DiabetesMLP.ipynb

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      "source": [
        "# Keras TF - Diabetes Classification Project\n",
        "\n",
        "We will explore a diabetes classification task with Keras API for TF 2.0"
      ],
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      "source": [
        "## The Data\n",
        "\n",
        "We will be using data from a Kaggle data set:\n",
        "\n",
        "https://www.kaggle.com/datasets/mathchi/diabetes-data-set\n",
        "\n",
        "#### Feature Columns\n",
        "    \n",
        "* Pregnancies: Number of times pregnant\n",
        "* Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
        "* BloodPressure: Diastolic blood pressure (mm Hg)\n",
        "* SkinThickness: Triceps skin fold thickness (mm)\n",
        "* Insulin: 2-Hour serum insulin (mu U/ml)\n",
        "* BMI: Body mass index (weight in kg/(height in m)^2)\n",
        "* DiabetesPedigreeFunction: Diabetes pedigree function\n",
        "* Age: Age (years)\n",
        "* Outcome: Class variable (0 or 1)\n",
        "\n",
        "#### Summary Statistics\n",
        "\n",
        "* Number of Instances: 768\n",
        "* Number of Attributes: 8 plus class\n",
        "* Missing Attribute Values: Yes\n",
        "* Class Distribution: (class value 1 is interpreted as \"tested positive for\n",
        "diabetes\")"
      ],
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      "source": [
        "# Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import statsmodels.api as sm\n",
        "import statsmodels.formula.api as smf\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import scale\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, cross_val_predict\n",
        "from sklearn.preprocessing import MinMaxScaler, LabelEncoder, StandardScaler, RobustScaler\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense,Dropout\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
        "from sklearn.metrics import roc_auc_score,roc_curve\n",
        "from warnings import filterwarnings\n",
        "filterwarnings('ignore')"
      ]
    },
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      "cell_type": "markdown",
      "source": [
        "# DATASET"
      ],
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        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.max_rows', None)\n",
        "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
        "pd.set_option('display.width', 500)\n",
        "\n",
        "diabetes = pd.read_csv(\"/content/diabetes.csv\")\n",
        "df = diabetes.copy()\n",
        "df.head()"
      ],
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              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin    BMI  DiabetesPedigreeFunction  Age  Outcome\n",
              "0            6      148             72             35        0 33.600                     0.627   50        1\n",
              "1            1       85             66             29        0 26.600                     0.351   31        0\n",
              "2            8      183             64              0        0 23.300                     0.672   32        1\n",
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        "def check_df(dataframe, head = 5):\n",
        "    print(\"############## Shape ##############\")\n",
        "    print(dataframe.shape)\n",
        "    print(\"############## Types ##############\")\n",
        "    print(dataframe.dtypes)\n",
        "    print(\"############## Head ##############\")\n",
        "    print(dataframe.head(head))\n",
        "    print(\"############## Tail ##############\")\n",
        "    print(dataframe.tail(head))\n",
        "    print(\"############## NA ##############\")\n",
        "    print(dataframe.isnull().sum())\n",
        "    print(\"############## Quantiles ##############\")\n",
        "    print(dataframe.describe([0, 0.05, 0.50, 0.95, 0.99, 1]).T)\n",
        "\n",
        "check_df(df)"
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            "############## Shape ##############\n",
            "(768, 9)\n",
            "############## Types ##############\n",
            "Pregnancies                   int64\n",
            "Glucose                       int64\n",
            "BloodPressure                 int64\n",
            "SkinThickness                 int64\n",
            "Insulin                       int64\n",
            "BMI                         float64\n",
            "DiabetesPedigreeFunction    float64\n",
            "Age                           int64\n",
            "Outcome                       int64\n",
            "dtype: object\n",
            "############## Head ##############\n",
            "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin    BMI  DiabetesPedigreeFunction  Age  Outcome\n",
            "0            6      148             72             35        0 33.600                     0.627   50        1\n",
            "1            1       85             66             29        0 26.600                     0.351   31        0\n",
            "2            8      183             64              0        0 23.300                     0.672   32        1\n",
            "3            1       89             66             23       94 28.100                     0.167   21        0\n",
            "4            0      137             40             35      168 43.100                     2.288   33        1\n",
            "############## Tail ##############\n",
            "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin    BMI  DiabetesPedigreeFunction  Age  Outcome\n",
            "763           10      101             76             48      180 32.900                     0.171   63        0\n",
            "764            2      122             70             27        0 36.800                     0.340   27        0\n",
            "765            5      121             72             23      112 26.200                     0.245   30        0\n",
            "766            1      126             60              0        0 30.100                     0.349   47        1\n",
            "767            1       93             70             31        0 30.400                     0.315   23        0\n",
            "############## NA ##############\n",
            "Pregnancies                 0\n",
            "Glucose                     0\n",
            "BloodPressure               0\n",
            "SkinThickness               0\n",
            "Insulin                     0\n",
            "BMI                         0\n",
            "DiabetesPedigreeFunction    0\n",
            "Age                         0\n",
            "Outcome                     0\n",
            "dtype: int64\n",
            "############## Quantiles ##############\n",
            "                           count    mean     std    min     0%     5%     50%     95%     99%    100%     max\n",
            "Pregnancies              768.000   3.845   3.370  0.000  0.000  0.000   3.000  10.000  13.000  17.000  17.000\n",
            "Glucose                  768.000 120.895  31.973  0.000  0.000 79.000 117.000 181.000 196.000 199.000 199.000\n",
            "BloodPressure            768.000  69.105  19.356  0.000  0.000 38.700  72.000  90.000 106.000 122.000 122.000\n",
            "SkinThickness            768.000  20.536  15.952  0.000  0.000  0.000  23.000  44.000  51.330  99.000  99.000\n",
            "Insulin                  768.000  79.799 115.244  0.000  0.000  0.000  30.500 293.000 519.900 846.000 846.000\n",
            "BMI                      768.000  31.993   7.884  0.000  0.000 21.800  32.000  44.395  50.759  67.100  67.100\n",
            "DiabetesPedigreeFunction 768.000   0.472   0.331  0.078  0.078  0.140   0.372   1.133   1.698   2.420   2.420\n",
            "Age                      768.000  33.241  11.760 21.000 21.000 21.000  29.000  58.000  67.000  81.000  81.000\n",
            "Outcome                  768.000   0.349   0.477  0.000  0.000  0.000   0.000   1.000   1.000   1.000   1.000\n"
          ]
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        "# DATA PRE-PROCESSING"
