neural-nwrk.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z2UqfKcKPRYo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# preprocessed data - https://drive.google.com/file/d/16ENSqS7aM8-2Ry1aRxwczHd41y-sB5_u/view?usp=drive_link"
      ],
      "metadata": {
        "id": "JzuYMZgOOXXJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4VarGw7XN_so",
        "outputId": "4cc58f59-6bf9-438b-f0a2-c991edd1c5e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "20/20 [==============================] - 1s 2ms/step - loss: 0.6366 - accuracy: 0.6661\n",
            "Epoch 2/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5407 - accuracy: 0.7394\n",
            "Epoch 3/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4888 - accuracy: 0.7508\n",
            "Epoch 4/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4623 - accuracy: 0.7655\n",
            "Epoch 5/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4451 - accuracy: 0.7720\n",
            "Epoch 6/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4362 - accuracy: 0.7752\n",
            "Epoch 7/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4292 - accuracy: 0.7899\n",
            "Epoch 8/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4218 - accuracy: 0.7915\n",
            "Epoch 9/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4170 - accuracy: 0.7932\n",
            "Epoch 10/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4123 - accuracy: 0.8062\n",
            "Epoch 11/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4091 - accuracy: 0.8078\n",
            "Epoch 12/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4038 - accuracy: 0.8029\n",
            "Epoch 13/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4012 - accuracy: 0.8046\n",
            "Epoch 14/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3991 - accuracy: 0.7980\n",
            "Epoch 15/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3981 - accuracy: 0.8062\n",
            "Epoch 16/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3964 - accuracy: 0.8029\n",
            "Epoch 17/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3860 - accuracy: 0.8078\n",
            "Epoch 18/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3826 - accuracy: 0.8143\n",
            "Epoch 19/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3806 - accuracy: 0.8208\n",
            "Epoch 20/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3769 - accuracy: 0.8241\n",
            "Epoch 21/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3755 - accuracy: 0.8274\n",
            "Epoch 22/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3746 - accuracy: 0.8290\n",
            "Epoch 23/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3724 - accuracy: 0.8241\n",
            "Epoch 24/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3675 - accuracy: 0.8290\n",
            "Epoch 25/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3672 - accuracy: 0.8322\n",
            "Epoch 26/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3632 - accuracy: 0.8241\n",
            "Epoch 27/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3605 - accuracy: 0.8257\n",
            "Epoch 28/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3609 - accuracy: 0.8290\n",
            "Epoch 29/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3556 - accuracy: 0.8306\n",
            "Epoch 30/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3543 - accuracy: 0.8306\n",
            "Epoch 31/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3533 - accuracy: 0.8371\n",
            "Epoch 32/100\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.3477 - accuracy: 0.8322\n",
            "Epoch 33/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3522 - accuracy: 0.8290\n",
            "Epoch 34/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3419 - accuracy: 0.8306\n",
            "Epoch 35/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3401 - accuracy: 0.8453\n",
            "Epoch 36/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3396 - accuracy: 0.8420\n",
            "Epoch 37/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3375 - accuracy: 0.8339\n",
            "Epoch 38/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3349 - accuracy: 0.8420\n",
            "Epoch 39/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3307 - accuracy: 0.8469\n",
            "Epoch 40/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3418 - accuracy: 0.8469\n",
            "Epoch 41/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3323 - accuracy: 0.8371\n",
            "Epoch 42/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3246 - accuracy: 0.8502\n",
            "Epoch 43/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3194 - accuracy: 0.8518\n",
            "Epoch 44/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3188 - accuracy: 0.8534\n",
            "Epoch 45/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3165 - accuracy: 0.8567\n",
            "Epoch 46/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3144 - accuracy: 0.8550\n",
            "Epoch 47/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3162 - accuracy: 0.8485\n",
            "Epoch 48/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3082 - accuracy: 0.8469\n",
            "Epoch 49/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3093 - accuracy: 0.8550\n",
            "Epoch 50/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3047 - accuracy: 0.8583\n",
            "Epoch 51/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3039 - accuracy: 0.8550\n",
            "Epoch 52/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3014 - accuracy: 0.8599\n",
            "Epoch 53/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2954 - accuracy: 0.8632\n",
            "Epoch 54/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2980 - accuracy: 0.8648\n",
            "Epoch 55/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2922 - accuracy: 0.8713\n",
            "Epoch 56/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2907 - accuracy: 0.8632\n",
            "Epoch 57/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2903 - accuracy: 0.8583\n",
            "Epoch 58/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2860 - accuracy: 0.8664\n",
            "Epoch 59/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2813 - accuracy: 0.8713\n",
            "Epoch 60/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2810 - accuracy: 0.8762\n",
            "Epoch 61/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2754 - accuracy: 0.8713\n",
            "Epoch 62/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2743 - accuracy: 0.8730\n",
            "Epoch 63/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2713 - accuracy: 0.8779\n",
            "Epoch 64/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2717 - accuracy: 0.8844\n",
            "Epoch 65/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2700 - accuracy: 0.8844\n",
            "Epoch 66/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2611 - accuracy: 0.8876\n",
            "Epoch 67/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2641 - accuracy: 0.8827\n",
