91,79,Infected
-77,male,22,79,38,91,83,Infected
-78,male,47,83,38,91,75,Infected
-79,male,52,75,38,91,74,Infected
-80,male,53,74,37.8,91,69,Infected
-81,male,46,69,36.5,91,74,Infected
-82,female,85,70,36.5,61.3,77,Infected
-83,female,69,69,36.5,61.3,70,Infected
-84,male,36,74,36.5,61.3,74,Infected
-85,male,73,76,37.5,88,87,Infected
-86,female,70,77,37.5,88,68,Infected
-87,male,81,77,37.8,98,66,Non Infected
-88,female,65,70,37.8,98,68,Non Infected
-89,male,42,74,38,93,72,Infected
-90,male,30,87,38,93,66,Infected
-91,female,29,68,38,98,72,Non Infected
-92,female,NA,66,37.8,97,68,Non Infected
-93,male,49,68,36.5,96,71,Non Infected
-94,male,23,72,37.8,92,64,Infected
-95,female,56,66,38,50,60,Infected
-96,male,39,71,38,97,87,Non Infected
-97,female,39,73,38,70,82,Infected
-98,female,34,72,37.8,70,85,Infected
-99,male,49,68,36.5,70,78,Infected
-100,male,70,71,36.5,70,69,Infected
-101,female,76,64,36.5,70,72,Infected
-102,male,72,90,39,95,75,Non Infected
-103,male,79,87,39,95,72,Non Infected
-104,male,55,92,39,95,70,Non Infected
-105,male,87,92,39,95,75,Non Infected
-106,female,66,92,36.9,92,89,Infected
-107,male,58,92,36.9,92,65,Infected
-108,male,66,89,36.9,92,63,Infected
-109,male,78,92,95,92,76,Infected
-110,male,67,86,95,92,62,Infected
-111,male,65,47,95,92,87,Infected
-112,male,58,99,95,92,78,Infected
-113,female,67,71,95,92,92,Infected
-114,female,82,68,95,92,62,Infected
-115,female,49,71,95,92,63,Infected
-116,female,2,72,38,97,84,Non Infected
-117,male,NA,68,38,97,59,Non Infected
-118,female,59,77,38,97,73,Non Infected
-119,male,57,78,38,97,63,Non Infected
-120,female,68,83,38,97,64,Non Infected
-121,male,40,80,38,97,68,Non Infected
-122,male,46,82,38,97,82,Non Infected
-123,male,56,77,38,97,66,Non Infected
-124,female,29,84,38,97,67,Non Infected
-125,male,29,89,38,97,73,Non Infected
-126,male,57,79,38,97,69,Non Infected
-127,male,30,83,38,97,77,Non Infected
-128,female,33,84,38,97,78,Non Infected
-129,female,20,87,38,97,81,Non Infected
-130,female,24,85,38,97,65,Non Infected
-131,male,28,78,38,97,80,Non Infected
-132,male,41,88,38,97,72,Non Infected
-133,male,46,91,38,97,80,Non Infected
-134,female,45,87,38,97,83,Non Infected
-135,female,9,86,38,97,72,Non Infected
-136,male,40,83,38,97,64,Non Infected
-137,male,28,77,38,84,60,Infected
-138,male,27,83,38,84,63,Infected
-139,male,15,90,38,82,66,Infected
-140,female,42,80,38,82,82,Infected
-141,male,24,84,38.6,82,95,Infected
-142,female,32,86,39.6,82,95,Infected
-143,male,34,89,37.2,82,80,Infected
-144,male,63,65,38.6,82,88,Infected
-145,male,58,77,39.6,82,84,Infected
-146,male,NA,81,37.2,82,93,Infected
-147,male,NA,72,38.6,82,94,Infected
-148,male,NA,82,39.6,82,82,Infected
-149,male,49,93,37.2,82,76,Infected
-150,male,33,113,38,82,86,Infected
-151,female,55,84,38.6,82,68,Infected
-152,female,79,86,39.6,82,79,Infected
-153,female,19,91,37.2,82,73,Infected
-154,female,58,95,38.6,82,77,Infected
-155,female,39,98,39.6,82,88,Infected
-156,female,21,79,37.2,82,93,Infected
-157,female,30,85,38.6,82,79,Infected
-158,female,64,82,39.6,82,85,Infected
-159,male,44,85,37.2,82,68,Infected
-160,female,70,118,38.6,82,66,Infected
-161,female,52,94,39.6,82,66,Infected
-162,female,32,85,37.2,82,86,Infected
-163,male,33,78,37.2,82,89,Infected
-164,male,71,105,39.6,82,82,Infected
-165,male,31,103,37.2,82,84,Infected
-166,female,48,117,37.2,82,80,Infected
-167,female,19,87,39.6,82,72,Infected
-168,female,68,82,37.2,82,81,Infected
-169,male,71,101,37.2,82,88,Infected
-170,male,35,82,39.6,82,84,Infected
-171,female,4,63,37.2,82,84,Infected
-172,male,38,65,37.2,82,61,Infected
-173,female,69,63,39.6,82,75,Infected
-174,male,50,62,37.2,82,79,Infected
-175,male,78,69,37.2,82,84,Infected
-176,male,49,68,39.6,82,85,Infected
-177,male,34,65,37.2,82,80,Infected
-178,male,NA,59,37.2,82,78,Infected
-179,female,55,59,37.2,82,80,Infected
-180,female,36,61,37.2,82,85,Infected
-181,male,65,61,36.4,82,80,Infected
-182,male,38,62,37.5,82,78,Infected
-183,female,36,63,37.5,89,92,Infected
-184,male,18,65,37.8,89,94,Infected
-185,male,50,64,37.8,89,79,Infected
-186,female,40,59,38,85,81,Infected
-187,female,63,65,38,85,82,Infected
-188,female,47,64,38,85,86,Infected
-189,female,42,65,37.8,85,86,Infected
-190,male,48,60,36.5,85,77,Infected
-191,male,39,63,37.2,85,74,Infected
-192,female,41,62,39.6,85,76,Infected
-193,male,31,60,37.2,85,79,Infected
-194,male,27,62,37.2,85,81,Infected
-195,female,28,61,39.6,85,65,Infected
-196,female,51,61,37.2,85,74,Infected
-197,male,37,64,37.2,85,82,Infected
-198,male,48,61,37.2,85,79,Infected
-199,male,31,61,37.2,85,74,Infected
-200,female,30,60,36.4,85,70,Infected
-201,male,80,58,37.5,85,83,Infected
-202,female,NA,59,37.5,85,86,Infected
-203,NA,NA,60,37.8,85,62,Infected
-204,NA,9,61,37.8,85,63,Infected
-205,NA,NA,61,38,85,89,Infected
-206,NA,NA,59,38,85,89,Infected
-207,NA,NA,59,38,85,93,Infected
-208,NA,NA,60,37.8,85,76,Infected
-209,NA,NA,61,36.5,85,78,Infected
-210,female,33,63,37.2,85,73,Infected
-211,male,63,63,37.2,85,66,Infected
-212,male,55,64,36.4,85,83,Infected
-213,male,36,60,37.5,85,79,Infected
-214,male,60,59,37.5,85,84,Infected
-215,female,60,61,37.8,85,83,Infected
-216,NA,NA,64,37.8,85,81,Infected
-217,NA,NA,60,38,85,87,Infected
-218,NA,NA,60,38,85,83,Infected
-219,NA,NA,64,38,85,85,Infected
-220,NA,NA,66,37.8,85,96,Infected
-221,NA,NA,60,36.5,85,78,Infected
-222,NA,NA,63,36.5,85,75,Infected
-223,NA,NA,58,36.5,85,95,Infected
-224,NA,NA,66,36.5,85,88,Infected
-225,NA,NA,61,37.5,85,80,Infected
-226,male,NA,60,37.5,85,84,Infected
-227,female,58,61,37.8,85,70,Infected
-228,male,31,61,37.8,85,74,Infected
-229,NA,NA,63,38,85,81,Infected
-230,male,72,61,38,85,87,Infected
-231,male,NA,62,38,85,83,Infected
-232,male,50,63,37.8,85,88,Infected
-233,NA,65,62,36.5,85,88,Infected
-234,female,35,59,37.8,85,71,Infected
-235,male,NA,61,38,70,79,Infected
-236,female,23,61,38,70,80,Infected
-237,male,35,61,38,70,62,Infected