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      "source": [
        "# Making variables's names to lower case\n",
        "\n",
        "df.columns = [col.lower() for col in df.columns]\n",
        "df.head()"
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              "   pregnancies  glucose  bloodpressure  skinthickness  insulin    bmi  diabetespedigreefunction  age  outcome\n",
              "0            6      148             72             35        0 33.600                     0.627   50        1\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4087d05c-fa06-4844-a990-b2eb03c0a698 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4087d05c-fa06-4844-a990-b2eb03c0a698');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking Outliers\n",
        "\n",
        "f, ax = plt.subplots(figsize=(20,20))\n",
        "fig = sns.boxplot(data=df, orient=\"h\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "34bdNI2RCkUp",
        "outputId": "b5d71e48-1959-439c-9b10-88a08098ed4e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking outcome distribution\n",
        "\n",
        "sns.countplot(x='outcome',data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "KWy042dJCnXj",
        "outputId": "2b22ddeb-066e-466e-e90e-10b5b9694eb8"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='outcome', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking correlation of the variables\n",
        "\n",
        "corr = df.corr()\n",
        "\n",
        "sns.heatmap(corr,\n",
        "         xticklabels=corr.columns,\n",
        "         yticklabels=corr.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "SJpl8CBSCqgG",
        "outputId": "0dffa1a2-e020-491f-be4d-ba4784f6ed6e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()['outcome'][:-1].sort_values().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "id": "hXh6DV_IeL-A",
        "outputId": "48a582d0-b9be-4980-ed78-a25d6725b84b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create histograms for all columns in the DataFrame\n",
        "df.hist()\n",
        "\n",
        "# Iterate through each subplot to set the font size for the titles\n",
        "for ax in plt.gcf().get_axes():\n",
        "    ax.set_title(ax.get_title(), fontsize=10)\n",
        "\n",
        "# Add tight layout for better spacing\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "3omMaYVVjBxc",
        "outputId": "779c5265-ce36-453b-ac23-5aa640531e18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a subplot of 3 x 3\n",
        "fig, axes = plt.subplots(3, 3, figsize=(7, 7))\n",
        "\n",
        "# Flatten the axes array for easy indexing\n",
        "axes = axes.flatten()\n",
        "\n",
        "# Plot a density plot for each variable\n",
        "for idx, col in enumerate(df.columns):\n",
        "    # Skip the 9th subplot (bottom right) since there are only 8 plots\n",
        "    if idx == 8:\n",
        "        axes[idx].set_visible(False)\n",
        "        continue\n",
        "    \n",
        "    ax = axes[idx]\n",
        "    ax.yaxis.set_ticklabels([])\n",
        "    sns.kdeplot(df.loc[df.outcome == 0][col], ls='-', color='black', label='No Diabetes', ax=ax)\n",
        "    sns.kdeplot(df.loc[df.outcome == 1][col], ls='--', color='black', label='Diabetes', ax=ax)\n",
        "    ax.set_title(col,fontsize=10)\n",
        "    # Move the legend to the upper right corner, Set the legend font size to 'small'\n",
        "    ax.legend(loc='upper right', fontsize='small')  \n",
        "# Add tight layout for better spacing\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "QYlyqRarh_FL",
        "outputId": "8c5fb297-d0f4-49c9-b8e2-45676e299005"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def grab_col_names(dataframe, cat_th=10, car_th=20):\n",
        "    \"\"\"\n",
        "\n",
        "   Returns the names of categorical, numeric and categorical but cardinal variables in the data set.\n",
        "   Note Categorical variables include categorical variables with numeric appearance.\n",
        "\n",
        "    Parameters\n",
        "    ------\n",
        "        dataframe: dataframe\n",
        "                Dataframe to get variable names\n",
        "        cat_th: int, optional\n",
        "                class threshold for numeric but categorical variables\n",
        "        car_th: int, optinal\n",
        "                class threshold for categorical but cardinal variables\n",
        "\n",
        "    Returns\n",
        "    ------\n",
        "        cat_cols: list\n",
        "                Categorical variable list\n",
        "        num_cols: list\n",
        "                Numeric variable list\n",
        "        cat_but_car: list\n",
        "                List of cardinal variables with categorical view\n",
        "\n",
        "    Examples\n",
        "    ------\n",
        "        import seaborn as sns\n",
        "        df = sns.load_dataset(\"iris\")\n",
        "        print(grab_col_names(df))\n",
        "\n",
        "\n",
        "    Notes\n",
        "    ------\n",
        "        cat_cols + num_cols + cat_but_car = total number of variables\n",
        "        \n",
        "        The sum of 3 lists with return equals the total number of variables: cat_cols + num_cols + cat_but_car = number of variables\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    # cat_cols, cat_but_car\n",
        "    cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == \"O\"]\n",
        "    num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and\n",
        "                   dataframe[col].dtypes != \"O\"]\n",
        "    cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and\n",
        "                   dataframe[col].dtypes == \"O\"]\n",
        "    cat_cols = cat_cols + num_but_cat\n",
        "    cat_cols = [col for col in cat_cols if col not in cat_but_car]\n",
        "\n",
        "    # num_cols\n",
        "    num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != \"O\"]\n",
        "    num_cols = [col for col in num_cols if col not in num_but_cat]\n",
        "\n",
        "    print(f\"Observations: {dataframe.shape[0]}\")\n",
        "    print(f\"Variables: {dataframe.shape[1]}\")\n",
        "    print(f'cat_cols: {len(cat_cols)}')\n",
        "    print(f'num_cols: {len(num_cols)}')\n",
        "    print(f'cat_but_car: {len(cat_but_car)}')\n",
        "    print(f'num_but_cat: {len(num_but_cat)}')\n",
        "    return cat_cols, num_cols, cat_but_car\n",
        "\n",
        "cat_cols, num_cols, cat_but_car = grab_col_names(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CtdFyCKKC8Un",
        "outputId": "1954ceae-b67a-4bcf-b154-4a6d1bd24b68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Observations: 768\n",
            "Variables: 9\n",
            "cat_cols: 1\n",
            "num_cols: 8\n",
            "cat_but_car: 0\n",
            "num_but_cat: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Outliers\n",
        "\n",
        "def outlier_thresholds(dataframe, col_name, q1=0.15, q3=0.85):\n",
        "    quartile1 = dataframe[col_name].quantile(q1)\n",
        "    quartile3 = dataframe[col_name].quantile(q3)\n",
        "    interquantile_range = quartile3 - quartile1\n",
        "    up_limit = quartile3 + interquantile_range\n",
        "    low_limit = quartile1 - 1.3 * interquantile_range\n",
        "    return low_limit, up_limit"
      ],
      "metadata": {
        "id": "RxkCzMzLDCN-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def check_outlier(dataframe, col_name):\n",
        "    low_limit, up_limit = outlier_thresholds(dataframe, col_name)\n",
        "    if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):\n",
        "        return True\n",
        "    else:\n",
        "        return False"
      ],
      "metadata": {
        "id": "cBCVFvBSDGa7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def grab_outliers(dataframe, col_name, index=False):\n",
        "    low, up = outlier_thresholds(dataframe, col_name)\n",
        "\n",
        "    if dataframe[((dataframe[col_name] < low) | (dataframe[col_name] > up))].shape[0] > 10:\n",
        "        print(dataframe[((dataframe[col_name] < low) | (dataframe[col_name] > up))].head())\n",
