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Diabetes Prediction Web App.py

@@ -0,0 +1,75 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jun 24 23:11:36 2023
+
+@author: rishabhsharma
+"""
+
+import numpy as np
+import pickle
+import streamlit as st
+
+
+# loading the saved model
+loaded_model = pickle.load(open('/Users/rishabhsharma/Desktop/Diabetes Prediction/trained_model.sav', 'rb'))
+
+
+# creating a function for Prediction
+
+def diabetes_prediction(input_data):
+    
+
+    # changing the input_data to numpy array
+    input_data_as_numpy_array = np.asarray(input_data)
+
+    # reshape the array as we are predicting for one instance
+    input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
+
+    prediction = loaded_model.predict(input_data_reshaped)
+    print(prediction)
+
+    if (prediction[0] == 0):
+      return 'The person is not diabetic'
+    else:
+      return 'The person is diabetic'
+  
+    
+  
+def main():
+    
+    
+    # giving a title
+    st.title('Diabetes Prediction Web App')
+    
+    
+    # getting the input data from the user
+    
+    
+    Pregnancies = st.text_input('Number of Pregnancies')
+    Glucose = st.text_input('Glucose Level')
+    BloodPressure = st.text_input('Blood Pressure value')
+    SkinThickness = st.text_input('Skin Thickness value')
+    Insulin = st.text_input('Insulin Level')
+    BMI = st.text_input('BMI value')
+    DiabetesPedigreeFunction = st.text_input('Diabetes Pedigree Function value')
+    Age = st.text_input('Age of the Person')
+    
+    
+    # code for Prediction
+    diagnosis = ''
+    
+    # creating a button for Prediction
+    
+    if st.button('Diabetes Test Result'):
+        diagnosis = diabetes_prediction([Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age])
+        
+        
+    st.success(diagnosis)
+    
+    
+    
+    
+    
+if __name__ == '__main__':
+    main()

Diabetes_Predicition.ipynb

@@ -0,0 +1,1216 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LnPbntVRnfvV"
+      },
+      "source": [
+        "Importing the Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-71UtHzNVWjB"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn import svm\n",
+        "from sklearn.metrics import accuracy_score"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bmfOfG8joBBy"
+      },
+      "source": [
+        "Data Collection and Analysis\n",
+        "\n",
+        "PIMA Diabetes Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Xpw6Mj_pn_TL"
+      },
+      "source": [
+        "# loading the diabetes dataset to a pandas DataFrame\n",
+        "diabetes_dataset = pd.read_csv('/content/diabetes.csv')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "-tjO09ncovoh",
+        "outputId": "4dd3939d-9cc2-4f80-cf6d-88dd95d02c2e"
+      },
+      "source": [
+        "# printing the first 5 rows of the dataset\n",
+        "diabetes_dataset.head()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+              "0            6      148             72             35        0  33.6   \n",
+              "1            1       85             66             29        0  26.6   \n",
+              "2            8      183             64              0        0  23.3   \n",
+              "3            1       89             66             23       94  28.1   \n",
+              "4            0      137             40             35      168  43.1   \n",
+              "\n",
+              "   DiabetesPedigreeFunction  Age  Outcome  \n",
+              "0                     0.627   50        1  \n",
+              "1                     0.351   31        0  \n",
+              "2                     0.672   32        1  \n",
+              "3                     0.167   21        0  \n",
+              "4                     2.288   33        1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-039a6e3c-7e3d-4d2e-b59d-1cb24047d0b7\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "    .dataframe tbody tr th {\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Pregnancies</th>\n",
+              "      <th>Glucose</th>\n",
+              "      <th>BloodPressure</th>\n",
+              "      <th>SkinThickness</th>\n",
+              "      <th>Insulin</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>DiabetesPedigreeFunction</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Outcome</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>6</td>\n",
+              "      <td>148</td>\n",
+              "      <td>72</td>\n",
+              "      <td>35</td>\n",
+              "      <td>0</td>\n",
+              "      <td>33.6</td>\n",
+              "      <td>0.627</td>\n",
+              "      <td>50</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>85</td>\n",
+              "      <td>66</td>\n",
+              "      <td>29</td>\n",
+              "      <td>0</td>\n",
+              "      <td>26.6</td>\n",
+              "      <td>0.351</td>\n",
+              "      <td>31</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>8</td>\n",
+              "      <td>183</td>\n",
+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>23.3</td>\n",
+              "      <td>0.672</td>\n",