-238,male,45,63,37.8,70,74,Infected
-239,female,35,63,36.5,70,90,Infected
-240,male,45,67,36.5,70,76,Infected
-241,male,45,63,36.5,70,87,Infected
-242,male,65,63,39,70,81,Infected
-243,female,45,61,39,70,68,Infected
-244,female,45,59,39,70,87,Infected
-245,male,55,63,39,70,73,Infected
-246,male,55,62,36.9,70,83,Infected
-247,female,35,87,36.9,70,93,Infected
-248,female,25,110,36.9,92,77,Infected
-249,female,25,57,36.9,96,72,Non Infected
-250,male,45,81,36.9,98,75,Non Infected
-251,male,45,78,36.9,98,65,Non Infected
-252,male,35,78,36.9,98,70,Non Infected
-253,female,35,78,36.9,98,67,Non Infected
-254,female,55,76,36.9,98,74,Non Infected
-255,male,55,83,36.9,98,84,Non Infected
-256,male,45,87,38,98,76,Non Infected
-257,male,25,78,38,98,79,Non Infected
-258,male,55,87,38,98,81,Non Infected
-259,male,25,80,38,98,77,Non Infected
-260,male,45,79,38,98,59,Non Infected
-261,male,55,79,38,98,69,Non Infected
-262,NA,NA,99,38,98,90,Non Infected
-263,female,85,77,38,98,66,Non Infected
-264,male,75,105,38,98,69,Non Infected
-265,male,55,89,38,98,57,Non Infected
-266,male,25,84,38,98,66,Non Infected
-267,male,75,86,38,98,77,Non Infected
-268,female,65,79,38,90,58,Infected
-269,female,55,86,38,90,68,Infected
-270,male,75,87,38,90,68,Infected
-271,male,65,80,38,90,83,Infected
-272,male,55,75,38,90,81,Infected
-273,male,35,85,38,90,70,Infected
-274,male,55,110,38,90,67,Infected
-275,female,55,105,38,90,71,Infected
-276,male,65,76,38,90,89,Infected
-277,female,65,109,38,90,82,Infected
-278,female,65,79,38,90,85,Infected
-279,male,45,76,38,90,87,Infected
-280,male,65,72,38,90,75,Infected
-281,male,35,93,38,90,79,Infected
-282,male,65,82,38,90,106,Infected
-283,male,65,94,38,90,101,Infected
-284,male,65,83,38,89,80,Infected
-285,female,85,89,38,89,77,Infected
-286,female,55,88,38,89,65,Infected
-287,male,55,85,38,89,65,Infected
-288,male,55,91,38,89,66,Infected
-289,female,45,86,38,89,84,Infected
-290,male,65,85,38,89,46,Infected
-291,male,35,88,38,89,80,Infected
-292,male,85,97,38,89,96,Infected
-293,male,25,115,38,89,92,Infected
-294,male,55,91,38,89,90,Infected
-295,male,65,94,38,89,83,Infected
-296,male,85,80,38,89,96,Infected
-297,male,75,83,38,89,84,Infected
-298,male,65,92,38,89,88,Infected
-299,male,45,96,38,89,81,Infected
-300,male,65,90,38,89,78,Infected
-301,male,65,87,37.9,89,79,Infected
-302,male,75,48,37.9,89,59,Infected
-303,female,75,95,37.9,89,54,Infected
-304,female,75,87,37.9,89,86,Infected
-305,female,55,79,37.9,89,70,Infected
-306,male,55,75,37.9,89,58,Infected
-307,male,65,84,37.9,89,62,Infected
-308,male,45,84,37.9,89,69,Infected
-309,female,75,76,37.9,89,72,Infected
-310,male,85,81,37.9,89,79,Infected
-311,NA,NA,74,37.9,89,67,Infected
-312,NA,NA,84,37.9,67,66,Infected
-313,male,85,82,37.9,67,71,Infected
-314,male,85,80,37.9,85,61,Infected
-315,female,65,84,37.9,85,65,Infected
-316,male,5,78,37.9,85,74,Infected
-317,male,15,70,37.9,85,77,Infected
-318,female,45,80,37.9,85,63,Infected
-319,male,55,78,37.9,85,62,Infected
-320,NA,5,71,38.1,85,74,Infected
-321,female,55,91,38.1,85,58,Infected
-322,female,75,91,38.1,85,76,Infected
-323,female,55,80,38.1,85,68,Infected
-324,male,65,91,38.1,85,58,Infected
-325,male,85,91,38.9,85,58,Infected
-326,male,75,95,38.9,85,68,Infected
-327,female,25,88,38.9,85,84,Infected
-328,female,25,88,38.9,85,68,Infected
-329,male,55,94,38.9,85,81,Infected
-330,male,65,90,38.9,85,94,Infected
-331,male,45,94,39,85,82,Infected
-332,female,65,99,39,85,90,Infected
-333,female,55,93,39,85,67,Infected
-334,female,75,68,39,85,78,Infected
-335,male,85,75,39,85,70,Infected
-336,male,75,72,39,85,69,Infected
-337,female,55,78,39,85,73,Infected
-338,male,65,76,39,85,71,Infected
-339,female,55,78,39,85,65,Infected
-340,female,15,80,39,85,68,Infected
-341,female,55,85,39,85,81,Infected
-342,male,65,116,39,85,75,Infected
-343,female,65,78,39,85,85,Infected
-344,female,46,78,39,85,84,Infected
-345,male,65,80,39,85,87,Infected
-346,male,65,78,39,85,94,Infected
-347,female,65,80,39,85,81,Infected
-348,male,55,105,39,85,79,Infected
-349,male,55,119,39,85,71,Infected
-350,male,75,112,39,85,61,Infected
-351,female,75,100,37.6,84,59,Infected
-352,male,75,104,37.6,84,58,Infected
-353,male,45,93,37.6,84,57,Infected
-354,male,35,82,37.6,84,56,Infected
-355,female,65,81,37.6,84,58,Infected
-356,male,25,80,37.1,96,59,Non Infected
-357,male,75,82,37.1,96,58,Non Infected
-358,female,35,81,38.9,92,55,Infected
-359,male,85,109,38.9,75,58,Infected
-360,female,45,85,38.9,75,64,Infected
-361,female,25,77,38.9,75,72,Infected
-362,female,55,84,38.9,75,69,Infected
-363,male,55,88,38.9,75,78,Infected
-364,male,65,87,38.9,75,73,Infected
-365,female,75,90,38.9,75,73,Infected
-366,male,55,86,38.9,75,57,Infected
-367,female,25,92,38.9,75,56,Infected
-368,male,55,96,38.9,75,54,Infected
-369,male,55,96,38.9,75,61,Infected
-370,male,45,80,38.9,75,76,Infected
-371,male,55,67,38.9,75,68,Infected
-372,male,35,76,38.9,75,74,Infected
-373,female,55,82,38.9,75,72,Infected
-374,female,25,76,38.9,75,62,Infected
-375,female,25,69,38.9,75,62,Infected
-376,male,65,74,38.9,75,73,Infected
-377,male,55,71,38.9,75,71,Infected
-378,female,65,71,38.9,75,67,Infected
-379,female,65,73,38.9,75,58,Infected
-380,female,55,95,38.9,75,79,Infected
-381,female,75,81,38.9,75,79,Infected
-382,female,75,79,38.9,75,76,Infected
-383,female,45,77,38.9,75,80,Infected
-384,male,55,79,38.9,75,76,Infected
-385,male,65,84,38.9,75,76,Infected
-386,male,75,74,38.9,75,63,Infected
-387,male,25,83,38.9,75,54,Infected
-388,female,25,94,38.9,75,84,Infected
-389,male,75,91,38.9,75,77,Infected
-390,NA,NA,93,38.9,75,64,Infected
-391,female,75,99,38.9,75,57,Infected
-392,female,75,97,38.9,75,59,Infected
-393,male,45,107,38.9,75,55,Infected
-394,male,65,106,38.9,75,66,Infected
-395,female,45,104,38.9,75,72,Infected
-396,male,85,99,38.9,75,63,Infected
-397,male,75,90,38.9,75,72,Infected
-398,male,75,90,38.9,75,53,Infected