        "    else:\n",
        "        print(dataframe[((dataframe[col_name] < low) | (dataframe[col_name] > up))])\n",
        "\n",
        "    if index:\n",
        "        outlier_index = dataframe[((dataframe[col_name] < low) | (dataframe[col_name] > up))].index\n",
        "        return outlier_index\n",
        "    \n",
        "    for col in df.columns:\n",
        "        print(col, grab_outliers(df,col))"
      ],
      "metadata": {
        "id": "iAtA5uZUDIXF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def replace_with_thresholds(dataframe, variable):\n",
        "        low_limit, up_limit = outlier_thresholds(dataframe, variable)\n",
        "        dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit\n",
        "        dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit\n",
        "\n",
        "for col in num_cols:\n",
        "        print(replace_with_thresholds(df, col))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5nvks-9DK1n",
        "outputId": "1f85f298-e1e7-4d8d-9fc3-847c8c7e6bfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "None\n",
            "None\n",
            "None\n",
            "None\n",
            "None\n",
            "None\n",
            "None\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "n8e2nj0rDMsM",
        "outputId": "44857726-e7d8-4fd5-99b2-3b8ee1f1bfae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           count    mean    std    min    25%     50%     75%     max\n",
              "pregnancies              768.000   3.842  3.360  0.000  1.000   3.000   6.000  15.000\n",
              "glucose                  768.000 120.937 31.816  6.500 99.000 117.000 140.250 199.000\n",
              "bloodpressure            768.000  70.188 15.673 24.200 62.000  72.000  80.000 110.000\n",
              "skinthickness            768.000  20.504 15.817  0.000  0.000  23.000  32.000  74.000\n",
              "insulin                  768.000  75.132 97.656  0.000  0.000  30.500 127.250 359.800\n",
              "bmi                      768.000  32.047  7.454  5.968 27.300  32.000  36.600  53.785\n",
              "diabetespedigreefunction 768.000   0.463  0.296  0.078  0.244   0.372   0.626   1.321\n",
              "age                      768.000  33.224 11.700 21.000 24.000  29.000  41.000  70.000\n",
              "outcome                  768.000   0.349  0.477  0.000  0.000   0.000   1.000   1.000"
            ],
            "text/html": [
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>pregnancies</th>\n",
              "      <td>768.000</td>\n",
              "      <td>3.842</td>\n",
              "      <td>3.360</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>15.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>glucose</th>\n",
              "      <td>768.000</td>\n",
              "      <td>120.937</td>\n",
              "      <td>31.816</td>\n",
              "      <td>6.500</td>\n",
              "      <td>99.000</td>\n",
              "      <td>117.000</td>\n",
              "      <td>140.250</td>\n",
              "      <td>199.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bloodpressure</th>\n",
              "      <td>768.000</td>\n",
              "      <td>70.188</td>\n",
              "      <td>15.673</td>\n",
              "      <td>24.200</td>\n",
              "      <td>62.000</td>\n",
              "      <td>72.000</td>\n",
              "      <td>80.000</td>\n",
              "      <td>110.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>skinthickness</th>\n",
              "      <td>768.000</td>\n",
              "      <td>20.504</td>\n",
              "      <td>15.817</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>23.000</td>\n",
              "      <td>32.000</td>\n",
              "      <td>74.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>insulin</th>\n",
              "      <td>768.000</td>\n",
              "      <td>75.132</td>\n",
              "      <td>97.656</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>30.500</td>\n",
              "      <td>127.250</td>\n",
              "      <td>359.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmi</th>\n",
              "      <td>768.000</td>\n",
              "      <td>32.047</td>\n",
              "      <td>7.454</td>\n",
              "      <td>5.968</td>\n",
              "      <td>27.300</td>\n",
              "      <td>32.000</td>\n",
              "      <td>36.600</td>\n",
              "      <td>53.785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>diabetespedigreefunction</th>\n",
              "      <td>768.000</td>\n",
              "      <td>0.463</td>\n",
              "      <td>0.296</td>\n",
              "      <td>0.078</td>\n",
              "      <td>0.244</td>\n",
              "      <td>0.372</td>\n",
              "      <td>0.626</td>\n",
              "      <td>1.321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>768.000</td>\n",
              "      <td>33.224</td>\n",
              "      <td>11.700</td>\n",
              "      <td>21.000</td>\n",
              "      <td>24.000</td>\n",
              "      <td>29.000</td>\n",
              "      <td>41.000</td>\n",
              "      <td>70.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>outcome</th>\n",
              "      <td>768.000</td>\n",
              "      <td>0.349</td>\n",
              "      <td>0.477</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          document.querySelector('#df-09279df2-62ef-44f6-8e59-4ed84317888a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-09279df2-62ef-44f6-8e59-4ed84317888a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# There are no missing observations in the dataset\n",
        "# but for variables such as Glucose, Insulin, etc., observation units containing a value of 0 may represent a missing value.\n",
        "\n",
        "variables = [\"skinthickness\", \"insulin\"]\n",
        "df[variables] = df[variables].replace(0,np.NaN)\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "atEiuATiDOjC",
        "outputId": "a40872ab-be35-4074-904b-db2256f7a67a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pregnancies                   0\n",
              "glucose                       0\n",
              "bloodpressure                 0\n",
              "skinthickness               227\n",
              "insulin                     374\n",
              "bmi                           0\n",
              "diabetespedigreefunction      0\n",
              "age                           0\n",
              "outcome                       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Handle to missing value\n",
        "df[\"skinthickness\"].fillna(df.groupby(\"outcome\")[\"skinthickness\"].transform(\"mean\"), inplace=True)\n",
        "df[\"insulin\"].fillna(df.groupby('outcome')[\"insulin\"].transform(\"mean\"), inplace=True)\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9aqusUpDTzU",
        "outputId": "2cfddde5-1e78-48a9-c76d-59434127452b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pregnancies                 0\n",
              "glucose                     0\n",
              "bloodpressure               0\n",
              "skinthickness               0\n",
              "insulin                     0\n",
              "bmi                         0\n",
              "diabetespedigreefunction    0\n",
              "age                         0\n",
              "outcome                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# FEATURE ENGINEERING"
      ],
      "metadata": {
        "id": "d90AUEfeDuBi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[(df['age'] >= 21) & (df['age'] <= 50), 'sex_cat'] = 'mature'\n",
        "df.loc[(df['age'] > 50), 'sex_cat'] = 'elder'"
      ],
      "metadata": {
        "id": "OBV0YGv1DW9H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"new_bmi\"] = pd.cut(x=df['bmi'], bins=[0, 18.5, 24.9, 29.9, 35, 40, 100],labels=[\"underweight\", \"healthy\", \"overweight\",\n",
        "                                                                                  \"obese1\", \"obese2\", \"obese3\"])"
      ],
      "metadata": {
