model/Diabetes_Prediction_using_feed_forward_neural_network.ipynb

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        "id": "yb0xtIg2yWWm",
        "outputId": "299cf782-1310-40c0-8fce-610ec1da979a"
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      "outputs": [
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.8/dist-packages (0.11.2)\n",
            "Requirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.8/dist-packages (from seaborn) (3.2.2)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.8/dist-packages (from seaborn) (1.3.5)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.8/dist-packages (from seaborn) (1.7.3)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.8/dist-packages (from seaborn) (1.21.6)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.23->seaborn) (2022.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib>=2.2->seaborn) (1.15.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install seaborn"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "f1ed1c73-9f61-41ff-d2cc-17435cf6f9fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "#Mounting google drive to google colab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ku6JabzHDwkq",
        "outputId": "91082adf-6c0e-4af3-98e7-8a6557ea70bb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.8/dist-packages (0.10.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn) (1.2.0)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn) (1.0.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn) (1.7.3)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn) (1.21.6)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn) (3.1.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install -U imbalanced-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b2eEwflcMYV-"
      },
      "outputs": [],
      "source": [
        "#Importing dependencies\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "import tensorflow as tf\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZByH5_ucMwLj"
      },
      "outputs": [],
      "source": [
        "# load data to pandas dataframe\n",
        "diabetes_df = pd.read_csv('/content/drive/My Drive/diabetes.csv')"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "lxFvI_uuPGH5",
        "outputId": "e949cfc5-305e-4299-8c85-f1a99e28f3d5"
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              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "129            0      105             84              0        0  27.9   \n",
              "322            0      124             70             20        0  27.4   \n",
              "460            9      120             72             22       56  20.8   \n",
              "181            0      119             64             18       92  34.9   \n",
              "705            6       80             80             36        0  39.8   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  Outcome  \n",
              "129                     0.741   62        1  \n",
              "322                     0.254   36        1  \n",
              "460                     0.733   48        0  \n",
              "181                     0.725   23        0  \n",
              "705                     0.177   28        0  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
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        }
      ],
      "source": [
        "#checking for random data samples\n",
        "diabetes_df.sample(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
              "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "diabetes_df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of patients with Diabetes : 268\n",
            "Number of patients without Diabetes : 500\n"
          ]
        }
      ],
      "source": [
        "print(f'Number of patients with Diabetes : {len(diabetes_df[diabetes_df[\"Outcome\"]==1])}')\n",
        "print(f'Number of patients without Diabetes : {len(diabetes_df[diabetes_df[\"Outcome\"]==0])}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ge39SZCUP4r4"
      },
      "source": [
        "The data is quite unbalanced given that the number og patients without diabetes is double than the ones with diabetes. This might skew the data in favor of class with higher number of samples. This needs to be balanced later in order for the model to learn the right pattrns from them"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Data Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We want to compare histogram distribution to how many actual values are present in the dataset.\n",
        "We set the `density` - true when plotting histogram to normalize the data using density ; taking each of the value and the total values there are in the data set so that it is a ratio for each column than the total numbers."
      ]
    },
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for i in range (len(diabetes_df.columns[:-1])):\n",
        "  label = diabetes_df.columns[i]\n",
        "  plt.hist(diabetes_df[diabetes_df[\"Outcome\"]==1][label], color = 'red', label = 'Diabetes', density = True, bins=20, alpha=0.7)\n",
        "  plt.hist(diabetes_df[diabetes_df[\"Outcome\"]==0][label], color = 'green' , label = 'No Diabetes', density = True, bins=20, alpha=0.7)\n",
        "  plt.title(label)\n",
        "  plt.xlabel(label)\n",
        "  plt.ylabel('Probability')\n",
        "  plt.legend()\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The histogram doesnt clearly show the difference in diabetic and non diabetic patients have when it comes to factors like Pregnancies ,Glucose , SkinThickness e.t.c . Sure some difference seen in BMI and bloot pressure but even those are pretty inconclusive to establish a definite correlation. Hence we use the neural networks that figure out the features contribute to separating one class from another"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ix8PsfrL9Qje"
