sigmoid_classification.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from keras.layers import Dense\n",
        "from keras.models import Sequential\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/diabetes.csv')"
      ],
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        "id": "1XmwgW43Ehzq"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()"
      ],
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          "height": 206
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            "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",
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              "      <td>43.1</td>\n",
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      "source": [
        "X=df.drop(columns='Outcome')\n",
        "y=df['Outcome']"
      ],
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        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=101)"
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        "model=Sequential()\n",
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          "text": [
            "Epoch 1/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 34.7411 - accuracy: 0.5977\n",
            "Epoch 2/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 32.4025 - accuracy: 0.5847\n",
            "Epoch 3/250\n",
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            "Epoch 4/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 28.5287 - accuracy: 0.5521\n",
            "Epoch 5/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 26.9861 - accuracy: 0.5423\n",
            "Epoch 6/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 25.8810 - accuracy: 0.5309\n",
            "Epoch 7/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 24.9286 - accuracy: 0.5163\n",
            "Epoch 8/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 24.0871 - accuracy: 0.5130\n",
            "Epoch 9/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 23.3101 - accuracy: 0.5081\n",
            "Epoch 10/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 22.6516 - accuracy: 0.5016\n",
            "Epoch 11/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 21.8986 - accuracy: 0.4919\n",
            "Epoch 12/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 21.2776 - accuracy: 0.4951\n",
            "Epoch 13/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 20.5998 - accuracy: 0.4935\n",
            "Epoch 14/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 19.9514 - accuracy: 0.4951\n",
            "Epoch 15/250\n",
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            "Epoch 16/250\n",
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            "Epoch 17/250\n",
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            "Epoch 18/250\n",
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            "Epoch 19/250\n",
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            "Epoch 20/250\n",
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            "Epoch 21/250\n",
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            "Epoch 22/250\n",
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            "Epoch 24/250\n",
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            "Epoch 25/250\n",
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            "Epoch 26/250\n",
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            "Epoch 27/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 11.8622 - accuracy: 0.4495\n",
            "Epoch 28/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 11.2982 - accuracy: 0.4479\n",
            "Epoch 29/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 10.7356 - accuracy: 0.4381\n",
            "Epoch 30/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 10.2256 - accuracy: 0.4332\n",
            "Epoch 31/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 9.7067 - accuracy: 0.4316\n",
            "Epoch 32/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 9.2126 - accuracy: 0.4267\n",
            "Epoch 33/250\n",
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            "Epoch 34/250\n",
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            "Epoch 35/250\n",
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            "Epoch 38/250\n",
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            "Epoch 39/250\n",
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            "Epoch 40/250\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 6.2580 - accuracy: 0.4104\n",
            "Epoch 41/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 6.0415 - accuracy: 0.4072\n",
            "Epoch 42/250\n",
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            "Epoch 43/250\n",
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            "Epoch 64/250\n",
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            "20/20 [==============================] - 0s 2ms/step - loss: 3.0142 - accuracy: 0.4658\n",
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            "20/20 [==============================] - 0s 2ms/step - loss: 2.9285 - accuracy: 0.4756\n",
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            "20/20 [==============================] - 0s 2ms/step - loss: 2.6720 - accuracy: 0.4674\n",
            "Epoch 73/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 2.5951 - accuracy: 0.4772\n",
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            "Epoch 75/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 2.4428 - accuracy: 0.4853\n",
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            "Epoch 77/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 2.2976 - accuracy: 0.4935\n",
            "Epoch 78/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 2.2365 - accuracy: 0.4919\n",
            "Epoch 79/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 2.1857 - accuracy: 0.4837\n",
            "Epoch 80/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 2.0982 - accuracy: 0.5212\n",
            "Epoch 81/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 2.0449 - accuracy: 0.5195\n",
            "Epoch 82/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 1.9806 - accuracy: 0.5114\n",
            "Epoch 83/250\n",
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            "Epoch 84/250\n",
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            "Epoch 248/250\n",
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            "Epoch 249/250\n",
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            "Epoch 250/250\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5705 - accuracy: 0.7166\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fb25d669a90>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=model.predict(X_test)\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBiGDly4FAK1",
        "outputId": "c00cd6ed-0b2e-4899-c147-1893d5e2d4a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.28952318],\n",
              "       [0.63914603],\n",
              "       [0.23085678],\n",
              "       [0.41426882],\n",
              "       [0.18703723],\n",
              "       [0.82921237],\n",
              "       [0.5545289 ],\n",
              "       [0.108219  ],\n",
              "       [0.24563748],\n",
              "       [0.23932198],\n",
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          },
          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(predictions)):\n",
        "  if predictions[i]>0.5:\n",
        "    predictions[i]=1.0\n",
        "  else:\n",
        "    predictions[i]=0.0\n",
        "\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
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        "id": "j0fN5iDFFCle",
        "outputId": "3db3f884-f0cf-4762-d313-c7d35c0892d7"
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      "execution_count": null,
      "outputs": [
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          "data": {
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(accuracy_score(y_test,predictions)*100,'%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E9qRafBFFElY",
        "outputId": "6b034525-9142-4887-da06-2edc3040cd22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "75.32467532467533 %\n"
          ]
        }
      ]
    }
  ]
}