        "id": "541L9cfAD0f8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[(df[\"glucose\"] < 140), 'glucose_cat'] = \"normal\"\n",
        "df.loc[(df[\"glucose\"] >= 140), 'glucose_cat'] = \"at_risk\""
      ],
      "metadata": {
        "id": "gIPmddY8D2qG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[(df['glucose'] < 140) & (df[\"age\"] <= 50), \"age_gul_cat\"] = \"normal_mature\"\n",
        "df.loc[(df['glucose'] < 140) & (df[\"age\"] > 50), \"age_gul_cat\"] = \"normal_elder\"\n",
        "df.loc[(df[\"glucose\"] >= 140)& (df['age'] <= 50), \"age_gul_cat\"] = \"at_risk_mature\"\n",
        "df.loc[(df[\"glucose\"] >= 140)& (df['age'] > 50), \"age_gul_cat\"] = \"at_risk_elder\""
      ],
      "metadata": {
        "id": "W9BxK1x3D4l6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[(df[\"insulin\"] < 126), \"insul_level\"] = \"is_normal\"\n",
        "df.loc[(df[\"insulin\"] >= 126), \"insul_level\"] = \"not_normal\""
      ],
      "metadata": {
        "id": "iapvOIqfD7kq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[(df[\"insulin\"] < 126) & (df[\"age\"] <= 50), \"age_insul_level\"] = \"is_normal_mature\"\n",
        "df.loc[(df[\"insulin\"] < 126) & (df[\"age\"] > 50), \"age_insul_level\"] = \"is_normal_elder\"\n",
        "df.loc[(df[\"insulin\"] >= 126) & (df[\"age\"] <= 50), \"age_insul_level\"] = \"not_normal_mature\"\n",
        "df.loc[(df[\"insulin\"] >= 126) & (df[\"age\"] > 50), \"age_insul_level\"] = \"not_normal_elder\""
      ],
      "metadata": {
        "id": "LZaBAMpZD9QD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"gulucos_insulin\"] = df[\"glucose\"]*df[\"insulin\"]\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "3SrC5E4ID_Ho",
        "outputId": "32aa3e86-7c95-4f53-e03b-b5d446cf5b6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pregnancies  glucose  bloodpressure  skinthickness  insulin    bmi  diabetespedigreefunction    age  outcome sex_cat     new_bmi glucose_cat     age_gul_cat insul_level    age_insul_level  gulucos_insulin\n",
              "0        6.000  148.000         72.000         35.000  189.692 33.600                     0.627 50.000        1  mature      obese1     at_risk  at_risk_mature  not_normal  not_normal_mature        28074.462\n",
              "1        1.000   85.000         66.000         29.000  125.155 26.600                     0.351 31.000        0  mature  overweight      normal   normal_mature   is_normal   is_normal_mature        10638.201\n",
              "2        8.000  183.000         64.000         32.861  189.692 23.300                     0.672 32.000        1  mature     healthy     at_risk  at_risk_mature  not_normal  not_normal_mature        34713.692\n",
              "3        1.000   89.000         66.000         23.000   94.000 28.100                     0.167 21.000        0  mature  overweight      normal   normal_mature   is_normal   is_normal_mature         8366.000\n",
              "4        0.000  137.000         40.000         35.000  168.000 43.100                     1.321 33.000        1  mature      obese3      normal   normal_mature  not_normal  not_normal_mature        23016.000"
            ],
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pregnancies</th>\n",
              "      <th>glucose</th>\n",
              "      <th>bloodpressure</th>\n",
              "      <th>skinthickness</th>\n",
              "      <th>insulin</th>\n",
              "      <th>bmi</th>\n",
              "      <th>diabetespedigreefunction</th>\n",
              "      <th>age</th>\n",
              "      <th>outcome</th>\n",
              "      <th>sex_cat</th>\n",
              "      <th>new_bmi</th>\n",
              "      <th>glucose_cat</th>\n",
              "      <th>age_gul_cat</th>\n",
              "      <th>insul_level</th>\n",
              "      <th>age_insul_level</th>\n",
              "      <th>gulucos_insulin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.000</td>\n",
              "      <td>148.000</td>\n",
              "      <td>72.000</td>\n",
              "      <td>35.000</td>\n",
              "      <td>189.692</td>\n",
              "      <td>33.600</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50.000</td>\n",
              "      <td>1</td>\n",
              "      <td>mature</td>\n",
              "      <td>obese1</td>\n",
              "      <td>at_risk</td>\n",
              "      <td>at_risk_mature</td>\n",
              "      <td>not_normal</td>\n",
              "      <td>not_normal_mature</td>\n",
              "      <td>28074.462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.000</td>\n",
              "      <td>85.000</td>\n",
              "      <td>66.000</td>\n",
              "      <td>29.000</td>\n",
              "      <td>125.155</td>\n",
              "      <td>26.600</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31.000</td>\n",
              "      <td>0</td>\n",
              "      <td>mature</td>\n",
              "      <td>overweight</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal_mature</td>\n",
              "      <td>is_normal</td>\n",
              "      <td>is_normal_mature</td>\n",
              "      <td>10638.201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.000</td>\n",
              "      <td>183.000</td>\n",
              "      <td>64.000</td>\n",
              "      <td>32.861</td>\n",
              "      <td>189.692</td>\n",
              "      <td>23.300</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32.000</td>\n",
              "      <td>1</td>\n",
              "      <td>mature</td>\n",
              "      <td>healthy</td>\n",
              "      <td>at_risk</td>\n",
              "      <td>at_risk_mature</td>\n",
              "      <td>not_normal</td>\n",
              "      <td>not_normal_mature</td>\n",
              "      <td>34713.692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.000</td>\n",
              "      <td>89.000</td>\n",
              "      <td>66.000</td>\n",
              "      <td>23.000</td>\n",
              "      <td>94.000</td>\n",
              "      <td>28.100</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21.000</td>\n",
              "      <td>0</td>\n",
              "      <td>mature</td>\n",
              "      <td>overweight</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal_mature</td>\n",
              "      <td>is_normal</td>\n",
              "      <td>is_normal_mature</td>\n",
              "      <td>8366.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000</td>\n",
              "      <td>137.000</td>\n",
              "      <td>40.000</td>\n",
              "      <td>35.000</td>\n",
              "      <td>168.000</td>\n",
              "      <td>43.100</td>\n",
              "      <td>1.321</td>\n",
              "      <td>33.000</td>\n",
              "      <td>1</td>\n",
              "      <td>mature</td>\n",
              "      <td>obese3</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal_mature</td>\n",
              "      <td>not_normal</td>\n",
              "      <td>not_normal_mature</td>\n",
              "      <td>23016.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking again grab_col_names function\n",
        "cat_cols, num_cols, cat_but_car = grab_col_names(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZKF3UlbhEBs7",
        "outputId": "89e45c4d-c062-458d-bc46-fea3479e819b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Observations: 768\n",
            "Variables: 16\n",
            "cat_cols: 7\n",
            "num_cols: 9\n",
            "cat_but_car: 0\n",
            "num_but_cat: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cat_summary(dataframe, col_name, plot=False):\n",
        "    print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n",
        "                        \"Ratio\": 100 * dataframe[col_name].value_counts() / len(dataframe)}))\n",
        "    print(\"##########################################\")\n",
        "    if plot:\n",
        "        sns.countplot(x=dataframe[col_name], data=dataframe)\n",
        "        plt.show()\n",
        "\n",
        "for col in cat_cols:\n",