            "Epoch 68/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2578 - accuracy: 0.8876\n",
            "Epoch 69/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2536 - accuracy: 0.8974\n",
            "Epoch 70/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2564 - accuracy: 0.8990\n",
            "Epoch 71/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2510 - accuracy: 0.8974\n",
            "Epoch 72/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2523 - accuracy: 0.9055\n",
            "Epoch 73/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2450 - accuracy: 0.9039\n",
            "Epoch 74/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2477 - accuracy: 0.8974\n",
            "Epoch 75/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2426 - accuracy: 0.9007\n",
            "Epoch 76/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2380 - accuracy: 0.9023\n",
            "Epoch 77/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2398 - accuracy: 0.8974\n",
            "Epoch 78/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2396 - accuracy: 0.9007\n",
            "Epoch 79/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2305 - accuracy: 0.9121\n",
            "Epoch 80/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2295 - accuracy: 0.9121\n",
            "Epoch 81/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2288 - accuracy: 0.9023\n",
            "Epoch 82/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2241 - accuracy: 0.9121\n",
            "Epoch 83/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2312 - accuracy: 0.9039\n",
            "Epoch 84/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2194 - accuracy: 0.9104\n",
            "Epoch 85/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2237 - accuracy: 0.9104\n",
            "Epoch 86/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2169 - accuracy: 0.9169\n",
            "Epoch 87/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2165 - accuracy: 0.9104\n",
            "Epoch 88/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2106 - accuracy: 0.9267\n",
            "Epoch 89/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2078 - accuracy: 0.9186\n",
            "Epoch 90/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2062 - accuracy: 0.9202\n",
            "Epoch 91/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2016 - accuracy: 0.9235\n",
            "Epoch 92/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.1997 - accuracy: 0.9349\n",
            "Epoch 93/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.1977 - accuracy: 0.9316\n",
            "Epoch 94/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2004 - accuracy: 0.9283\n",
            "Epoch 95/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.1954 - accuracy: 0.9283\n",
            "Epoch 96/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.1924 - accuracy: 0.9349\n",
            "Epoch 97/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.1877 - accuracy: 0.9397\n",
            "Epoch 98/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.1896 - accuracy: 0.9332\n",
            "Epoch 99/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.1855 - accuracy: 0.9332\n",
            "Epoch 100/100\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.1849 - accuracy: 0.9381\n",
            "Accuracy: 0.7337662577629089\n",
            "1/1 [==============================] - 0s 87ms/step\n",
            "Prediction: [[1.]]\n",
            "1/1 [==============================] - 0s 58ms/step\n",
            "Prediction: [[1.]]\n",
            "Diabetic\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "# Load the diabetes dataset\n",
        "diabetes_data = pd.read_csv('/content/preprocessed_data.csv')  # Assuming 'diabetes.csv' is the dataset file name\n",
        "\n",
        "# Split the dataset into features (X) and target variable (y)\n",
        "X = diabetes_data.drop('Outcome', axis=1)  # Features are all columns except 'Outcome'\n",
        "y = diabetes_data['Outcome']  # Target variable is 'Outcome'\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",
        "# Here, 80% of the data is used for training and 20% for testing\n",
        "# random_state ensures reproducibility of the split\n",
        "\n",
        "# Standardize the features\n",
        "scaler = StandardScaler()  # StandardScaler is used to standardize features by removing the mean and scaling to unit variance\n",
        "X_train = scaler.fit_transform(X_train)  # Fit to data, then transform it\n",
        "X_test = scaler.transform(X_test)  # Scale features of X_test using the scaler fitted on X_train\n",
        "\n",
        "# Create an Artificial Neural Network (ANN) model\n",
        "model = Sequential()  # Sequential model is a linear stack of layers\n",
        "model.add(Dense(64, activation='relu', input_shape=(X_train.shape[1],)))  # Input layer with 64 neurons, 'relu' activation function, and input shape matching number of features\n",
        "model.add(Dense(64, activation='relu'))  # Hidden layer with 64 neurons and 'relu' activation function\n",
        "model.add(Dense(1, activation='sigmoid'))  # Output layer with 1 neuron and 'sigmoid' activation function\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "# 'binary_crossentropy' is used as the loss function for binary classification problems\n",
        "# 'adam' optimizer is used for optimizing the network parameters\n",
        "# 'accuracy' is used as the metric to evaluate the model's performance during training\n",
        "\n",
        "# Train the model on the training data\n",
        "model.fit(X_train, y_train, epochs=100, batch_size=32, verbose=1)\n",
        "# epochs: number of times the entire training dataset is passed forward and backward through the neural network\n",
        "# batch_size: number of samples propagated through the network before the model is updated\n",
        "\n",
        "# Evaluate the model on the test data\n",
        "_, accuracy = model.evaluate(X_test, y_test, verbose=0)\n",
        "print('Accuracy:', accuracy)\n",
        "\n",
        "# Predict on new data\n",
        "new_data = pd.DataFrame([[8, 183, 64, 0, 0,0, 23.3, 0.672, 32]], columns=X.columns)\n",
        "new_data_scaled = scaler.transform(new_data)\n",
        "prediction = model.predict(new_data_scaled)\n",
        "print('Prediction:', prediction)\n",
        "\n",
        "# Predict on another new data point\n",
        "new_data = pd.DataFrame([[5, 40, 444, 0, 0, 425.6, 0.201, 40,40]])\n",
        "prediction = model.predict(new_data)\n",
        "print('Prediction:', prediction)\n",
        "if prediction==1:\n",
        "  print(\"Diabetic\")\n",
        "else:\n",
        "    print(\"Not Diabetic\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "\n",
        "Accuracy: 0.7337662577629089\n",
        "1/1 [==============================] - 0s 87ms/step\n",
        "Prediction: [[1.]]\n",
        "1/1 [==============================] - 0s 58ms/step\n",
        "Prediction: [[1.]]\n",
        "Diabetic\n",
        "\n",
        "'''"
      ],
      "metadata": {
        "id": "lme89HSKPsIW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}