-399,male,55,97,38.9,75,69,Infected
-400,male,65,88,38.9,75,62,Infected
-401,male,75,91,38.9,75,55,Infected
-402,male,75,91,38.9,75,65,Infected
-403,female,75,88,38.9,75,57,Infected
-404,male,55,88,38.9,75,55,Infected
-405,female,55,87,38.9,75,80,Infected
-406,male,55,87,38.9,75,76,Infected
-407,male,55,93,38.5,75,55,Infected
-408,female,60,77,38.5,75,74,Infected
-409,male,45,86,38.5,75,56,Infected
-410,male,55,79,38.5,75,54,Infected
-411,male,55,83,38.5,75,55,Infected
-412,male,35,87,38.5,75,61,Infected
-413,male,75,90,38.5,75,59,Infected
-414,male,65,69,38.1,75,54,Infected
-415,male,45,68,38.1,75,55,Infected
-416,male,45,67,38.1,75,59,Infected
-417,male,5,67,38.1,75,59,Infected
-418,male,35,67,38.1,55,59,Infected
-419,male,5,65,38.1,55,55,Infected
-420,male,85,70,38.1,55,55,Infected
-421,male,35,61,38.1,55,59,Infected
-422,male,85,60,38.1,55,63,Infected
-423,male,65,59,38.1,40,63,Infected
-424,female,45,58,38.1,40,52,Infected
-425,female,65,58,38.1,20,54,Infected
-426,male,65,58,38.1,20,51,Infected
-427,male,40,56,38.1,20,50,Infected
-428,female,65,54,38.1,20,53,Infected
-429,male,11,54,38.1,70,56,Infected
-430,male,2,55,38.1,80,50,Infected
-431,female,4,55,38.1,130,51,Non Infected
-432,female,36,55,38.1,45,52,Infected
-433,male,52,56,38.1,60,52,Infected
-434,female,49,58,38.1,25,53,Infected
-435,male,41,56,38.1,55,54,Infected
-436,male,63,57,38.1,55,52,Infected
-437,male,45,55,38.1,55,53,Infected
-438,male,9,57,38.1,55,54,Infected
-439,female,40,56,38.1,25,53,Infected
-440,female,37,58,38.1,25,54,Infected
-441,female,59,59,38.1,25,55,Infected
-442,female,67,60,38.1,90,55,Infected
-443,female,65,60,38.1,45,52,Infected
-444,male,31,58,38.1,45,67,Infected
-445,female,39,58,38.1,25,56,Infected
-446,male,27,59,38.1,65,54,Infected
-447,female,32,60,38.1,30,55,Infected
-448,female,83,61,38.1,25,57,Infected
-449,female,53,60,38.1,20,55,Infected
-450,male,32,62,38.1,20,55,Infected
-451,male,66,62,38.1,20,54,Infected
-452,female,53,61,38.1,40,56,Infected
-453,male,37,60,38.1,340,57,Non Infected
-454,male,36,59,38.1,80,56,Infected
-455,female,56,60,38.1,80,57,Infected
-456,male,56,60,38.1,80,58,Infected
-457,male,35,63,38.1,80,56,Infected
-458,female,56,66,38.1,80,54,Infected
-459,male,56,63,38.1,80,57,Infected
-460,male,56,57,38.1,40,64,Infected
-461,female,31,61,38.1,80,53,Infected
-462,female,37,64,38.1,80,59,Infected
-463,female,73,66,38.1,130,55,Non Infected
-464,male,31,62,38.1,70,52,Infected
-465,female,47,66,38.1,165,53,Non Infected
-466,male,38,67,38.1,35,55,Infected
-467,female,47,66,38.1,35,56,Infected
-468,female,31,65,38.1,35,54,Infected
-469,female,28,65,38.1,35,58,Infected
-470,female,48,65,38.1,35,51,Infected
-471,female,44,64,38.1,35,51,Infected
-472,NA,NA,65,38.1,35,52,Infected
-473,NA,NA,64,38.1,35,56,Infected
-474,female,32,63,38.1,20,57,Infected
-475,male,NA,62,38.1,40,56,Infected
-476,female,42,65,38.1,40,55,Infected
-477,male,0.5,57,38.1,40,56,Infected
-478,male,45,58,38.1,40,56,Infected
-479,male,41,57,38.1,40,54,Infected
-480,male,27,58,38.1,40,54,Infected
-481,male,53,58,38.1,30,55,Infected
-482,female,42,58,38.1,30,54,Infected
-483,female,39,56,38.1,30,53,Infected
-484,female,40,55,38.1,40,52,Infected
-485,male,64,56,38.1,20,56,Infected
-486,female,38,60,38.1,20,55,Infected
-487,male,53,57,38.1,20,51,Infected
-488,female,52,57,38.1,20,56,Infected
-489,male,51,56,38.1,25,56,Infected
-490,male,36,56,38.1,40,53,Infected
-491,male,71,60,38.1,35,56,Infected
-492,male,39,59,38.1,55,54,Infected
-493,male,54,63,38.1,55,53,Infected
-494,male,37,62,38.1,55,56,Infected
-495,female,2,61,38.1,30,50,Infected
-496,male,35,57,38.1,30,57,Infected
-497,male,39,58,38.1,30,56,Infected
-498,male,34,95,38.1,30,52,Infected
-499,male,46,78,38.1,30,51,Infected
-500,male,62,83,39,30,63,Infected
-501,male,48,78,39,30,52,Infected
-502,male,37,72,39,30,54,Infected
-503,male,54,61,39,30,54,Infected
-504,female,55,69,39,30,54,Infected
-505,male,30,72,39,30,51,Infected
-506,male,30,74,39,30,54,Infected
-507,male,26,73,39,30,51,Infected
-508,male,55,70,39,30,53,Infected
-509,male,61,73,39,30,53,Infected
-510,female,51,71,39,30,50,Infected
-511,male,57,68,39,30,51,Infected
-512,female,44,66,39,30,53,Infected
-513,female,54,69,39,30,53,Infected
-514,male,50,78,39,30,53,Infected
-515,female,61,77,39,30,52,Infected
-516,male,28,74,39,30,53,Infected
-517,female,56,69,39,30,54,Infected
-518,female,79,67,39,30,53,Infected
-519,male,26,66,39,30,59,Infected
-520,female,27,64,39,30,53,Infected
-521,male,25,72,39,30,53,Infected
-522,male,40,66,39,30,56,Infected
-523,male,43,60,39,30,55,Infected
-524,male,29,67,39,30,53,Infected
-525,female,71,69,39,30,52,Infected
-526,male,1,73,39,30,52,Infected
-527,male,35,72,39,30,52,Infected
-528,female,57,64,39,30,52,Infected
-529,female,35,61,39,30,54,Infected
-530,female,38,71,39,30,51,Infected
-531,male,50,72,39,30,54,Infected
-532,female,57,68,39,30,55,Infected
-533,male,54,75,39,30,55,Infected
-534,female,35,66,39,30,54,Infected
-535,male,36,70,39,30,52,Infected
-536,male,24,77,39,30,50,Infected
-537,male,32,73,39,30,50,Infected
-538,female,30,69,39,30,53,Infected
-539,male,41,65,39,30,51,Infected
-540,female,75,68,39,30,50,Infected
-541,female,58,67,39,30,58,Infected
-542,male,47,72,39,30,57,Infected
-543,male,38,73,39,30,55,Infected
-544,female,35,68,39,30,54,Infected
-545,male,55,71,39,30,52,Infected
-546,male,54,81,39,30,52,Infected
-547,male,55,83,39,30,52,Infected
-548,male,32,67,37.6,30,55,Infected
-549,male,56,76,37.6,30,56,Infected
-550,male,28,74,37.6,30,56,Infected
-551,female,62,56,37.6,30,58,Infected
-552,female,28,68,37.6,30,57,Infected
-553,female,52,75,37.6,30,54,Infected
-554,male,25,77,37.6,30,78,Infected
-555,male,48,73,37.6,30,76,Infected
-556,male,28,70,37.6,30,58,Infected
-557,female,40,80,37.6,30,52,Infected
-558,male,43,66,37.6,30,54,Infected
-559,female,42,67,37.6,30,54,Infected
-560,male,37,70,37.6,30,57,Infected