+              "      <td>32</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>89</td>\n",
+              "      <td>66</td>\n",
+              "      <td>23</td>\n",
+              "      <td>94</td>\n",
+              "      <td>28.1</td>\n",
+              "      <td>0.167</td>\n",
+              "      <td>21</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>137</td>\n",
+              "      <td>40</td>\n",
+              "      <td>35</td>\n",
+              "      <td>168</td>\n",
+              "      <td>43.1</td>\n",
+              "      <td>2.288</td>\n",
+              "      <td>33</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-039a6e3c-7e3d-4d2e-b59d-1cb24047d0b7')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
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+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-039a6e3c-7e3d-4d2e-b59d-1cb24047d0b7 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-039a6e3c-7e3d-4d2e-b59d-1cb24047d0b7');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
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+              "          element.appendChild(docLink);\n",
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+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lynParo6pEMB",
+        "outputId": "8d134bf4-ed17-4ee5-9cbe-48d88cdd4495"
+      },
+      "source": [
+        "# number of rows and Columns in this dataset\n",
+        "diabetes_dataset.shape"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(768, 9)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 364
+        },
+        "id": "3NDJOlrEpmoL",
+        "outputId": "7a404a6f-955b-4c04-fbe4-8634869eaf8f"
+      },
+      "source": [
+        "# getting the statistical measures of the data\n",
+        "diabetes_dataset.describe()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
+              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
+              "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
+              "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
+              "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
+              "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
+              "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
+              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
+              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
+              "\n",
+              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
+              "count  768.000000                768.000000  768.000000  768.000000  \n",
+              "mean    31.992578                  0.471876   33.240885    0.348958  \n",
+              "std      7.884160                  0.331329   11.760232    0.476951  \n",
+              "min      0.000000                  0.078000   21.000000    0.000000  \n",
+              "25%     27.300000                  0.243750   24.000000    0.000000  \n",
+              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
+              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
+              "max     67.100000                  2.420000   81.000000    1.000000  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7e6e1f73-08b2-436f-8bd1-cee4cb79b0e4\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\n",
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+              "        text-align: right;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Pregnancies</th>\n",
+              "      <th>Glucose</th>\n",
+              "      <th>BloodPressure</th>\n",
+              "      <th>SkinThickness</th>\n",
+              "      <th>Insulin</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>DiabetesPedigreeFunction</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Outcome</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>3.845052</td>\n",
+              "      <td>120.894531</td>\n",
+              "      <td>69.105469</td>\n",
+              "      <td>20.536458</td>\n",
+              "      <td>79.799479</td>\n",
+              "      <td>31.992578</td>\n",
+              "      <td>0.471876</td>\n",
+              "      <td>33.240885</td>\n",
+              "      <td>0.348958</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>3.369578</td>\n",
+              "      <td>31.972618</td>\n",
+              "      <td>19.355807</td>\n",
+              "      <td>15.952218</td>\n",
+              "      <td>115.244002</td>\n",
+              "      <td>7.884160</td>\n",
+              "      <td>0.331329</td>\n",
+              "      <td>11.760232</td>\n",
+              "      <td>0.476951</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.078000</td>\n",
+              "      <td>21.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>99.000000</td>\n",
+              "      <td>62.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>27.300000</td>\n",
+              "      <td>0.243750</td>\n",
+              "      <td>24.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>117.000000</td>\n",
+              "      <td>72.000000</td>\n",
+              "      <td>23.000000</td>\n",
+              "      <td>30.500000</td>\n",