        "    cat_summary(df, col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0UAQFkeSEFr9",
        "outputId": "4eb46eb6-8e8d-4736-c44a-7fb383e36c0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        sex_cat  Ratio\n",
            "mature      687 89.453\n",
            "elder        81 10.547\n",
            "##########################################\n",
            "         glucose_cat  Ratio\n",
            "normal           571 74.349\n",
            "at_risk          197 25.651\n",
            "##########################################\n",
            "                age_gul_cat  Ratio\n",
            "normal_mature           529 68.880\n",
            "at_risk_mature          158 20.573\n",
            "normal_elder             42  5.469\n",
            "at_risk_elder            39  5.078\n",
            "##########################################\n",
            "            insul_level  Ratio\n",
            "is_normal           434 56.510\n",
            "not_normal          334 43.490\n",
            "##########################################\n",
            "                   age_insul_level  Ratio\n",
            "is_normal_mature               397 51.693\n",
            "not_normal_mature              290 37.760\n",
            "not_normal_elder                44  5.729\n",
            "is_normal_elder                 37  4.818\n",
            "##########################################\n",
            "   outcome  Ratio\n",
            "0      500 65.104\n",
            "1      268 34.896\n",
            "##########################################\n",
            "             new_bmi  Ratio\n",
            "obese1           228 29.688\n",
            "overweight       179 23.307\n",
            "obese2           148 19.271\n",
            "healthy          102 13.281\n",
            "obese3            96 12.500\n",
            "underweight       15  1.953\n",
            "##########################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding\n",
        "\n",
        "le = LabelEncoder()\n",
        "\n",
        "def label_encoder(dataframe, binary_col):\n",
        "    labelencoder = LabelEncoder()\n",
        "    dataframe[binary_col] = labelencoder.fit_transform(dataframe[binary_col])\n",
        "    return dataframe\n",
        "\n",
        "binary_cols = [col for col in df.columns if df[col].dtypes == \"O\" and df[col].nunique() == 2]\n",
        "print(binary_cols)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAZcQgr2EI4U",
        "outputId": "e659399d-f6cc-4701-a7b2-38fc4911272f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['sex_cat', 'glucose_cat', 'insul_level']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = label_encoder(df, \"sex_cat\")\n",
        "df = label_encoder(df, 'glucose_cat')\n",
        "df = label_encoder(df,'insul_level')"
      ],
      "metadata": {
        "id": "4jnwajWzELTe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def one_hot_encoder(dataframe, categorical_cols, drop_first=True):\n",
        "    dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)\n",
        "    return dataframe\n",
        "\n",
        "ohe_cols = [col for col in df.columns if 12 >= df[col].nunique() > 2]\n",
        "\n",
        "df = one_hot_encoder(df, ohe_cols, drop_first=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "sk3nWhW3EN4_",
        "outputId": "300f3b28-88b7-4bae-8b3f-2273f8504dc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pregnancies  glucose  bloodpressure  skinthickness  insulin    bmi  diabetespedigreefunction    age  outcome  sex_cat  glucose_cat  insul_level  gulucos_insulin  new_bmi_healthy  new_bmi_overweight  new_bmi_obese1  new_bmi_obese2  new_bmi_obese3  age_gul_cat_at_risk_mature  age_gul_cat_normal_elder  age_gul_cat_normal_mature  age_insul_level_is_normal_mature  age_insul_level_not_normal_elder  age_insul_level_not_normal_mature\n",
              "0        6.000  148.000         72.000         35.000  189.692 33.600                     0.627 50.000        1        1            0            1        28074.462                0                   0               1               0               0                           1                         0                          0                                 0                                 0                                  1\n",
              "1        1.000   85.000         66.000         29.000  125.155 26.600                     0.351 31.000        0        1            1            0        10638.201                0                   1               0               0               0                           0                         0                          1                                 1                                 0                                  0\n",
              "2        8.000  183.000         64.000         32.861  189.692 23.300                     0.672 32.000        1        1            0            1        34713.692                1                   0               0               0               0                           1                         0                          0                                 0                                 0                                  1\n",
              "3        1.000   89.000         66.000         23.000   94.000 28.100                     0.167 21.000        0        1            1            0         8366.000                0                   1               0               0               0                           0                         0                          1                                 1                                 0                                  0\n",
              "4        0.000  137.000         40.000         35.000  168.000 43.100                     1.321 33.000        1        1            1            1        23016.000                0                   0               0               0               1                           0                         0                          1                                 0                                 0                                  1"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pregnancies</th>\n",
              "      <th>glucose</th>\n",
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              "      <th>gulucos_insulin</th>\n",
              "      <th>new_bmi_healthy</th>\n",
              "      <th>new_bmi_overweight</th>\n",
              "      <th>new_bmi_obese1</th>\n",
              "      <th>new_bmi_obese2</th>\n",
              "      <th>new_bmi_obese3</th>\n",
              "      <th>age_gul_cat_at_risk_mature</th>\n",
              "      <th>age_gul_cat_normal_elder</th>\n",
              "      <th>age_gul_cat_normal_mature</th>\n",
              "      <th>age_insul_level_is_normal_mature</th>\n",
              "      <th>age_insul_level_not_normal_elder</th>\n",
              "      <th>age_insul_level_not_normal_mature</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>183.000</td>\n",
              "      <td>64.000</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
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      "metadata": {
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          "height": 270
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        "id": "oj739TjzEQ2-",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pregnancies  glucose  bloodpressure  skinthickness  insulin    bmi  diabetespedigreefunction    age  outcome  sex_cat  glucose_cat  insul_level  gulucos_insulin  new_bmi_healthy  new_bmi_overweight  new_bmi_obese1  new_bmi_obese2  new_bmi_obese3  age_gul_cat_at_risk_mature  age_gul_cat_normal_elder  age_gul_cat_normal_mature  age_insul_level_is_normal_mature  age_insul_level_not_normal_elder  age_insul_level_not_normal_mature\n",
              "0        6.000  148.000         72.000         35.000  189.692 33.600                     0.627 50.000        1        1            0            1        28074.462                0                   0               1               0               0                           1                         0                          0                                 0                                 0                                  1\n",
