Upload 5 files

projects/ML_DiabetesPrediction/Dataset.csv

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+1,93,70,31,0,30.4,0.315,23,0

projects/ML_DiabetesPrediction/Deploying_Machine_Learning_model_using_Streamlit.ipynb

@@ -0,0 +1,946 @@
+{
+  "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": "0f5f8129-eb57-4ba0-f329-312bba4aae27"
+      },
+      "source": [
+        "# printing the first 5 rows of the dataset\n",
+        "diabetes_dataset.head()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>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>"
+            ],
+            "text/plain": [
+              "   Pregnancies  Glucose  BloodPressure  ...  DiabetesPedigreeFunction  Age  Outcome\n",
+              "0            6      148             72  ...                     0.627   50        1\n",
+              "1            1       85             66  ...                     0.351   31        0\n",
+              "2            8      183             64  ...                     0.672   32        1\n",
+              "3            1       89             66  ...                     0.167   21        0\n",
+              "4            0      137             40  ...                     2.288   33        1\n",
+              "\n",
+              "[5 rows x 9 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lynParo6pEMB",
+        "outputId": "ab7d817a-1f20-46d0-d504-833efb433f7d"
+      },
+      "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": 300
+        },
+        "id": "3NDJOlrEpmoL",
+        "outputId": "12af9f8e-b5fb-4f7f-a4bb-f5df64cce508"
+      },
+      "source": [
+        "# getting the statistical measures of the data\n",
+        "diabetes_dataset.describe()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>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>"
+            ],
+            "text/plain": [
+              "       Pregnancies     Glucose  ...         Age     Outcome\n",
+              "count   768.000000  768.000000  ...  768.000000  768.000000\n",
+              "mean      3.845052  120.894531  ...   33.240885    0.348958\n",
+              "std       3.369578   31.972618  ...   11.760232    0.476951\n",
+              "min       0.000000    0.000000  ...   21.000000    0.000000\n",
+              "25%       1.000000   99.000000  ...   24.000000    0.000000\n",
+              "50%       3.000000  117.000000  ...   29.000000    0.000000\n",
+              "75%       6.000000  140.250000  ...   41.000000    1.000000\n",
+              "max      17.000000  199.000000  ...   81.000000    1.000000\n",
+              "\n",
+              "[8 rows x 9 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LrpHzaGpp5dQ",
+        "outputId": "916953df-2cee-43a9-cc80-2e58fe6b43d2"
+      },
+      "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": 143
+        },
+        "id": "I6MWR0k_qSCK",
+        "outputId": "47b23d5c-8973-4868-8582-b0fa95bfed46"
+      },
+      "source": [
+        "diabetes_dataset.groupby('Outcome').mean()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>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>"
+            ],
+            "text/plain": [
+              "         Pregnancies     Glucose  ...  DiabetesPedigreeFunction        Age\n",
+              "Outcome                           ...                                     \n",
+              "0           3.298000  109.980000  ...                  0.429734  31.190000\n",
+              "1           4.865672  141.257463  ...                  0.550500  37.067164\n",
+              "\n",
+              "[2 rows x 8 columns]"
+            ]
+          },
+          "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": "552c0851-90ec-4068-812d-c848224be8a7"
+      },
+      "source": [
+        "print(X)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "     Pregnancies  Glucose  BloodPressure  ...   BMI  DiabetesPedigreeFunction  Age\n",
+            "0              6      148             72  ...  33.6                     0.627   50\n",
+            "1              1       85             66  ...  26.6                     0.351   31\n",
+            "2              8      183             64  ...  23.3                     0.672   32\n",
+            "3              1       89             66  ...  28.1                     0.167   21\n",
+            "4              0      137             40  ...  43.1                     2.288   33\n",
+            "..           ...      ...            ...  ...   ...                       ...  ...\n",
+            "763           10      101             76  ...  32.9                     0.171   63\n",
+            "764            2      122             70  ...  36.8                     0.340   27\n",
+            "765            5      121             72  ...  26.2                     0.245   30\n",
+            "766            1      126             60  ...  30.1                     0.349   47\n",
+            "767            1       93             70  ...  30.4                     0.315   23\n",
+            "\n",
+            "[768 rows x 8 columns]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AoxgTJAMrcCl",
+        "outputId": "d6f83516-18e5-41ca-c6ce-4495bdf733cb"
+      },
+      "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": "2b7c195d-58d7-4c4d-803d-34e09791b07a"
+      },
+      "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/"
+        },
+        "id": "ncJWY_7suPAb",
+        "outputId": "1a8fc42b-37a5-4e59-d52a-5dd5e09560e8"
+      },
+      "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')"
+            ]
+          },
+          "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": "1b1c3d32-b9f2-40c0-89ed-5d59b674cdfe"
+      },
+      "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": "a65c3281-1621-4c8f-b57f-bbf0bc81d129"
+      },
+      "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": "63b3fd00-f094-4642-b45e-3eb21331c3df"
+      },
+      "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/python3.7/dist-packages/sklearn/base.py:446: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
+            "  \"X does not have valid feature names, but\"\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": "abd39207-0fea-4c68-e91b-710244c8e73d"
+      },
+      "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/python3.7/dist-packages/sklearn/base.py:446: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
+            "  \"X does not have valid feature names, but\"\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iGRhGvgfRkvm"
+      },
+      "source": [],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

projects/ML_DiabetesPrediction/ML_DiabetesPrediction.py

@@ -0,0 +1,34 @@
+from projects.ML_StudentPerformance.src.pipelines.predict_pipeline import CustomData, PredictPipeline
+from pydantic import BaseModel
+
+# Function to handle the prediction logic
+def predict_student_performance(data):
+    # Convert the incoming form data to a DataFrame
+    pred_df = data.get_data_as_dataframe()
+
+    # Initialize the prediction pipeline
+    predict_pipeline = PredictPipeline()
+    results = predict_pipeline.predict(pred_df)
+
+    return results[0]  # Return the first prediction result
+
+# Function to handle form data conversion
+def create_custom_data(gender, ethnicity, parental_level_of_education, lunch, test_preparation_course, reading_score, writing_score):
+    return CustomData(
+        gender=gender,
+        race_ethnicity=ethnicity,
+        parental_level_of_education=parental_level_of_education,
+        lunch=lunch,
+        test_preparation_course=test_preparation_course,
+        reading_score=float(reading_score),
+        writing_score=float(writing_score)
+    )
+
+class form1(BaseModel):
+    gender: str
+    ethnicity: str
+    parental_level_of_education: str
+    lunch: str
+    test_preparation_course: str
+    reading_score: float
+    writing_score: float

projects/ML_DiabetesPrediction/classifier.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:307e2ae2e82e89efe5d58987f2f395fe0e792aebd3d760e6e660313add623cdc
+size 27634