      },
      "outputs": [],
      "source": [
        "X = diabetes_df[diabetes_df.columns[:-1]].values\n",
        "Y = diabetes_df[diabetes_df.columns[-1]].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gW0imppC_yZ",
        "outputId": "fa6c0fa7-6a27-44ef-a418-6a92409e0b28"
      },
      "outputs": [
        {
          "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.   ]])"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NYR4eU_9DMMt",
        "outputId": "d302df1d-6b31-4e56-c935-2d0b8e567558"
      },
      "outputs": [
        {
          "data": {
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              "       0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
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              "       1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0])"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1XtVsJAOQWpB"
      },
      "source": [
        "## Training and evaluating model withing scaling and balancing the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WTW0sjWbDNSc"
      },
      "outputs": [],
      "source": [
        "X_temp, X_test, y_temp, y_test = train_test_split(X,Y, test_size=0.2, random_state=0)\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(X_temp,y_temp, test_size=0.2, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uQQc1e2cDrZa"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(16, activation='relu'),\n",
        "    tf.keras.layers.Dense(16, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x0bg2IuAEdVj"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sv1UWDO7FZot",
        "outputId": "3cf0ab6b-12d6-449e-c03f-0ea61d417a9a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "31/31 [==============================] - 1s 8ms/step - loss: 1.9919 - accuracy: 0.5377 - val_loss: 2.2181 - val_accuracy: 0.5285\n",
            "Epoch 2/20\n",
            "31/31 [==============================] - 0s 2ms/step - loss: 1.5548 - accuracy: 0.5438 - val_loss: 1.6707 - val_accuracy: 0.5447\n",
            "Epoch 3/20\n",
            "31/31 [==============================] - 0s 2ms/step - loss: 1.2363 - accuracy: 0.5906 - val_loss: 1.3864 - val_accuracy: 0.4797\n",
            "Epoch 4/20\n",
            "31/31 [==============================] - 0s 2ms/step - loss: 1.0776 - accuracy: 0.5845 - val_loss: 1.0996 - val_accuracy: 0.6098\n",
            "Epoch 5/20\n",
            "31/31 [==============================] - 0s 2ms/step - loss: 1.0502 - accuracy: 0.6477 - val_loss: 1.2947 - val_accuracy: 0.4797\n",
            "Epoch 6/20\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.9644 - accuracy: 0.6049 - val_loss: 0.9231 - val_accuracy: 0.6341\n",
            "Epoch 7/20\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.9125 - accuracy: 0.6171 - val_loss: 0.8181 - val_accuracy: 0.6504\n",
            "Epoch 8/20\n",
            "31/31 [==============================] - 0s 4ms/step - loss: 0.8683 - accuracy: 0.6578 - val_loss: 0.8671 - val_accuracy: 0.6016\n",
            "Epoch 9/20\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 0.8648 - accuracy: 0.6375 - val_loss: 0.7628 - val_accuracy: 0.6667\n",
            "Epoch 10/20\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 0.8157 - accuracy: 0.6456 - val_loss: 0.7967 - val_accuracy: 0.6098\n",
            "Epoch 11/20\n",
            "31/31 [==============================] - 0s 4ms/step - loss: 0.8909 - accuracy: 0.6314 - val_loss: 0.7825 - val_accuracy: 0.6341\n",
            "Epoch 12/20\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 0.8119 - accuracy: 0.6517 - val_loss: 0.6969 - val_accuracy: 0.6748\n",
            "Epoch 13/20\n",
            "31/31 [==============================] - 0s 4ms/step - loss: 0.7985 - accuracy: 0.6578 - val_loss: 0.7627 - val_accuracy: 0.6585\n",
            "Epoch 14/20\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.7729 - accuracy: 0.6517 - val_loss: 0.7909 - val_accuracy: 0.6423\n",
            "Epoch 15/20\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 0.7944 - accuracy: 0.6680 - val_loss: 0.8514 - val_accuracy: 0.6911\n",
            "Epoch 16/20\n",
            "31/31 [==============================] - 0s 4ms/step - loss: 0.7665 - accuracy: 0.6436 - val_loss: 0.7145 - val_accuracy: 0.6748\n",
            "Epoch 17/20\n",
            "31/31 [==============================] - 0s 4ms/step - loss: 0.7083 - accuracy: 0.6721 - val_loss: 0.6920 - val_accuracy: 0.6748\n",
            "Epoch 18/20\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 0.7146 - accuracy: 0.6823 - val_loss: 0.7295 - val_accuracy: 0.6829\n",
            "Epoch 19/20\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 0.7529 - accuracy: 0.6517 - val_loss: 0.7830 - val_accuracy: 0.6585\n",
            "Epoch 20/20\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.7527 - accuracy: 0.6782 - val_loss: 0.7713 - val_accuracy: 0.6504\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fed365f9eb0>"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.fit(X_train, y_train, batch_size=16, epochs=20, validation_data=(X_valid, y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l8qNM9g8FbrT",
        "outputId": "1d0f31a9-c4ec-4e42-fa2a-495dd0e48285"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5/5 [==============================] - 0s 4ms/step - loss: 0.6471 - accuracy: 0.7338\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.6471028923988342, 0.7337662577629089]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.evaluate(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "esu9FFB3LYTe"