-561,female,21,76,37.6,30,61,Infected
-562,male,36,77,37.6,30,59,Infected
-563,female,41,67,37.6,30,60,Infected
-564,female,59,67,37.6,30,94,Infected
-565,male,46,73,37.6,30,57,Infected
-566,female,58,72,37.6,30,53,Infected
-567,male,28,68,37.6,30,54,Infected
-568,female,73,70,37.6,30,57,Infected
-569,male,51,68,37.3,97,88,Non Infected
-570,female,37,69,37.3,97,88,Non Infected
-571,female,30,64,37.3,97,91,Non Infected
-572,male,82,72,37.3,97,86,Non Infected
-573,female,68,73,37.3,97,83,Non Infected
-574,female,61,64,37.3,97,78,Non Infected
-575,female,11,71,38.3,70,70,Infected
-576,female,40,67,38.3,70,60,Infected
-577,male,24,69,38.3,70,58,Infected
-578,female,26,73,38.1,70,67,Infected
-579,female,48,67,38.1,70,56,Infected
-580,male,49,70,38.1,70,57,Infected
-581,female,57,74,38.1,70,58,Infected
-582,female,61,70,38.1,70,59,Infected
-583,male,77,69,38.1,70,56,Infected
-584,female,69,69,38.1,70,53,Infected
-585,female,29,68,38.1,70,55,Infected
-586,female,58,71,38.1,98,55,Non Infected
-587,female,46,60,38.1,98,55,Non Infected
-588,female,54,64,38.1,98,55,Non Infected
-589,male,28,62,38.1,98,51,Non Infected
-590,female,63,64,40,93,51,Infected
-591,female,72,62,38.5,93,49,Infected
-592,male,58,80,38.5,93,49,Infected
-593,male,76,69,37.7,95,51,Non Infected
-594,female,61,82,37.7,95,54,Non Infected
-595,male,24,89,37.7,95,52,Non Infected
-596,female,38,75,38,90,52,Infected
-597,male,57,69,38,90,52,Infected
-598,male,59,72,38,95,56,Non Infected
-599,male,75,66,39.6,80,55,Infected
-600,male,30,113,38.5,79,54,Infected
-601,female,26,102,38.5,79,61,Infected
-602,female,34,108,38.5,79,62,Infected
-603,female,59,62,38.5,79,62,Infected
-604,female,58,77,38.5,79,63,Infected
-605,male,60,77,38.5,79,69,Infected
-606,female,52,90,38.5,79,54,Infected
-607,female,59,87,38.5,79,53,Infected
-608,female,50,87,38.5,79,57,Infected
-609,male,22,88,38.5,79,52,Infected
-610,female,30,88,38.5,79,52,Infected
-611,female,56,88,38.5,79,55,Infected
-612,male,29,90,38,79,50,Infected
-613,female,48,90,38.2,87,55,Infected
-614,female,56,85,38.2,66,56,Infected
-615,female,39,83,38.2,66,55,Infected
-616,female,20,93,38.2,66,56,Infected
-617,male,49,94,36.1,66,58,Infected
-618,female,23,93,36.1,66,59,Infected
-619,male,36,92,36.1,66,57,Infected
-620,female,57,91,36.1,66,59,Infected
-621,male,55,92,37.5,66,60,Infected
-622,female,22,92,36,97,60,Non Infected
-623,female,34,91,39.1,97,58,Non Infected
-624,female,65,93,36.8,97,58,Non Infected
-625,male,64,89,38.3,88,57,Infected
-626,NA,NA,90,40,88,57,Infected
-627,NA,NA,90,37,88,57,Infected
-628,NA,NA,91,36.7,88,60,Infected
-629,NA,NA,92,36.8,88,62,Infected
-630,NA,NA,93,38.5,88,61,Infected
-631,NA,NA,92,38.5,88,69,Infected
-632,NA,NA,95,38.5,88,60,Infected
-633,NA,NA,96,39.4,88,60,Infected
-634,NA,NA,95,39.4,90,60,Infected
-635,NA,NA,96,39.4,90,55,Infected
-636,NA,NA,97,36.8,95,55,Non Infected
-637,NA,NA,101,36.8,95,61,Non Infected
-638,NA,NA,99,36.8,95,60,Non Infected
-639,NA,NA,99,36.8,95,93,Non Infected
-640,NA,NA,97,36.8,95,90,Non Infected
-641,NA,NA,96,39,95,83,Non Infected
-642,NA,NA,96,39,95,98,Non Infected
-643,NA,NA,97,39,85,75,Infected
-644,NA,NA,96,39,85,89,Infected
-645,NA,NA,95,39,85,94,Infected
-646,NA,NA,96,39,85,86,Infected
-647,NA,NA,97,38.2,92,88,Infected
-648,male,67,96,38.2,92,96,Infected
-649,male,74,96,38.2,92,83,Infected
-650,male,35,96,38.2,92,100,Infected
-651,male,62,96,38.2,92,88,Infected
-652,male,58,96,38.2,92,72,Infected
-653,male,62,97,38.2,92,50,Infected
-654,male,41,96,38.2,92,52,Infected
-655,male,36,97,38.2,92,48,Infected
-656,female,69,96,38.2,92,55,Infected
-657,male,75,96,38.5,85,50,Infected
-658,female,55,96,38.5,85,53,Infected
-659,female,55,97,38.5,85,66,Infected
-660,male,55,97,38.5,85,68,Infected
-661,female,50,97,38.5,85,64,Infected
-662,female,50,100,38.5,85,60,Infected
-663,female,70,101,38.5,85,72,Infected
-664,female,70,99,38.5,85,63,Infected
-665,male,55,96,38.5,85,70,Infected
-666,male,NA,96,38.5,85,71,Infected
-667,female,NA,96,38.5,85,72,Infected
-668,male,55,96,38.5,85,63,Infected
-669,male,45,95,38.5,85,62,Infected
-670,female,25,94,38.5,85,82,Infected
-671,male,55,93,38.5,85,80,Infected
-672,female,55,92,38.5,85,78,Infected
-673,female,45,91,38.5,85,67,Infected
-674,male,25,90,38.5,85,70,Infected
-675,male,25,90,38.5,85,61,Infected
-676,male,65,90,38.5,85,64,Infected
-677,male,55,89,38.5,85,77,Infected
-678,female,85,89,38.5,85,72,Infected
-679,male,35,88,38.5,85,71,Infected
-680,female,65,90,38.5,85,68,Infected
-681,female,60,91,38.5,85,76,Infected
-682,female,45,93,38.5,85,55,Infected
-683,female,25,96,38.5,85,84,Infected
-684,male,85,96,38.5,85,87,Infected
-685,male,55,95,38.5,85,94,Infected
-686,male,45,95,38.5,85,65,Infected
-687,female,75,97,38.5,85,66,Infected
-688,male,NA,98,38.5,85,64,Infected
-689,female,35,99,38.5,85,55,Infected
-690,male,35,100,38.5,85,49,Infected
-691,female,55,102,38.5,85,52,Infected
-692,female,61,102,38.5,85,58,Infected
-693,female,74,101,38.5,85,53,Infected
-694,male,68,103,38.5,85,54,Infected
-695,female,73,99,38.5,85,51,Infected
-696,male,NA,97,38.5,85,53,Infected
-697,female,33,97,38.5,85,61,Infected
-698,female,57,98,38.5,85,56,Infected
-699,female,73,98,38.5,85,62,Infected
-700,NA,6,98,38.5,85,55,Infected
-701,NA,60,99,38.5,85,57,Infected
-702,NA,NA,99,38.5,85,68,Infected
-703,NA,NA,100,38.5,85,75,Infected
-704,NA,NA,102,38.5,85,68,Infected
-705,NA,NA,103,38.5,85,53,Infected
-706,NA,NA,103,38.5,85,47,Infected
-707,NA,NA,102,38.5,85,53,Infected
-708,NA,NA,102,38.5,85,50,Infected
-709,NA,NA,103,38.5,85,69,Infected
-710,NA,NA,103,38.5,85,58,Infected
-711,male,50,106,38.5,85,54,Infected
-712,NA,NA,106,38.5,85,66,Infected
-713,NA,NA,106,38.5,85,74,Infected
-714,NA,NA,107,38.5,85,91,Infected
-715,male,70,106,38.5,85,51,Infected
-716,NA,NA,107,38.5,85,59,Infected
-717,NA,NA,106,38.5,85,65,Infected
-718,NA,NA,106,38.5,85,79,Infected
-719,NA,NA,107,38.5,85,103,Infected
-720,NA,NA,107,38.5,85,56,Infected
