ann.ipynb

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        "<a href=\"https://colab.research.google.com/github/rafaym1/Artificial-neural-network/blob/main/ann.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "QYR0uSTJ47Nr"
      },
      "outputs": [],
      "source": [
        "import keras\n",
        "import sklearn\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(\"diabetes.csv\") #to import data"
      ],
      "metadata": {
        "id": "fQoQXX8s8Y8p"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head() #to see data"
      ],
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          "height": 206
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        "id": "DVZiW3ex-69K",
        "outputId": "27034fbe-b325-4e8a-93c8-b6049d852410"
      },
      "execution_count": 5,
      "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  "
            ],
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              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
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              "      <th>4</th>\n",
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              "      <td>137</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "X=data.values[:,0:8]\n",
        "Y=data.values[:,8]"
      ],
      "metadata": {
        "id": "tY0VBuK1_RVQ"
      },
      "execution_count": 6,
      "outputs": []
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wnPiEkZMAvZA",
        "outputId": "7e8211eb-4a0b-4738-ab61-98771ea1da4e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  6.   , 148.   ,  72.   , ...,  33.6  ,   0.627,  50.   ],\n",
              "       [  1.   ,  85.   ,  66.   , ...,  26.6  ,   0.351,  31.   ],\n",
              "       [  8.   , 183.   ,  64.   , ...,  23.3  ,   0.672,  32.   ],\n",
              "       ...,\n",
              "       [  5.   , 121.   ,  72.   , ...,  26.2  ,   0.245,  30.   ],\n",
              "       [  1.   , 126.   ,  60.   , ...,  30.1  ,   0.349,  47.   ],\n",
              "       [  1.   ,  93.   ,  70.   , ...,  30.4  ,   0.315,  23.   ]])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n"
      ],
      "metadata": {
        "id": "ftvRNu_Q_g9m"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_TRAIN,X_TEST,Y_TRAIN,Y_TEST = train_test_split(X,Y,test_size = 0.2)"
      ],
      "metadata": {
        "id": "64SuNekxAB9J"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_TRAIN.shape #to know the number of training data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "flHz_7YFBBaV",
        "outputId": "78f39c1c-a0e8-4ddf-bbce-fbad5791e46d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(614, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Input,Dense\n",
        "from keras.models import Sequential"
      ],
      "metadata": {
        "id": "rAZVrhR3BL-z"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "#model = keras.models.Sequential()\n",
        "model.add(Input(shape=(8,)))\n",
        "model.add(Dense(86,activation=\"relu\"))\n",
        "model.add(Dense(64,activation=\"relu\"))\n",
        "model.add(Dense(32,activation=\"relu\"))\n",
        "model.add(Dense(12,activation=\"relu\"))\n",
        "model.add(Dense(1,activation=\"sigmoid\"))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "siZW8qmfBe38",
        "outputId": "3ae3a282-77cc-4589-e5bd-22e813ef9ddb"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 86)                774       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 64)                5568      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 12)                396       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 13        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8,831\n",
            "Trainable params: 8,831\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',loss = 'binary_crossentropy',metrics=[\"accuracy\"])\n",
        "history = model.fit(X_TRAIN,Y_TRAIN,epochs = 200)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3uq32mopXdM",
        "outputId": "ff2530a1-c372-45ba-f1c5-cc90c712fa3a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "20/20 [==============================] - 1s 2ms/step - loss: 2.1239 - accuracy: 0.5765\n",
            "Epoch 2/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6203 - accuracy: 0.6498\n",
            "Epoch 3/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6124 - accuracy: 0.6580\n",
            "Epoch 4/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6179 - accuracy: 0.6661\n",
            "Epoch 5/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6158 - accuracy: 0.6694\n",
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            "Epoch 182/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4042 - accuracy: 0.7980\n",
            "Epoch 183/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3929 - accuracy: 0.7948\n",
            "Epoch 184/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3870 - accuracy: 0.8143\n",
            "Epoch 185/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4161 - accuracy: 0.7948\n",
            "Epoch 186/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3887 - accuracy: 0.8176\n",
            "Epoch 187/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3997 - accuracy: 0.8111\n",
            "Epoch 188/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3997 - accuracy: 0.7850\n",
            "Epoch 189/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4050 - accuracy: 0.8013\n",
            "Epoch 190/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3810 - accuracy: 0.8192\n",
            "Epoch 191/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4253 - accuracy: 0.7801\n",
            "Epoch 192/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4026 - accuracy: 0.8029\n",
            "Epoch 193/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4325 - accuracy: 0.7850\n",
            "Epoch 194/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3898 - accuracy: 0.8192\n",
            "Epoch 195/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3736 - accuracy: 0.8176\n",
            "Epoch 196/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3807 - accuracy: 0.8111\n",
            "Epoch 197/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4043 - accuracy: 0.8029\n",
            "Epoch 198/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3969 - accuracy: 0.7980\n",
            "Epoch 199/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3755 - accuracy: 0.8160\n",
            "Epoch 200/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3875 - accuracy: 0.8046\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_TEST,Y_TEST)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_10rpDBrRol",
        "outputId": "6ee24cef-7547-4077-cb7f-995038f996ea"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 0s 2ms/step - loss: 0.7222 - accuracy: 0.7403\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.7221626043319702, 0.7402597665786743]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"model_file.h5\")"
      ],
      "metadata": {
        "id": "_UshLGKrrcjq"
      },
      "execution_count": 16,
      "outputs": []
    }
  ]
}