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              "2        8.000  183.000         64.000         32.861  189.692 23.300                     0.672 32.000        1        1            0            1        34713.692                1                   0               0               0               0                           1                         0                          0                                 0                                 0                                  1\n",
              "3        1.000   89.000         66.000         23.000   94.000 28.100                     0.167 21.000        0        1            1            0         8366.000                0                   1               0               0               0                           0                         0                          1                                 1                                 0                                  0\n",
              "4        0.000  137.000         40.000         35.000  168.000 43.100                     1.321 33.000        1        1            1            1        23016.000                0                   0               0               0               1                           0                         0                          1                                 0                                 0                                  1"
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              "\n",
              "    .colab-df-convert {\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MLP MODEL"
      ],
      "metadata": {
        "id": "bgeRiwcUEm_V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qXyo4FAPEUdy",
        "outputId": "3f2935f9-1a13-4618-8cb8-5c5c7955ce0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 24 columns):\n",
            " #   Column                             Non-Null Count  Dtype  \n",
            "---  ------                             --------------  -----  \n",
            " 0   pregnancies                        768 non-null    float64\n",
            " 1   glucose                            768 non-null    float64\n",
            " 2   bloodpressure                      768 non-null    float64\n",
            " 3   skinthickness                      768 non-null    float64\n",
            " 4   insulin                            768 non-null    float64\n",
            " 5   bmi                                768 non-null    float64\n",
            " 6   diabetespedigreefunction           768 non-null    float64\n",
            " 7   age                                768 non-null    float64\n",
            " 8   outcome                            768 non-null    int64  \n",
            " 9   sex_cat                            768 non-null    int64  \n",
            " 10  glucose_cat                        768 non-null    int64  \n",
            " 11  insul_level                        768 non-null    int64  \n",
            " 12  gulucos_insulin                    768 non-null    float64\n",
            " 13  new_bmi_healthy                    768 non-null    uint8  \n",
            " 14  new_bmi_overweight                 768 non-null    uint8  \n",
            " 15  new_bmi_obese1                     768 non-null    uint8  \n",
            " 16  new_bmi_obese2                     768 non-null    uint8  \n",
            " 17  new_bmi_obese3                     768 non-null    uint8  \n",
            " 18  age_gul_cat_at_risk_mature         768 non-null    uint8  \n",
            " 19  age_gul_cat_normal_elder           768 non-null    uint8  \n",
            " 20  age_gul_cat_normal_mature          768 non-null    uint8  \n",
            " 21  age_insul_level_is_normal_mature   768 non-null    uint8  \n",
            " 22  age_insul_level_not_normal_elder   768 non-null    uint8  \n",
            " 23  age_insul_level_not_normal_mature  768 non-null    uint8  \n",
            "dtypes: float64(9), int64(4), uint8(11)\n",
            "memory usage: 86.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('outcome',axis=1).values\n",
        "y = df['outcome'].values"
      ],
      "metadata": {
        "id": "PXQuLE4LEfPO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=101)"
      ],
      "metadata": {
        "id": "jwtXMGGME1DH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaling for numeric cols\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "X_train= scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "dmEc4Ys3E9OW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vVTkSdj5E_0y",
        "outputId": "6912bfb9-299b-46a6-a18e-06f8fb8b412d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(614, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the model\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "# https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw\n",
        "\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Dense(16, activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "\n",
        "# For a binary classification problem\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "OPAg9CQkhUPb"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=35)"
      ],
      "metadata": {
        "id": "_X3-rOkohWhg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x=X_train, \n",
        "          y=y_train, \n",
        "          batch_size=32,\n",
        "          epochs=400,\n",
        "          validation_data=(X_test, y_test), \n",
        "          verbose=1,\n",
        "          callbacks=[early_stop],\n",
        "          )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DAyzpcqqhaPM",
        "outputId": "1f4cdcf9-81ad-4816-d0b3-dea03163f1f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/400\n",
            "20/20 [==============================] - 2s 19ms/step - loss: 0.6576 - accuracy: 0.6091 - val_loss: 0.5881 - val_accuracy: 0.6688\n",
            "Epoch 2/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5900 - accuracy: 0.6661 - val_loss: 0.4838 - val_accuracy: 0.7727\n",
            "Epoch 3/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4849 - accuracy: 0.7769 - val_loss: 0.3934 - val_accuracy: 0.8312\n",
            "Epoch 4/400\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.4223 - accuracy: 0.8160 - val_loss: 0.3677 - val_accuracy: 0.8312\n",
            "Epoch 5/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4275 - accuracy: 0.8274 - val_loss: 0.3658 - val_accuracy: 0.8442\n",
            "Epoch 6/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4246 - accuracy: 0.8208 - val_loss: 0.3585 - val_accuracy: 0.8377\n",
            "Epoch 7/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4094 - accuracy: 0.8339 - val_loss: 0.3503 - val_accuracy: 0.8442\n",
            "Epoch 8/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4120 - accuracy: 0.8388 - val_loss: 0.3443 - val_accuracy: 0.8442\n",
            "Epoch 9/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4127 - accuracy: 0.8469 - val_loss: 0.3417 - val_accuracy: 0.8506\n",
            "Epoch 10/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4032 - accuracy: 0.8436 - val_loss: 0.3407 - val_accuracy: 0.8571\n",
            "Epoch 11/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4065 - accuracy: 0.8306 - val_loss: 0.3413 - val_accuracy: 0.8377\n",
            "Epoch 12/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.3984 - accuracy: 0.8371 - val_loss: 0.3362 - val_accuracy: 0.8442\n",
            "Epoch 13/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4016 - accuracy: 0.8355 - val_loss: 0.3266 - val_accuracy: 0.8766\n",
            "Epoch 14/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3846 - accuracy: 0.8550 - val_loss: 0.3222 - val_accuracy: 0.8506\n",
            "Epoch 15/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3869 - accuracy: 0.8388 - val_loss: 0.3231 - val_accuracy: 0.8636\n",
            "Epoch 16/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3761 - accuracy: 0.8388 - val_loss: 0.3161 - val_accuracy: 0.8636\n",
            "Epoch 17/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3894 - accuracy: 0.8420 - val_loss: 0.3190 - val_accuracy: 0.8506\n",