      },
      "source": [
        "## Model training after normalizing and oversampling the data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kUnTDpigHHNX"
      },
      "outputs": [],
      "source": [
        "# Scaling the data to normalize the features into similar range\n",
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "data = np.hstack((X, np.reshape(Y, (-1, 1))))\n",
        "transformed_df = pd.DataFrame(data, columns=diabetes_df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5ZE_VhXxIEk5",
        "outputId": "9a93357b-1cd0-4ebd-9572-dcac02597281"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for i in range (len(diabetes_df.columns[:-1])):\n",
        "  label = diabetes_df.columns[i]\n",
        "  plt.hist(transformed_df[transformed_df[\"Outcome\"]==1][label], color = 'red', label = 'Diabetes', density = True, bins=20, alpha=0.7)\n",
        "  plt.hist(transformed_df[transformed_df[\"Outcome\"]==0][label], color = 'green' , label = 'No Diabetes', density = True, bins=20, alpha=0.7)\n",
        "  plt.title(label)\n",
        "  plt.xlabel(label)\n",
        "  plt.ylabel('Probability')\n",
        "  plt.legend()\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7nDBEOmDLl1n"
      },
      "outputs": [],
      "source": [
        "# Oversampling the data to make the number of samples balanced for both classes\n",
        "over_sampler = RandomOverSampler()\n",
        "X, Y = over_sampler.fit_resample(X,Y)\n",
        "data = np.hstack((X, np.reshape(Y, (-1, 1))))\n",
        "transformed_df = pd.DataFrame(data, columns=diabetes_df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GhVgXL-tM40_",
        "outputId": "f9d26da5-8791-4a7c-f1a9-2a72df6156f4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([[ 0.63994726,  0.84832379,  0.14964075, ...,  0.20401277,\n",
              "          0.46849198,  1.4259954 ],\n",
              "        [-0.84488505, -1.12339636, -0.16054575, ..., -0.68442195,\n",
              "         -0.36506078, -0.19067191],\n",
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          "execution_count": 20,
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      ],
      "source": [
        "X , len(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "RYUsk0w1M51i",
        "outputId": "fbcf78bf-b96a-495e-b398-a1c6c6bb2607"
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      "outputs": [
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            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y, len(Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cqBT_0ZoMv8O",
        "outputId": "4d0a5f3c-3726-455c-bcf0-efd2b4eb07c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of patients with Diabetes : 500\n",
            "Number of patients without Diabetes : 500\n"
          ]
        }
      ],
      "source": [
        "print(f'Number of patients with Diabetes : {len(transformed_df[transformed_df[\"Outcome\"]==1])}')\n",
        "print(f'Number of patients without Diabetes : {len(transformed_df[transformed_df[\"Outcome\"]==0])}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "blki0y3SGf5w"
      },
      "outputs": [],
      "source": [
        "X_temp, X_test, y_temp, y_test = train_test_split(X,Y, test_size=0.2, random_state=0)\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(X_temp,y_temp, test_size=0.2, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "6ORv-FE6JdRn"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(16, activation='relu'),\n",
        "    tf.keras.layers.Dense(16, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "B_MXpInkJdF4"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TaNcSZReJdCg",
        "outputId": "062295a4-d236-4fb7-8864-edd515a41cca"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "40/40 [==============================] - 1s 5ms/step - loss: 0.7580 - accuracy: 0.4641 - val_loss: 0.6911 - val_accuracy: 0.5875\n",
            "Epoch 2/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.6941 - accuracy: 0.5391 - val_loss: 0.6373 - val_accuracy: 0.7188\n",
            "Epoch 3/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.6563 - accuracy: 0.6297 - val_loss: 0.6052 - val_accuracy: 0.7437\n",
            "Epoch 4/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.6287 - accuracy: 0.6922 - val_loss: 0.5792 - val_accuracy: 0.7375\n",
            "Epoch 5/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.6037 - accuracy: 0.7312 - val_loss: 0.5563 - val_accuracy: 0.7688\n",
            "Epoch 6/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.5778 - accuracy: 0.7312 - val_loss: 0.5301 - val_accuracy: 0.7937\n",
            "Epoch 7/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.5513 - accuracy: 0.7453 - val_loss: 0.5093 - val_accuracy: 0.8000\n",
            "Epoch 8/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.5327 - accuracy: 0.7375 - val_loss: 0.4974 - val_accuracy: 0.7875\n",
            "Epoch 9/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.5195 - accuracy: 0.7437 - val_loss: 0.4909 - val_accuracy: 0.7875\n",
            "Epoch 10/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.5097 - accuracy: 0.7453 - val_loss: 0.4843 - val_accuracy: 0.7812\n",
            "Epoch 11/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.5030 - accuracy: 0.7531 - val_loss: 0.4826 - val_accuracy: 0.7937\n",
            "Epoch 12/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.4974 - accuracy: 0.7578 - val_loss: 0.4832 - val_accuracy: 0.7875\n",