-721,NA,NA,107,38.5,85,55,Infected
-722,NA,NA,106,38.5,85,72,Infected
-723,NA,NA,106,38.5,85,49,Infected
-724,female,54,106,38.5,85,78,Infected
-725,female,35,107,38.5,85,71,Infected
-726,female,60,109,38.5,85,55,Infected
-727,female,31,111,38.5,85,59,Infected
-728,male,29,110,38.5,85,52,Infected
-729,male,NA,109,38.5,85,54,Infected
-730,female,NA,110,38.5,85,73,Infected
-731,male,8,106,38.5,65,65,Infected
-732,male,25,106,38.5,65,73,Infected
-733,male,35,106,38.5,175,55,Non Infected
-734,female,61,107,38.5,35,75,Infected
-735,male,NA,107,38.5,35,75,Infected
-736,NA,NA,109,38.5,175,71,Non Infected
-737,NA,NA,109,38.5,175,45,Non Infected
-738,male,65,109,38.5,175,45,Non Infected
-739,male,NA,108,38.5,175,51,Non Infected
-740,male,25,108,38.5,175,73,Non Infected
-741,male,57,107,38.5,175,79,Non Infected
-742,female,57,107,38.5,175,77,Non Infected
-743,female,NA,107,38.5,100,78,Non Infected
-744,NA,NA,106,38.5,85,91,Infected
-745,NA,NA,106,38.5,105,86,Non Infected
-746,NA,NA,105,38.5,105,74,Non Infected
-747,NA,NA,107,38.5,105,85,Non Infected
-748,NA,NA,108,38.5,105,77,Non Infected
-749,NA,NA,108,38.5,75,96,Infected
-750,NA,NA,109,38.5,40,75,Infected
-751,male,66,109,38.5,65,78,Infected
-752,male,28,109,38.5,45,73,Infected
-753,NA,NA,110,38.5,45,75,Infected
-754,NA,NA,110,38.5,99,64,Non Infected
-755,NA,NA,108,38.5,99,94,Non Infected
-756,NA,NA,109,38.5,99,75,Non Infected
-757,NA,NA,110,38.5,99,100,Non Infected
-758,NA,NA,110,38.5,87.3,100,Non infected
-759,male,30,111,38.5,87.3,68,Infected
-760,female,42,112,38.5,87.3,83,Infected
-761,female,16,110,38.5,87.3,105,Non Infected
-762,NA,49,110,38.5,87.3,104,Non Infected
-763,female,29,110,38.5,87.3,94,Infected
-764,female,55,109,38.5,87.3,82,Infected
-765,NA,0.25,111,38.5,87.3,87,Infected
-766,male,50,111,38.5,87.3,76,Infected
-767,male,35,110,38.5,87.3,83,Infected
-768,male,43,112,38.5,87.3,78,Infected
-769,male,53,113,38.5,87.3,69,Infected
-770,male,55,114,38.5,87.3,72,Infected
-771,female,21,114,38.5,87.3,74,Infected
-772,male,44,114,38.5,87.3,63,Infected
-773,male,65,113,38.5,87.3,84,Infected
-774,female,45,111,38.5,87.3,84,Infected
-775,female,42,112,38.5,87.3,69,Infected
-776,male,60,112,38.5,87.3,68,Infected
-777,female,60,111,38.5,87.3,82,Infected
-778,female,25,112,38.5,87.3,78,Infected
-779,male,8,112,38.5,87.3,72,Infected
-780,male,37,110,38.5,87.3,75,Infected
-781,female,37,109,38.5,87.3,68,Infected
-782,male,56,109,38.5,87.3,79,Infected
-783,female,55,111,38.5,87.3,74,Infected
-784,male,45,111,38.5,87.3,74,Infected
-785,female,25,111,38.5,87.3,67,Infected
-786,female,55,111,38.5,87.3,65,Infected
-787,male,35,111,38.5,87.3,71,Infected
-788,female,35,111,38.5,87.3,73,Infected
-789,female,35,111,38.5,87.3,63,Infected
-790,female,35,108,38.5,87.3,66,Infected
-791,female,25,110,38.5,87.3,74,Infected
-792,male,45,112,38.5,87.3,68,Infected
-793,female,65,113,38.5,87.3,91,Infected
-794,male,60,113,38.5,87.3,94,Infected
-795,female,40,112,38.5,87.3,81,Infected
-796,male,33,112,38.5,87.3,68,Infected
-797,NA,NA,112,38.5,87.3,63,Infected
-798,NA,NA,108,38.5,87.3,80,Infected
-799,NA,NA,108,38.5,87.3,77,Infected
-800,male,NA,109,38.5,87.3,72,Infected
-801,NA,NA,109,38.5,87.3,105,Non infected
-802,male,52,108,38.5,87.3,99,Non infected
-803,male,33,108,38.5,87.3,71,Infected
-804,NA,NA,111,38.5,87.3,67,Infected
-805,NA,NA,112,38.5,87.3,74,Infected
-806,NA,NA,112,38.5,87.3,64,Infected
-807,male,58,112,38.5,87.3,73,Infected
-808,female,NA,111,38.5,87.3,75,Infected
-809,female,NA,110,38.5,87.3,70,Infected
-810,male,49,109,38.5,87.3,80,Infected
-811,NA,NA,110,38.5,87.3,73,Infected
-812,male,25,111,38.5,87.3,79,Infected
-813,male,60,111,38.5,87.3,69,Infected
-814,female,24,108,38.5,87.3,84,Infected
-815,male,32,109,38.5,87.3,81,Infected
-816,male,47,109,38.5,87.3,72,Infected
-817,female,46,111,38.5,87.3,77,Infected
-818,male,41,114,38.5,87.3,76,Infected
-819,NA,NA,114,38.5,87.3,86,Infected
-820,NA,NA,113,38.5,87.3,87,Infected
-821,NA,NA,114,38.5,87.3,83,Infected
-822,NA,NA,114,38.5,87.3,80,Infected
-823,NA,NA,115,38.5,87.3,93,Infected
-824,NA,NA,112,38.5,87.3,83,Infected
-825,NA,NA,112,38.5,87.3,77,Infected
-826,NA,NA,112,38.5,87.3,91,Infected
-827,NA,NA,112,38.5,30,79,Infected
-828,NA,NA,112,38.5,30,77,Infected
-829,NA,NA,111,38.5,30,75,Infected
-830,NA,NA,110,38.5,30,87,Infected
-831,NA,NA,112,38.5,30,88,Infected
-832,NA,NA,114,38.5,30,82,Infected
-833,NA,NA,112,38.5,30,77,Infected
-834,NA,NA,112,38.5,30,76,Infected
-835,NA,NA,111,38.5,30,75,Infected
-836,male,NA,112,38.5,30,77,Infected
-837,female,NA,114,38.5,30,71,Infected
-838,female,NA,114,38.5,30,78,Infected
-839,male,NA,113,38.5,30,82,Infected
-840,male,NA,113,38.5,30,79,Infected
-841,NA,NA,112,38.5,30,89,Infected
-842,male,NA,112,38.5,30,80,Infected
-843,male,31,112,38.5,30,74,Infected
-844,male,NA,111,38.5,30,81,Infected
-845,male,NA,112,38.5,30,82,Infected
-846,male,NA,113,38.5,30,87,Infected
-847,male,NA,110,38.5,30,75,Infected
-848,male,32,110,38.5,30,80,Infected
-849,female,NA,110,38.5,30,69,Infected
-850,female,73,111,38.5,30,63,Infected
-851,female,36,111,38.5,30,70,Infected
-852,male,41,110,38.5,30,74,Infected
-853,male,8,113,38.5,30,82,Infected
-854,NA,NA,113,38.5,30,77,Infected
-855,NA,NA,113,38.5,30,79,Infected
-856,NA,NA,113,38.5,30,85,Infected
-857,NA,NA,112,38.5,30,76,Infected
-858,male,37,109,38.5,30,59,Infected
-859,NA,34,109,38.5,30,74,Infected
-860,NA,39,110,38.5,30,75,Infected
-861,male,70,113,38.5,30,78,Infected
-862,female,64,113,38.5,30,71,Infected
-863,NA,NA,113,38.5,30,67,Infected
-864,NA,NA,112,38.5,30,83,Infected
-865,NA,NA,112,38.5,30,74,Infected
-866,NA,NA,111,38.5,30,87,Infected
-867,NA,NA,111,38.5,30,86,Infected
-868,NA,NA,111,38.5,30,78,Infected
-869,NA,NA,112,38.5,30,66,Infected
-870,NA,NA,113,38.5,30,65,Infected
-871,male,39,114,38.5,30,89,Infected
-872,male,56,115,38.5,30,72,Infected
-873,female,62,115,38.5,30,72,Infected