            "Epoch 18/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4088 - accuracy: 0.8436 - val_loss: 0.3216 - val_accuracy: 0.8896\n",
            "Epoch 19/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3665 - accuracy: 0.8599 - val_loss: 0.3073 - val_accuracy: 0.8701\n",
            "Epoch 20/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3620 - accuracy: 0.8632 - val_loss: 0.3028 - val_accuracy: 0.8766\n",
            "Epoch 21/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3629 - accuracy: 0.8632 - val_loss: 0.3084 - val_accuracy: 0.8766\n",
            "Epoch 22/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3692 - accuracy: 0.8599 - val_loss: 0.3075 - val_accuracy: 0.8766\n",
            "Epoch 23/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3780 - accuracy: 0.8583 - val_loss: 0.3098 - val_accuracy: 0.8766\n",
            "Epoch 24/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3793 - accuracy: 0.8485 - val_loss: 0.3075 - val_accuracy: 0.8636\n",
            "Epoch 25/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3704 - accuracy: 0.8550 - val_loss: 0.3095 - val_accuracy: 0.8831\n",
            "Epoch 26/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3611 - accuracy: 0.8567 - val_loss: 0.3025 - val_accuracy: 0.8766\n",
            "Epoch 27/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.3578 - accuracy: 0.8795 - val_loss: 0.3098 - val_accuracy: 0.8506\n",
            "Epoch 28/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3584 - accuracy: 0.8616 - val_loss: 0.2999 - val_accuracy: 0.8766\n",
            "Epoch 29/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3615 - accuracy: 0.8681 - val_loss: 0.3071 - val_accuracy: 0.8701\n",
            "Epoch 30/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3811 - accuracy: 0.8567 - val_loss: 0.3046 - val_accuracy: 0.8961\n",
            "Epoch 31/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.3768 - accuracy: 0.8648 - val_loss: 0.2965 - val_accuracy: 0.8896\n",
            "Epoch 32/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3395 - accuracy: 0.8616 - val_loss: 0.3099 - val_accuracy: 0.8701\n",
            "Epoch 33/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3619 - accuracy: 0.8681 - val_loss: 0.2933 - val_accuracy: 0.8766\n",
            "Epoch 34/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.3351 - accuracy: 0.8664 - val_loss: 0.2943 - val_accuracy: 0.8831\n",
            "Epoch 35/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3470 - accuracy: 0.8730 - val_loss: 0.2942 - val_accuracy: 0.8766\n",
            "Epoch 36/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3385 - accuracy: 0.8616 - val_loss: 0.2894 - val_accuracy: 0.8766\n",
            "Epoch 37/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3477 - accuracy: 0.8648 - val_loss: 0.2922 - val_accuracy: 0.8636\n",
            "Epoch 38/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3439 - accuracy: 0.8697 - val_loss: 0.2892 - val_accuracy: 0.8701\n",
            "Epoch 39/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3296 - accuracy: 0.8697 - val_loss: 0.2931 - val_accuracy: 0.8636\n",
            "Epoch 40/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3385 - accuracy: 0.8697 - val_loss: 0.2946 - val_accuracy: 0.8831\n",
            "Epoch 41/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3315 - accuracy: 0.8632 - val_loss: 0.3025 - val_accuracy: 0.8636\n",
            "Epoch 42/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3229 - accuracy: 0.8779 - val_loss: 0.2895 - val_accuracy: 0.8766\n",
            "Epoch 43/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.3474 - accuracy: 0.8518 - val_loss: 0.2939 - val_accuracy: 0.8766\n",
            "Epoch 44/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3456 - accuracy: 0.8567 - val_loss: 0.2914 - val_accuracy: 0.8766\n",
            "Epoch 45/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3375 - accuracy: 0.8697 - val_loss: 0.2861 - val_accuracy: 0.8831\n",
            "Epoch 46/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.3203 - accuracy: 0.8746 - val_loss: 0.2843 - val_accuracy: 0.8766\n",
            "Epoch 47/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3451 - accuracy: 0.8730 - val_loss: 0.2946 - val_accuracy: 0.8636\n",
            "Epoch 48/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.3342 - accuracy: 0.8648 - val_loss: 0.2894 - val_accuracy: 0.8636\n",
            "Epoch 49/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3394 - accuracy: 0.8697 - val_loss: 0.2831 - val_accuracy: 0.8701\n",
            "Epoch 50/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3187 - accuracy: 0.8664 - val_loss: 0.2814 - val_accuracy: 0.8831\n",
            "Epoch 51/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3209 - accuracy: 0.8681 - val_loss: 0.2823 - val_accuracy: 0.8831\n",
            "Epoch 52/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3346 - accuracy: 0.8713 - val_loss: 0.2885 - val_accuracy: 0.8701\n",
            "Epoch 53/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3241 - accuracy: 0.8827 - val_loss: 0.2890 - val_accuracy: 0.8896\n",
            "Epoch 54/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3232 - accuracy: 0.8567 - val_loss: 0.2853 - val_accuracy: 0.8766\n",
            "Epoch 55/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3208 - accuracy: 0.8664 - val_loss: 0.2820 - val_accuracy: 0.8961\n",
            "Epoch 56/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3187 - accuracy: 0.8746 - val_loss: 0.2868 - val_accuracy: 0.8701\n",
            "Epoch 57/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3374 - accuracy: 0.8713 - val_loss: 0.2803 - val_accuracy: 0.8896\n",
            "Epoch 58/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3280 - accuracy: 0.8762 - val_loss: 0.2816 - val_accuracy: 0.8766\n",
            "Epoch 59/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3306 - accuracy: 0.8730 - val_loss: 0.2798 - val_accuracy: 0.8701\n",
            "Epoch 60/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3336 - accuracy: 0.8746 - val_loss: 0.2896 - val_accuracy: 0.8831\n",
            "Epoch 61/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3124 - accuracy: 0.8746 - val_loss: 0.2798 - val_accuracy: 0.8766\n",
            "Epoch 62/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.3271 - accuracy: 0.8681 - val_loss: 0.2796 - val_accuracy: 0.8636\n",
            "Epoch 63/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3132 - accuracy: 0.8713 - val_loss: 0.2886 - val_accuracy: 0.8571\n",
            "Epoch 64/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3171 - accuracy: 0.8779 - val_loss: 0.2858 - val_accuracy: 0.8701\n",
            "Epoch 65/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3005 - accuracy: 0.8795 - val_loss: 0.2876 - val_accuracy: 0.8766\n",
            "Epoch 66/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3095 - accuracy: 0.8844 - val_loss: 0.2875 - val_accuracy: 0.8896\n",
            "Epoch 67/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3323 - accuracy: 0.8681 - val_loss: 0.3068 - val_accuracy: 0.8701\n",
            "Epoch 68/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3145 - accuracy: 0.8811 - val_loss: 0.2807 - val_accuracy: 0.9026\n",
            "Epoch 69/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.3120 - accuracy: 0.8632 - val_loss: 0.2838 - val_accuracy: 0.8701\n",
            "Epoch 70/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3276 - accuracy: 0.8697 - val_loss: 0.2837 - val_accuracy: 0.8701\n",
            "Epoch 71/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3028 - accuracy: 0.8844 - val_loss: 0.2828 - val_accuracy: 0.8766\n",
            "Epoch 72/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3180 - accuracy: 0.8958 - val_loss: 0.2826 - val_accuracy: 0.8831\n",
            "Epoch 73/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3079 - accuracy: 0.8844 - val_loss: 0.2842 - val_accuracy: 0.8636\n",
            "Epoch 74/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3009 - accuracy: 0.8827 - val_loss: 0.2883 - val_accuracy: 0.8766\n",
            "Epoch 75/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.3215 - accuracy: 0.8779 - val_loss: 0.2815 - val_accuracy: 0.8701\n",