+              "      <td>32.000000</td>\n",
+              "      <td>0.372500</td>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>6.000000</td>\n",
+              "      <td>140.250000</td>\n",
+              "      <td>80.000000</td>\n",
+              "      <td>32.000000</td>\n",
+              "      <td>127.250000</td>\n",
+              "      <td>36.600000</td>\n",
+              "      <td>0.626250</td>\n",
+              "      <td>41.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>17.000000</td>\n",
+              "      <td>199.000000</td>\n",
+              "      <td>122.000000</td>\n",
+              "      <td>99.000000</td>\n",
+              "      <td>846.000000</td>\n",
+              "      <td>67.100000</td>\n",
+              "      <td>2.420000</td>\n",
+              "      <td>81.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e6e1f73-08b2-436f-8bd1-cee4cb79b0e4')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-7e6e1f73-08b2-436f-8bd1-cee4cb79b0e4 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-7e6e1f73-08b2-436f-8bd1-cee4cb79b0e4');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LrpHzaGpp5dQ",
+        "outputId": "ccc5292b-de17-4fd0-e7a9-ecd379a08404"
+      },
+      "source": [
+        "diabetes_dataset['Outcome'].value_counts()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    500\n",
+              "1    268\n",
+              "Name: Outcome, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cB1qRaNcqeh5"
+      },
+      "source": [
+        "0 --> Non-Diabetic\n",
+        "\n",
+        "1 --> Diabetic"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "id": "I6MWR0k_qSCK",
+        "outputId": "2ac9729d-c4ce-4866-d886-5a2da81ba7dd"
+      },
+      "source": [
+        "diabetes_dataset.groupby('Outcome').mean()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
+              "Outcome                                                                      \n",
+              "0           3.298000  109.980000      68.184000      19.664000   68.792000   \n",
+              "1           4.865672  141.257463      70.824627      22.164179  100.335821   \n",
+              "\n",
+              "               BMI  DiabetesPedigreeFunction        Age  \n",
+              "Outcome                                                  \n",
+              "0        30.304200                  0.429734  31.190000  \n",
+              "1        35.142537                  0.550500  37.067164  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-98fb70f3-cf03-414d-87c7-1dea5a48b28f\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Pregnancies</th>\n",
+              "      <th>Glucose</th>\n",
+              "      <th>BloodPressure</th>\n",
+              "      <th>SkinThickness</th>\n",
+              "      <th>Insulin</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>DiabetesPedigreeFunction</th>\n",
+              "      <th>Age</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Outcome</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>3.298000</td>\n",
+              "      <td>109.980000</td>\n",
+              "      <td>68.184000</td>\n",
+              "      <td>19.664000</td>\n",
+              "      <td>68.792000</td>\n",
+              "      <td>30.304200</td>\n",
+              "      <td>0.429734</td>\n",
+              "      <td>31.190000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>4.865672</td>\n",
+              "      <td>141.257463</td>\n",
+              "      <td>70.824627</td>\n",
+              "      <td>22.164179</td>\n",
+              "      <td>100.335821</td>\n",
+              "      <td>35.142537</td>\n",
+              "      <td>0.550500</td>\n",
+              "      <td>37.067164</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98fb70f3-cf03-414d-87c7-1dea5a48b28f')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-98fb70f3-cf03-414d-87c7-1dea5a48b28f button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-98fb70f3-cf03-414d-87c7-1dea5a48b28f');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "RoDW7l9mqqHZ"
+      },
+      "source": [
+        "# separating the data and labels\n",
+        "X = diabetes_dataset.drop(columns = 'Outcome', axis=1)\n",
+        "Y = diabetes_dataset['Outcome']"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3eiRW9M9raMm",
+        "outputId": "a4dbd160-65e3-4f7f-f65e-e089695ad3b9"
+      },
+      "source": [
+        "print(X)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+            "0              6      148             72             35        0  33.6   \n",
+            "1              1       85             66             29        0  26.6   \n",
+            "2              8      183             64              0        0  23.3   \n",
+            "3              1       89             66             23       94  28.1   \n",
+            "4              0      137             40             35      168  43.1   \n",
+            "..           ...      ...            ...            ...      ...   ...   \n",
+            "763           10      101             76             48      180  32.9   \n",
+            "764            2      122             70             27        0  36.8   \n",