-874,female,62,114,38.5,30,63,Infected
-875,male,63,111,38.5,30,86,Infected
-876,male,47,111,38.5,30,66,Infected
-877,female,68,110,38.5,30,64,Infected
-878,male,64,109,38.5,30,64,Infected
-879,female,73,109,38.5,30,64,Infected
-880,male,72,110,38.5,30,69,Infected
-881,female,37,111,38.5,30,71,Infected
-882,male,75,113,38.5,30,77,Infected
-883,male,39,111,38.5,30,89,Infected
-884,male,80,111,38.5,30,82,Infected
-885,female,72,110,38.5,30,87,Infected
-886,female,64,112,38.5,30,74,Infected
-887,male,60,112,38.5,30,83,Infected
-888,male,25,109,38.5,30,87,Infected
-889,female,56,111,38.5,30,88,Infected
-890,female,28,110,38.5,30,90,Infected
-891,male,56,111,38.5,30,92,Infected
-892,female,55,111,38.5,30,78,Infected
-893,female,63,110,38.5,30,82,Infected
-894,female,65,110,38.5,30,73,Infected
-895,male,58,109,38.5,30,83,Infected
-896,male,42,110,38.5,30,82,Infected
-897,male,24,111,38.5,30,86,Infected
-898,male,70,111,38.5,30,76,Infected
-899,male,68,112,38.5,30,87,Infected
-900,female,57,112,38.5,30,74,Infected
-901,female,91,116,38.5,30,70,Infected
-902,female,51,115,38.5,30,83,Infected
-903,female,25,115,38.5,30,80,Infected
-904,male,22,115,38.5,30,78,Infected
-905,female,50,115,38.5,30,75,Infected
-906,male,23,115,38.5,30,91,Infected
-907,female,55,115,38.5,30,88,Infected
-908,male,52,114,38.5,30,83,Infected
-909,male,69,113,38.5,30,75,Infected
-910,female,63,113,38.5,30,89,Infected
-911,female,86,114,38.5,30,78,Infected
-912,female,62,113,38.5,30,88,Infected
-913,male,59,113,38.5,30,76,Infected
-914,female,60,114,38.5,30,82,Infected
-915,male,71,115,38.5,30,66,Infected
-916,male,37,115,38.5,30,66,Infected
-917,male,66,113,38.5,30,81,Infected
-918,female,37,113,38.5,30,97,Infected
-919,male,75,113,38.5,30,75,Infected
-920,male,51,113,38.5,30,69,Infected
-921,male,43,112,38.5,30,88,Infected
-922,female,67,111,38.5,30,82,Infected
-923,male,37,111,38.5,30,72,Infected
-924,female,41,112,38.5,30,85,Infected
-925,male,70,112,38.5,30,87,Infected
-926,female,NA,115,38.5,30,84,Infected
-927,male,54,116,38.5,30,70,Infected
-928,male,69,116,38.5,30,66,Infected
-929,male,45,114,38.5,30,91,Infected
-930,female,46,114,38.5,30,79,Infected
-931,female,32,115,38.5,30,86,Infected
-932,male,58,114,38.5,30,96,Infected
-933,female,83,114,38.5,30,104,Non infected
-934,female,83,114,38.5,30,68,Infected
-935,male,68,115,37.9,30,71,Infected
-936,female,70,116,39.1,30,82,Infected
-937,male,75,117,39.1,30,85,Infected
-938,female,38,120,39.1,30,78,Infected
-939,male,58,120,39.1,30,85,Infected
-940,male,48,116,38,30,82,Infected
-941,female,96,115,38,30,68,Infected
-942,male,68,114,38,30,103,Non infected
-943,male,62,112,38,30,78,Infected
-944,female,80,112,38,30,73,Infected
-945,female,76,112,38,30,82,Infected
-946,male,35,114,38,30,89,Infected
-947,female,57,112,38,30,82,Infected
-948,female,68,112,38,30,87,Infected
-949,female,59,114,38,30,76,Infected
-950,female,58,114,38,30,85,Infected
-951,male,57,116,38,30,84,Infected
-952,female,57,116,38,30,82,Infected
-953,male,33,117,38,30,79,Infected
-954,female,55,115,38,30,77,Infected
-955,male,24,115,38,30,85,Infected
-956,female,60,115,38,30,89,Infected
-957,female,49,116,38,30,60,Infected
-958,female,21,117,38,30,67,Infected
-959,male,16,118,38,30,86,Infected
-960,male,80,116,38,30,70,Infected
-961,female,29,113,38,30,59,Infected
-962,male,26,113,38,30,61,Infected
-963,female,70,114,38,30,51,Infected
-964,female,89,114,38,30,44,Infected
-965,male,NA,113,38,30,49,Infected
-966,female,NA,113,38,30,105,Non infected
-967,male,35,114,38,30,86,Infected
-968,female,NA,113,38,30,81,Infected
-969,male,NA,113,38,30,77,Infected
-970,NA,NA,113,38,30,93,Infected
-971,NA,NA,111,38,30,93,Infected
-972,male,53,112,38,30,77,Infected
-973,NA,NA,113,38,30,98,Infected
-974,male,NA,113,38,30,95,Infected
-975,male,NA,115,38,30,93,Infected
-976,male,NA,117,38,30,107,Non infected
-977,female,NA,113,38,30,105,Non infected
-978,female,NA,112,38,30,104,Non infected
-979,NA,NA,113,38,30,94,Infected
-980,NA,NA,113,38,30,63,Infected
-981,NA,NA,116,38,30,65,Infected
-982,NA,NA,117,38,30,68,Infected
-983,NA,NA,117,38,30,71,Infected
-984,NA,NA,115,38,30,74,Infected
-985,NA,NA,113,38,30,63,Infected
-986,NA,NA,113,38,30,52,Infected
-987,NA,NA,114,38,30,70,Infected
-988,female,NA,115,38,30,82,Infected
-989,male,NA,117,38,30,58,Infected
-990,female,NA,117,38,30,72,Infected
-991,NA,NA,115,38,30,64,Infected
-992,NA,NA,116,38,30,79,Infected
-993,male,44,117,38,30,79,Infected
-994,female,38,118,38,30,61,Infected
-995,female,60,116,38,30,66,Infected
-996,female,32,115,38,30,75,Infected
-997,male,NA,114,38,30,70,Infected
-998,male,NA,114,38,30,59,Infected
-999,male,69,115,38,30,65,Infected
-1000,female,NA,114,38,30,64,Infected
-1001,female,36,114,38,30,60,Infected
-1002,NA,24,115,38,30,87,Infected
-1003,male,31,113,38,30,67,Infected
-1004,NA,NA,113,38,30,77,Infected
-1005,NA,NA,112,38,30,62,Infected
-1006,NA,NA,111,38,30,99,Infected
-1007,male,22,111,38,30,71,Infected
-1008,male,62,112,38,30,63,Infected
-1009,female,NA,112,38,30,65,Infected
-1010,male,44,115,38,30,79,Infected
-1011,NA,77,114,38,30,73,Infected
-1012,NA,50,114,38,30,68,Infected
-1013,female,22,114,38,30,89,Infected
-1014,male,30,114,38,30,75,Infected
-1015,NA,18,115,38,30,70,Infected
-1016,male,NA,113,38,30,70,Infected
-1017,male,NA,112,38,30,60,Infected
-1018,male,NA,112,38,30,69,Infected
-1019,male,NA,112,38,30,70,Infected
-1020,female,NA,109,38,30,87,Infected
-1021,male,NA,110,38,30,66,Infected
-1022,male,28,109,38,30,80,Infected
-1023,male,42,109,38,30,78,Infected
-1024,male,53,110,38,30,73,Infected
-1025,NA,81,109,38,30,73,Infected
-1026,male,55,109,38,30,80,Infected
-1027,female,25,111,38,30,81,Infected
-1028,male,58,110,38,30,72,Infected
-1029,NA,66,110,38,30,66,Infected
-1030,female,27,109,38,30,64,Infected
-1031,female,25,109,38,30,60,Infected
-1032,NA,NA,108,38,30,74,Infected
-1033,NA,NA,110,38,30,68,Infected
