Diabetes_Data_Analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1LADeEnhOVyZQFssFK2Z57lBz6gUkXFXk",
      "authorship_tag": "ABX9TyPA8cWOBpuN8W//a1qHviE6",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/httperror505/Neural-Network/blob/colab/Diabetes_Data_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 784
        },
        "id": "fUI2-s2nLP50",
        "outputId": "cde4a288-d714-4f4f-91dc-1750c95126b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Preview:\n",
            "   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  \n",
            "\n",
            "Mean Squared Error: 9720.204300130148\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model saved successfully as 'linear_regression_model.pth'\n"
          ]
        }
      ],
      "source": [
        "# Import libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "# Load data from Google Drive\n",
        "url = '/content/drive/MyDrive/Datasets/diabetes.csv'\n",
        "df = pd.read_csv(url)\n",
        "\n",
        "# Display the first few rows of the dataset\n",
        "print(\"Dataset Preview:\")\n",
        "print(df.head())\n",
        "\n",
        "# Data organization (if needed)\n",
        "# Example: Assume columns 1-4 are features, and column 5 is the target variable\n",
        "X = df.iloc[:, :4]  # Features\n",
        "y = df.iloc[:, 4]   # Target variable\n",
        "\n",
        "# Split the data 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)\n",
        "\n",
        "# Linear Regression model\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(\"\\nMean Squared Error:\", mse)\n",
        "\n",
        "# Plot the predictions vs actual values\n",
        "plt.scatter(X_test.iloc[:, 0], y_test, color='black', label='Actual')\n",
        "plt.scatter(X_test.iloc[:, 0], y_pred, color='blue', label='Predicted')\n",
        "plt.xlabel('Feature 1')\n",
        "plt.ylabel('Target Variable')\n",
        "plt.legend()\n",
        "plt.title('Linear Regression Predictions vs Actual')\n",
        "plt.show()\n",
        "\n",
        "# Save the model to a .pth file\n",
        "torch.save(model, 'linear_regression_model.pth')\n",
        "print(\"\\nModel saved successfully as 'linear_regression_model.pth'\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import torch\n",
        "\n",
        "# Load data from Google Drive\n",
        "url = '/content/drive/MyDrive/Datasets/diabetes.csv'\n",
        "df = pd.read_csv(url)\n",
        "\n",
        "# Display the first few rows of the dataset\n",
        "print(\"Dataset Preview:\")\n",
        "print(df.head())\n",
        "\n",
        "# Data organization (if needed)\n",
        "# Example: Assume columns 1-8 are features, and column 9 is the target variable\n",
        "X = df.iloc[:, :8]  # Features\n",
        "y = df.iloc[:, 8]   # Target variable\n",
        "\n",
        "# Split the data 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)\n",
        "\n",
        "# Standardize features\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Logistic Regression model\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "\n",
        "# Save the model to a .pth file\n",
        "torch.save(model, 'logistic_regression_model.pth')\n",
        "print(\"\\nModel saved successfully as 'logistic_regression_model.pth'\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3PVlBfcLbp2",
        "outputId": "d589ba0c-2b8d-4330-e79b-166c238d47a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Preview:\n",
            "   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  \n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.80      0.81        99\n",
            "           1       0.65      0.67      0.66        55\n",
            "\n",
            "    accuracy                           0.75       154\n",
            "   macro avg       0.73      0.74      0.73       154\n",
            "weighted avg       0.76      0.75      0.75       154\n",
            "\n",
            "\n",
            "Confusion Matrix:\n",
            "[[79 20]\n",
            " [18 37]]\n",
            "\n",
            "Model saved successfully as 'logistic_regression_model.pth'\n"
          ]
        }
      ]
    }
  ]
}