+            "765            5      121             72             23      112  26.2   \n",
+            "766            1      126             60              0        0  30.1   \n",
+            "767            1       93             70             31        0  30.4   \n",
+            "\n",
+            "     DiabetesPedigreeFunction  Age  \n",
+            "0                       0.627   50  \n",
+            "1                       0.351   31  \n",
+            "2                       0.672   32  \n",
+            "3                       0.167   21  \n",
+            "4                       2.288   33  \n",
+            "..                        ...  ...  \n",
+            "763                     0.171   63  \n",
+            "764                     0.340   27  \n",
+            "765                     0.245   30  \n",
+            "766                     0.349   47  \n",
+            "767                     0.315   23  \n",
+            "\n",
+            "[768 rows x 8 columns]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AoxgTJAMrcCl",
+        "outputId": "a76a9089-12b4-4319-da60-0bfc7c638ad0"
+      },
+      "source": [
+        "print(Y)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0      1\n",
+            "1      0\n",
+            "2      1\n",
+            "3      0\n",
+            "4      1\n",
+            "      ..\n",
+            "763    0\n",
+            "764    0\n",
+            "765    0\n",
+            "766    1\n",
+            "767    0\n",
+            "Name: Outcome, Length: 768, dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gHciEFkxsoQP"
+      },
+      "source": [
+        "Train Test Split"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AEfKGj_yslvD"
+      },
+      "source": [
+        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, stratify=Y, random_state=2)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DR05T-o0t3FQ",
+        "outputId": "24f5b16d-a500-49ca-de75-6503b41528d5"
+      },
+      "source": [
+        "print(X.shape, X_train.shape, X_test.shape)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(768, 8) (614, 8) (154, 8)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ElJ3tkOtuC_n"
+      },
+      "source": [
+        "Training the Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5szLWHlNt9xc"
+      },
+      "source": [
+        "classifier = svm.SVC(kernel='linear')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "ncJWY_7suPAb",
+        "outputId": "e6e9a274-acb9-4d42-f0e0-f5c37e378f8a"
+      },
+      "source": [
+        "#training the support vector Machine Classifier\n",
+        "classifier.fit(X_train, Y_train)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SVC(kernel='linear')"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UV4-CAfquiyP"
+      },
+      "source": [
+        "Model Evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yhAjGPJWunXa"
+      },
+      "source": [
+        "Accuracy Score"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fJLEPQK7ueXp"
+      },
+      "source": [
+        "# accuracy score on the training data\n",
+        "X_train_prediction = classifier.predict(X_train)\n",
+        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mmJ22qhVvNwj",
+        "outputId": "7540f8ca-5527-4612-d5cd-8746d711220e"
+      },
+      "source": [
+        "print('Accuracy score of the training data : ', training_data_accuracy)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy score of the training data :  0.7833876221498371\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "G2CICFMEvcCl"
+      },
+      "source": [
+        "# accuracy score on the test data\n",
+        "X_test_prediction = classifier.predict(X_test)\n",
+        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "i2GcW_t_vz7C",
+        "outputId": "e2b18fd9-f005-42fa-9444-81e8eb57d947"
+      },
+      "source": [
+        "print('Accuracy score of the test data : ', test_data_accuracy)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy score of the test data :  0.7727272727272727\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gq8ZX1xpwPF5"
+      },
+      "source": [
+        "Making a Predictive System"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "U-ULRe4yv5tH",
+        "outputId": "c218e6cf-ac30-4246-9bc6-cc09ac9d81ae"
+      },
+      "source": [
+        "input_data = (5,166,72,19,175,25.8,0.587,51)\n",
+        "\n",
+        "# changing the input_data to numpy array\n",
+        "input_data_as_numpy_array = np.asarray(input_data)\n",
+        "\n",
+        "# reshape the array as we are predicting for one instance\n",
+        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
+        "\n",
+        "prediction = classifier.predict(input_data_reshaped)\n",
+        "print(prediction)\n",
+        "\n",
+        "if (prediction[0] == 0):\n",
+        "  print('The person is not diabetic')\n",
+        "else:\n",
+        "  print('The person is diabetic')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[1]\n",
+            "The person is diabetic\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vgL6wblpQUtX"
+      },
+      "source": [