-1034,NA,NA,110,38,30,75,Infected
-1035,NA,NA,112,38,30,83,Infected
-1036,NA,NA,112,38,30,78,Infected
-1037,NA,NA,112,38,30,61,Infected
-1038,NA,NA,113,38,30,79,Infected
-1039,NA,NA,113,38,30,78,Infected
-1040,NA,NA,114,38,30,79,Infected
-1041,NA,NA,113,38,30,81,Infected
-1042,NA,NA,113,38,30,89,Infected
-1043,NA,NA,111,38,30,70,Infected
-1044,NA,NA,110,38,30,74,Infected
-1045,NA,NA,109,38,30,74,Infected
-1046,NA,NA,109,38,30,78,Infected
-1047,NA,NA,109,38,30,74,Infected
-1048,NA,NA,108,38,30,71,Infected
-1049,NA,NA,108,38,30,66,Infected
-1050,NA,NA,108,38,30,54,Infected
-1051,NA,NA,108,38,30,58,Infected
-1052,female,NA,108,38,30,57,Infected
-1053,female,45,107,38,30,68,Infected
-1054,NA,NA,108,38,30,62,Infected
-1055,NA,NA,108,38,30,66,Infected
-1056,NA,NA,108,38,30,56,Infected
-1057,NA,NA,109,38,30,57,Infected
-1058,NA,NA,110,38,30,60,Infected
-1059,NA,NA,110,38,30,57,Infected
-1060,NA,NA,110,38,30,60,Infected
-1061,NA,NA,110,38,30,97,Infected
-1062,female,NA,110,38,30,80,Infected
-1063,NA,NA,110,38,30,93,Infected
-1064,NA,NA,110,38,30,83,Infected
-1065,male,NA,111,38,30,81,Infected
-1066,female,NA,114,38,30,78,Infected
-1067,female,NA,117,38,30,87,Infected
-1068,female,NA,116,38,30,96,Infected
-1069,female,NA,115,38,30,83,Infected
-1070,female,NA,118,38,30,86,Infected
-1071,female,NA,114,38,30,83,Infected
-1072,female,NA,114,38,30,80,Infected
-1073,female,NA,113,38,30,69,Infected
-1074,female,NA,113,38,30,73,Infected
-1075,male,NA,112,38,30,78,Infected
-1076,male,NA,112,38,30,83,Infected
-1077,male,NA,112,38,30,109,Non infected
-1078,female,NA,111,38,30,99,Non infected
-1079,female,NA,111,38,30,80,Infected
-1080,NA,24,109,38,30,84,Infected
-1081,NA,24,110,38,30,72,Infected
-1082,NA,35,110,38,30,74,Infected
-1083,male,NA,110,38,30,68,Infected
-1084,male,NA,110,38,30,67,Infected
-1085,male,70,110,38,30,70,Infected
diff --git a/Jupyter-notebook/Machine learning/Health prediction model/new_app/mac_address/mac_address.ino b/Jupyter-notebook/Machine learning/Health prediction model/new_app/mac_address/mac_address.ino
deleted file mode 100644
index e6cd934..0000000
--- a/Jupyter-notebook/Machine learning/Health prediction model/new_app/mac_address/mac_address.ino
+++ /dev/null
@@ -1,11 +0,0 @@
-#include <WiFi.h>
-
-void setup() {
-  Serial.begin(115200);
-  WiFi.mode(WIFI_STA);
-  Serial.print("MAC Address: ");
-  Serial.println(WiFi.macAddress());
-}
-
-void loop() {
-}
diff --git a/Jupyter-notebook/Machine learning/Health prediction model/new_app/new_app-20240627T061025Z-001/new_app/mac_address/mac_address.ino b/Jupyter-notebook/Machine learning/Health prediction model/new_app/new_app-20240627T061025Z-001/new_app/mac_address/mac_address.ino
deleted file mode 100644
index 5f593f4..0000000
--- a/Jupyter-notebook/Machine learning/Health prediction model/new_app/new_app-20240627T061025Z-001/new_app/mac_address/mac_address.ino
+++ /dev/null
@@ -1,13 +0,0 @@
-#include <WiFi.h>
-//08:B6:1F:3B:85:FC esp32 
-// 30:AE:A4:47:73:1C esp32 lolin 
-
-void setup() {
-  Serial.begin(115200);
-  WiFi.mode(WIFI_STA);
-  Serial.print("MAC Address: ");
-  Serial.println(WiFi.macAddress());
-}
-
-void loop() {
-}
diff --git a/Jupyter-notebook/Machine learning/Health prediction model/new_app/new_app-20240627T061025Z-001/new_app/receiver/receiver.ino b/Jupyter-notebook/Machine learning/Health prediction model/new_app/new_app-20240627T061025Z-001/new_app/receiver/receiver.ino
deleted file mode 100644
index e9252ef..0000000
--- a/Jupyter-notebook/Machine learning/Health prediction model/new_app/new_app-20240627T061025Z-001/new_app/receiver/receiver.ino
+++ /dev/null
@@ -1,115 +0,0 @@
-#include <Wire.h>
-#include "MAX30105.h"
-#include "heartRate.h"
-#include <esp_now.h>
-#include <WiFi.h>
-
-MAX30105 particleSensor;
-
-float beatsPerMinute;
-float SpO2Value; // Variable to store SpO2 value
-long lastBeat = 0; // Time at which the last beat occurred
-
-// Peer MAC Address - replace this with the MAC address of the sender ESP32
-uint8_t peerAddress[] = {0x30, 0xAE, 0xA4, 0x47, 0x73, 0x1C}; // Example MAC, replace with actual peer's MAC
-
-typedef struct struct_message {
-  float temperature;
-} struct_message;
-
-struct_message incomingData;
-
-void onDataReceive(const uint8_t * mac, const uint8_t *data, int len) {
-  memcpy(&incomingData, data, sizeof(incomingData));
-  Serial.print("Temperature received: ");
-  Serial.println(incomingData.temperature);
-}
-
-void setup()
-{
-  Serial.begin(115200);
-  Serial.println("Initializing...");
-
-  // Initialize sensor
-  if (!particleSensor.begin(Wire, I2C_SPEED_FAST)) //Use default I2C port, 400kHz speed
-  {
-    Serial.println("MAX30105 was not found. Please check wiring/power. ");
-    while (1);
-  }
-  Serial.println("Place your index finger on the sensor with steady pressure.");
-
-  particleSensor.setup(); //Configure sensor with default settings
-  particleSensor.setPulseAmplitudeRed(0x0A); //Turn Red LED to low to indicate sensor is running
-  particleSensor.setPulseAmplitudeGreen(0); //Turn off Green LED
-
-  // Set device as a Wi-Fi Station
-  WiFi.mode(WIFI_STA);
-
-  // Initialize ESP-NOW
-  if (esp_now_init() != ESP_OK) {
-    Serial.println("Error initializing ESP-NOW");
-    return;
-  }
-
-  // Register peer
-  esp_now_peer_info_t peerInfo;
-  memset(&peerInfo, 0, sizeof(peerInfo)); // Clear peerInfo structure
-  memcpy(peerInfo.peer_addr, peerAddress, 6);
-  peerInfo.channel = 0;  
-  peerInfo.encrypt = false;
-
-  // Add peer
-  if (esp_now_add_peer(&peerInfo) != ESP_OK) {
-    Serial.println("Failed to add peer");
-    return;
-  }
-
-  // Register for a callback function that will be called when data is received
-  esp_now_register_recv_cb(onDataReceive);
-}
-
-void loop()
-{
-  long irValue = particleSensor.getIR();
-  long redValue = particleSensor.getRed();
-
-  if (checkForBeat(irValue) == true)
-  {
-    // We sensed a beat!