            "Epoch 13/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.4936 - accuracy: 0.7484 - val_loss: 0.4814 - val_accuracy: 0.7875\n",
            "Epoch 14/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.4894 - accuracy: 0.7625 - val_loss: 0.4812 - val_accuracy: 0.7875\n",
            "Epoch 15/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.4857 - accuracy: 0.7672 - val_loss: 0.4795 - val_accuracy: 0.7812\n",
            "Epoch 16/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.4832 - accuracy: 0.7672 - val_loss: 0.4793 - val_accuracy: 0.7875\n",
            "Epoch 17/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.4804 - accuracy: 0.7656 - val_loss: 0.4802 - val_accuracy: 0.7875\n",
            "Epoch 18/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.4776 - accuracy: 0.7703 - val_loss: 0.4821 - val_accuracy: 0.7875\n",
            "Epoch 19/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.4750 - accuracy: 0.7641 - val_loss: 0.4774 - val_accuracy: 0.7875\n",
            "Epoch 20/20\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.4722 - accuracy: 0.7781 - val_loss: 0.4805 - val_accuracy: 0.7875\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fed31c3ec10>"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.fit(X_train, y_train, batch_size=16, epochs=20, validation_data=(X_valid, y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nZudTJq1LMEu",
        "outputId": "c39aa014-732c-42ec-f907-73842ee7a4a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "7/7 [==============================] - 0s 2ms/step - loss: 0.4260 - accuracy: 0.7900\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.42602208256721497, 0.7900000214576721]"
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          "execution_count": 27,
          "metadata": {},
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      ],
      "source": [
        "model.evaluate(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "yB5hHD7fzDJg",
        "outputId": "d634d083-b52d-459e-9727-ecbebf085bf2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "7/7 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
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              "       [0.08172919],\n",
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              "       [0.6437954 ],\n",
              "       [0.28244314],\n",
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              "       [0.8205223 ],\n",
              "       [0.40232727],\n",
              "       [0.01037587],\n",
              "       [0.77545303],\n",
              "       [0.21841839],\n",
              "       [0.6015397 ],\n",
              "       [0.0392907 ],\n",
              "       [0.6361412 ],\n",
              "       [0.6896521 ],\n",
              "       [0.58765846],\n",
              "       [0.07186101],\n",
              "       [0.6955822 ],\n",
              "       [0.22625728],\n",
              "       [0.1620635 ],\n",
              "       [0.5225427 ],\n",
              "       [0.20864956],\n",
              "       [0.30517328],\n",
              "       [0.84275603],\n",
              "       [0.6437954 ],\n",
              "       [0.47357902],\n",
              "       [0.47219834],\n",
              "       [0.72603637],\n",
              "       [0.89850956],\n",
              "       [0.2622205 ],\n",
              "       [0.80621624],\n",
              "       [0.07340001],\n",
              "       [0.5634908 ],\n",
              "       [0.8795102 ],\n",
              "       [0.33966628],\n",
              "       [0.6650197 ]], dtype=float32)"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictions = model.predict(X_test)\n",
        "predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "4jAS66cBy7Pw",
        "outputId": "795f01e8-d41e-4866-9952-b34846ea0359"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-48-5e66f48f5abc>:6: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "  ax= plt.subplot()\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#converting the predictied probablities into binary class\n",
        "test_preditions = [1 if preds > 0.5 else 0 for preds in predictions]\n",
        "\n",
        "matrix_confusion = confusion_matrix(y_test, test_preditions)\n",
        "sns.heatmap(matrix_confusion, square=True, annot=True, cmap='Blues', fmt='d', cbar=False)\n",
        "\n",
        "ax= plt.subplot()\n",
        "\n",
        "# labels, title and ticks\n",
        "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
        "ax.set_title('Confusion Matrix'); \n",
        "ax.xaxis.set_ticklabels(['non-diabetic', 'diabetic']); ax.yaxis.set_ticklabels(['non-diabetic', 'diabetic']);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Here the number of false negatives i.e patients that are not diabetic but are labeld as diabetid is 26. And the false positives, i.e patients that are diabetic but are labeld as non diabetid is 12. We need to create or tune a model in order to reduce these miss classifications. However, it needs to be noted that the greater false positive numbers with have more serious consequences in real application, if the diabetic patiens go undiagnosed. Non diabetic patiens if wrongly diagnosed will still reflect badly on the model but the damage control is just a second screening away.\n",
        "\n",
        "Also, since deep learning requeirs big volume of data for the model to learn the right patters off of the features, using classic machile leaning models could be another option for this project"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OG91RqGYzRlS"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
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