+        "Saving the trained model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Nn60MdxByjgz"
+      },
+      "source": [
+        "import pickle"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cWzPQs4mQZN_"
+      },
+      "source": [
+        "filename = 'trained_model.sav'\n",
+        "pickle.dump(classifier, open(filename, 'wb'))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Wk1T2sMcQ6_U"
+      },
+      "source": [
+        "# loading the saved model\n",
+        "loaded_model = pickle.load(open('trained_model.sav', 'rb'))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Bd5OpxHnRPyy",
+        "outputId": "daa664c6-683c-4ac6-986d-46654598fac6"
+      },
+      "source": [
+        "input_data = (5,166,72,19,175,25.8,0.587,51)\n",
+        "\n",
+        "# changing the input_data to numpy array\n",
+        "input_data_as_numpy_array = np.asarray(input_data)\n",
+        "\n",
+        "# reshape the array as we are predicting for one instance\n",
+        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
+        "\n",
+        "prediction = loaded_model.predict(input_data_reshaped)\n",
+        "print(prediction)\n",
+        "\n",
+        "if (prediction[0] == 0):\n",
+        "  print('The person is not diabetic')\n",
+        "else:\n",
+        "  print('The person is diabetic')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[1]\n",
+            "The person is diabetic\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iGRhGvgfRkvm"
+      },
+      "source": [],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Predicitive System.py

@@ -0,0 +1,22 @@
+import numpy as np
+import pickle
+
+# loading the saved model
+loaded_model = pickle.load(open('/Users/rishabhsharma/Desktop/Diabetes Prediction/trained_model.sav', 'rb'))
+
+
+input_data = (5,166,72,19,175,25.8,0.587,51)
+
+# changing the input_data to numpy array
+input_data_as_numpy_array = np.asarray(input_data)
+
+# reshape the array as we are predicting for one instance
+input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
+
+prediction = loaded_model.predict(input_data_reshaped)
+print(prediction)
+
+if (prediction[0] == 0):
+  print('The person is not diabetic')
+else:
+  print('The person is diabetic')

diabetes_predicition.py

@@ -0,0 +1,125 @@
+# -*- coding: utf-8 -*-
+"""Diabetes Predicition.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1aNMlOsS2sOTF_m50QYOm5pAz-UmbD4_u
+
+Importing the Dependencies
+"""
+
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn import svm
+from sklearn.metrics import accuracy_score
+
+"""Data Collection and Analysis
+
+PIMA Diabetes Dataset
+"""
+
+# loading the diabetes dataset to a pandas DataFrame
+diabetes_dataset = pd.read_csv('/content/diabetes.csv')
+
+# printing the first 5 rows of the dataset
+diabetes_dataset.head()
+
+# number of rows and Columns in this dataset
+diabetes_dataset.shape
+
+# getting the statistical measures of the data
+diabetes_dataset.describe()
+
+diabetes_dataset['Outcome'].value_counts()
+
+"""0 --> Non-Diabetic
+
+1 --> Diabetic
+"""
+
+diabetes_dataset.groupby('Outcome').mean()
+
+# separating the data and labels
+X = diabetes_dataset.drop(columns = 'Outcome', axis=1)
+Y = diabetes_dataset['Outcome']
+
+print(X)
+
+print(Y)
+
+"""Train Test Split"""
+
+X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, stratify=Y, random_state=2)
+
+print(X.shape, X_train.shape, X_test.shape)
+
+"""Training the Model"""
+
+classifier = svm.SVC(kernel='linear')
+
+#training the support vector Machine Classifier
+classifier.fit(X_train, Y_train)
+
+"""Model Evaluation
+
+Accuracy Score
+"""
+
+# accuracy score on the training data
+X_train_prediction = classifier.predict(X_train)
+training_data_accuracy = accuracy_score(X_train_prediction, Y_train)
+
+print('Accuracy score of the training data : ', training_data_accuracy)
+
+# accuracy score on the test data
+X_test_prediction = classifier.predict(X_test)
+test_data_accuracy = accuracy_score(X_test_prediction, Y_test)
+
+print('Accuracy score of the test data : ', test_data_accuracy)
+
+"""Making a Predictive System"""
+
+input_data = (5,166,72,19,175,25.8,0.587,51)
+
+# changing the input_data to numpy array
+input_data_as_numpy_array = np.asarray(input_data)
+
+# reshape the array as we are predicting for one instance
+input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
+
+prediction = classifier.predict(input_data_reshaped)
+print(prediction)
+
+if (prediction[0] == 0):
+  print('The person is not diabetic')
+else:
+  print('The person is diabetic')
+
+"""Saving the trained model"""
+
+import pickle
+
+filename = 'trained_model.sav'
+pickle.dump(classifier, open(filename, 'wb'))
+
+# loading the saved model
+loaded_model = pickle.load(open('trained_model.sav', 'rb'))
+
+input_data = (5,166,72,19,175,25.8,0.587,51)
+
+# changing the input_data to numpy array
+input_data_as_numpy_array = np.asarray(input_data)
+
+# reshape the array as we are predicting for one instance
+input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
+
+prediction = loaded_model.predict(input_data_reshaped)
+print(prediction)
+
+if (prediction[0] == 0):
+  print('The person is not diabetic')
+else:
+  print('The person is diabetic')
+