-    long delta = millis() - lastBeat;
-    lastBeat = millis();
-
-    beatsPerMinute = 60 / (delta / 1000.0);
-  }
-
-  // Calculate SpO2 value based on IR and Red readings
-  // You can use your own algorithm or a library to calculate SpO2
-  SpO2Value = calculateSpO2(irValue, redValue); // Placeholder function
-
-  // Print all values to Serial
-  Serial.print("IR=");
-  Serial.print(irValue);
-  Serial.print(", Red=");
-  Serial.print(redValue);
-  Serial.print(", BPM=");
-  Serial.print(beatsPerMinute);
-  Serial.print(", SpO2=");
-  Serial.print(SpO2Value);
-  Serial.print(", Temperature=");
-  Serial.print(incomingData.temperature);
-
-  if (irValue < 50000)
-    Serial.print(" No finger?");
-
-  Serial.println();
-
-  delay(2000); // Update every 2 seconds
-}
-
-// Placeholder function for SpO2 calculation
-float calculateSpO2(long irValue, long redValue)
-{
-  // Implement SpO2 calculation algorithm here
-  // For demonstration, return a dummy value
-  return 98.0; // Replace with actual calculation
-}
diff --git a/Jupyter-notebook/Machine learning/Health prediction model/new_app/new_app-20240627T061025Z-001/new_app/sender/sender.ino b/Jupyter-notebook/Machine learning/Health prediction model/new_app/new_app-20240627T061025Z-001/new_app/sender/sender.ino
deleted file mode 100644
index f223b5d..0000000
--- a/Jupyter-notebook/Machine learning/Health prediction model/new_app/new_app-20240627T061025Z-001/new_app/sender/sender.ino
+++ /dev/null
@@ -1,79 +0,0 @@
-#include <esp_now.h>
-#include <WiFi.h>
-#include <OneWire.h>
-#include <DallasTemperature.h>
-
-// Data wire is connected to GPIO4
-#define ONE_WIRE_BUS 4
-
-// Setup a oneWire instance to communicate with any OneWire devices
-OneWire oneWire(ONE_WIRE_BUS);
-
-// Pass our oneWire reference to Dallas Temperature sensor
-DallasTemperature sensors(&oneWire);
-
-// Peer MAC Address - replace this with the MAC address of the receiver ESP32
-uint8_t peerAddress[] = {0x08, 0xB6, 0x1F, 0x3B, 0x85, 0xFC}; // Correct MAC address format
-
-typedef struct struct_message {
-  float temperature;
-} struct_message;
-
-struct_message myData;
-
-void setup() {
-  // Initialize Serial Monitor
-  Serial.begin(115200);
-  
-  // Initialize the DS18B20 sensor
-  sensors.begin();
-
-  // Set device as a Wi-Fi Station
-  WiFi.mode(WIFI_STA);
-
-  // Initialize ESP-NOW
-  if (esp_now_init() != ESP_OK) {
-    Serial.println("Error initializing ESP-NOW");
-    return;
-  }
-
-  // Register peer
-  esp_now_peer_info_t peerInfo;
-  memset(&peerInfo, 0, sizeof(peerInfo)); // Clear peerInfo structure
-  memcpy(peerInfo.peer_addr, peerAddress, 6);
-  peerInfo.channel = 0;  
-  peerInfo.encrypt = false;
-
-  // Add peer
-  if (esp_now_add_peer(&peerInfo) != ESP_OK) {
-    Serial.println("Failed to add peer");
-    return;
-  }
-}
-
-void loop() {
-  // Request temperature from DS18B20 sensor
-  sensors.requestTemperatures();
-  float temperature = sensors.getTempCByIndex(0);
-  
-  // Check if any reads failed
-  if (temperature == DEVICE_DISCONNECTED_C) {
-    Serial.println("Failed to read from DS18B20 sensor!");
-    return;
-  }
-
-  // Prepare data to send
-  myData.temperature = temperature;
-
-  // Send message via ESP-NOW
-  esp_err_t result = esp_now_send(peerAddress, (uint8_t *) &myData, sizeof(myData));
-
-  if (result == ESP_OK) {
-    Serial.print("Temperature sent: ");
-    Serial.println(temperature);
-  } else {
-    Serial.println("Error sending the data");
-  }
-
-  delay(2000); // Send data every 2 seconds
-}
diff --git a/Jupyter-notebook/Machine learning/Health prediction model/new_app/receiver/receiver.ino b/Jupyter-notebook/Machine learning/Health prediction model/new_app/receiver/receiver.ino
deleted file mode 100644
index 8134b54..0000000
--- a/Jupyter-notebook/Machine learning/Health prediction model/new_app/receiver/receiver.ino
+++ /dev/null
@@ -1,40 +0,0 @@
-#include <esp_now.h>
-#include <WiFi.h>
-
-typedef struct struct_message {
-  float temperature;
-} struct_message;
-
-struct_message myData;
-
-float temperature = 0.0;  // Variable to store the received temperature
-
-// Callback when data is received
-void OnDataRecv(const esp_now_recv_info *info, const uint8_t *incomingData, int len) {
-  memcpy(&myData, incomingData, sizeof(myData));
-  temperature = myData.temperature;  // Store the received temperature in the variable
-}
-
-void setup() {
-  // Initialize Serial Monitor
-  Serial.begin(115200);
-  
-  // Set device as a Wi-Fi Station
-  WiFi.mode(WIFI_STA);
-
-  // Initialize ESP-NOW
-  if (esp_now_init() != ESP_OK) {
-    Serial.println("Error initializing ESP-NOW");
-    return;
-  }
-
-  // Register receive callback
-  esp_now_register_recv_cb(OnDataRecv);
-}
-
-void loop() {
-  // Print the stored temperature every second
-  Serial.print("Temperature: ");
-  Serial.println(temperature);
-  delay(1000);  // Delay for 1 second
-}
diff --git a/Jupyter-notebook/Machine learning/Health prediction model/new_app/sender/sender.ino b/Jupyter-notebook/Machine learning/Health prediction model/new_app/sender/sender.ino
deleted file mode 100644
index 073a49b..0000000
--- a/Jupyter-notebook/Machine learning/Health prediction model/new_app/sender/sender.ino
+++ /dev/null
@@ -1,73 +0,0 @@
-#include <esp_now.h>
-#include <WiFi.h>
-#include <DHT.h>
-
-#define DHTPIN 4     // Pin connected to the DHT11 sensor
-#define DHTTYPE DHT11   // DHT 11
-
-DHT dht(DHTPIN, DHTTYPE);
-
-// Peer MAC Address - replace this with the MAC address of the receiver ESP32
-uint8_t peerAddress[] = {0x4C, 0x11, 0xAE, 0x9C, 0x19, 0xC4}; // Example MAC, replace with actual peer's MAC
-
-typedef struct struct_message {
-  float temperature;
-} struct_message;
-
-struct_message myData;
-
-void setup() {
-  // Initialize Serial Monitor
-  Serial.begin(115200);
-  
-  // Initialize DHT sensor
-  dht.begin();
-
-  // Set device as a Wi-Fi Station
-  WiFi.mode(WIFI_STA);
-
-  // Initialize ESP-NOW
-  if (esp_now_init() != ESP_OK) {
-    Serial.println("Error initializing ESP-NOW");
-    return;
-  }
-
-  // Register peer
-  esp_now_peer_info_t peerInfo;
-  memset(&peerInfo, 0, sizeof(peerInfo)); // Clear peerInfo structure
-  memcpy(peerInfo.peer_addr, peerAddress, 6);
-  peerInfo.channel = 0;  
-  peerInfo.encrypt = false;
-
-  // Add peer
-  if (esp_now_add_peer(&peerInfo) != ESP_OK) {
-    Serial.println("Failed to add peer");
-    return;
-  }
-}
-
-void loop() {
-  // Read temperature from DHT11 sensor
-  float temperature = dht.readTemperature();
-  
-  // Check if any reads failed
-  if (isnan(temperature)) {
-    Serial.println("Failed to read from DHT sensor!");
-    return;
-  }
-
-  // Prepare data to send
-  myData.temperature = temperature;
-
-  // Send message via ESP-NOW
-  esp_err_t result = esp_now_send(peerAddress, (uint8_t *) &myData, sizeof(myData));
-
-  if (result == ESP_OK) {
-    Serial.print("Temperature sent: ");
-    Serial.println(temperature);
-  } else {
-    Serial.println("Error sending the data");
-  }
-
-  delay(2000); // Send data every 2 seconds
-}
author	Pratik-Nemane	2024-08-13 17:31:47 +0530
committer	GitHub	2024-08-13 17:31:47 +0530
commit	33beb5610cf9c63ee5a9016376735baf480a45a5 (patch)
tree	11847fd87615e8fe227e63046f40e43f24d15c51
parent	56255cbc2ed57416e273596c32922aa35a3bc2ee (diff)
download	FLOSS-Arduino-Book-33beb5610cf9c63ee5a9016376735baf480a45a5.tar.gz FLOSS-Arduino-Book-33beb5610cf9c63ee5a9016376735baf480a45a5.tar.bz2 FLOSS-Arduino-Book-33beb5610cf9c63ee5a9016376735baf480a45a5.zip