            "Epoch 76/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3099 - accuracy: 0.8746 - val_loss: 0.2824 - val_accuracy: 0.8701\n",
            "Epoch 77/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3207 - accuracy: 0.8730 - val_loss: 0.2918 - val_accuracy: 0.8571\n",
            "Epoch 78/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3056 - accuracy: 0.8795 - val_loss: 0.2833 - val_accuracy: 0.8701\n",
            "Epoch 79/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.3209 - accuracy: 0.8746 - val_loss: 0.2838 - val_accuracy: 0.8701\n",
            "Epoch 80/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2980 - accuracy: 0.8697 - val_loss: 0.2749 - val_accuracy: 0.8701\n",
            "Epoch 81/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3190 - accuracy: 0.8730 - val_loss: 0.2805 - val_accuracy: 0.8766\n",
            "Epoch 82/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3157 - accuracy: 0.8795 - val_loss: 0.2745 - val_accuracy: 0.8831\n",
            "Epoch 83/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3057 - accuracy: 0.8844 - val_loss: 0.2781 - val_accuracy: 0.8701\n",
            "Epoch 84/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3006 - accuracy: 0.8876 - val_loss: 0.2825 - val_accuracy: 0.8636\n",
            "Epoch 85/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3027 - accuracy: 0.8730 - val_loss: 0.2770 - val_accuracy: 0.8701\n",
            "Epoch 86/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3008 - accuracy: 0.8958 - val_loss: 0.2815 - val_accuracy: 0.8766\n",
            "Epoch 87/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2819 - accuracy: 0.8941 - val_loss: 0.2765 - val_accuracy: 0.8766\n",
            "Epoch 88/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2980 - accuracy: 0.8746 - val_loss: 0.2736 - val_accuracy: 0.8831\n",
            "Epoch 89/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2665 - accuracy: 0.8990 - val_loss: 0.3031 - val_accuracy: 0.8636\n",
            "Epoch 90/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.2965 - accuracy: 0.8893 - val_loss: 0.2744 - val_accuracy: 0.8831\n",
            "Epoch 91/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2903 - accuracy: 0.8958 - val_loss: 0.2855 - val_accuracy: 0.8766\n",
            "Epoch 92/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.2932 - accuracy: 0.8876 - val_loss: 0.2776 - val_accuracy: 0.8896\n",
            "Epoch 93/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2840 - accuracy: 0.8876 - val_loss: 0.2814 - val_accuracy: 0.8831\n",
            "Epoch 94/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.3110 - accuracy: 0.8762 - val_loss: 0.2792 - val_accuracy: 0.8766\n",
            "Epoch 95/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2900 - accuracy: 0.8909 - val_loss: 0.2834 - val_accuracy: 0.8701\n",
            "Epoch 96/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.2876 - accuracy: 0.8844 - val_loss: 0.2877 - val_accuracy: 0.8766\n",
            "Epoch 97/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.2826 - accuracy: 0.8909 - val_loss: 0.2794 - val_accuracy: 0.8831\n",
            "Epoch 98/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.2857 - accuracy: 0.8860 - val_loss: 0.2791 - val_accuracy: 0.8831\n",
            "Epoch 99/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2779 - accuracy: 0.8990 - val_loss: 0.3045 - val_accuracy: 0.8571\n",
            "Epoch 100/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.3191 - accuracy: 0.8616 - val_loss: 0.2863 - val_accuracy: 0.8831\n",
            "Epoch 101/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2922 - accuracy: 0.8925 - val_loss: 0.2910 - val_accuracy: 0.8766\n",
            "Epoch 102/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2989 - accuracy: 0.8697 - val_loss: 0.2693 - val_accuracy: 0.8961\n",
            "Epoch 103/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2753 - accuracy: 0.8893 - val_loss: 0.2944 - val_accuracy: 0.8571\n",
            "Epoch 104/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.2805 - accuracy: 0.8941 - val_loss: 0.2750 - val_accuracy: 0.8831\n",
            "Epoch 105/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.2608 - accuracy: 0.8974 - val_loss: 0.2835 - val_accuracy: 0.8831\n",
            "Epoch 106/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2763 - accuracy: 0.8990 - val_loss: 0.2853 - val_accuracy: 0.8701\n",
            "Epoch 107/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2706 - accuracy: 0.8974 - val_loss: 0.2834 - val_accuracy: 0.8766\n",
            "Epoch 108/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2817 - accuracy: 0.8925 - val_loss: 0.2911 - val_accuracy: 0.8766\n",
            "Epoch 109/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2660 - accuracy: 0.9072 - val_loss: 0.2847 - val_accuracy: 0.9026\n",
            "Epoch 110/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2692 - accuracy: 0.9023 - val_loss: 0.2923 - val_accuracy: 0.8896\n",
            "Epoch 111/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2519 - accuracy: 0.9088 - val_loss: 0.3048 - val_accuracy: 0.8571\n",
            "Epoch 112/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2978 - accuracy: 0.8811 - val_loss: 0.2909 - val_accuracy: 0.8766\n",
            "Epoch 113/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2775 - accuracy: 0.8990 - val_loss: 0.2845 - val_accuracy: 0.8896\n",
            "Epoch 114/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2846 - accuracy: 0.8893 - val_loss: 0.2871 - val_accuracy: 0.8961\n",
            "Epoch 115/400\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2958 - accuracy: 0.8860 - val_loss: 0.2852 - val_accuracy: 0.8831\n",
            "Epoch 116/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2730 - accuracy: 0.8909 - val_loss: 0.2810 - val_accuracy: 0.8961\n",
            "Epoch 117/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2677 - accuracy: 0.8925 - val_loss: 0.3017 - val_accuracy: 0.8636\n",
            "Epoch 118/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2778 - accuracy: 0.8941 - val_loss: 0.2917 - val_accuracy: 0.8961\n",
            "Epoch 119/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2611 - accuracy: 0.8958 - val_loss: 0.2897 - val_accuracy: 0.8896\n",
            "Epoch 120/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2848 - accuracy: 0.8811 - val_loss: 0.2896 - val_accuracy: 0.8701\n",
            "Epoch 121/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2546 - accuracy: 0.9104 - val_loss: 0.2888 - val_accuracy: 0.8831\n",
            "Epoch 122/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2553 - accuracy: 0.8909 - val_loss: 0.2872 - val_accuracy: 0.8896\n",
            "Epoch 123/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2466 - accuracy: 0.9039 - val_loss: 0.2818 - val_accuracy: 0.8831\n",
            "Epoch 124/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2591 - accuracy: 0.9055 - val_loss: 0.3004 - val_accuracy: 0.8636\n",
            "Epoch 125/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2868 - accuracy: 0.8860 - val_loss: 0.2851 - val_accuracy: 0.8766\n",
            "Epoch 126/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2701 - accuracy: 0.8893 - val_loss: 0.2759 - val_accuracy: 0.8896\n",
            "Epoch 127/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2760 - accuracy: 0.8844 - val_loss: 0.2843 - val_accuracy: 0.8636\n",
            "Epoch 128/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2604 - accuracy: 0.8958 - val_loss: 0.2911 - val_accuracy: 0.8766\n",
            "Epoch 129/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2477 - accuracy: 0.9023 - val_loss: 0.2885 - val_accuracy: 0.8831\n",
            "Epoch 130/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2655 - accuracy: 0.8990 - val_loss: 0.2832 - val_accuracy: 0.8766\n",
            "Epoch 131/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2502 - accuracy: 0.9023 - val_loss: 0.2816 - val_accuracy: 0.9026\n",
            "Epoch 132/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2608 - accuracy: 0.8990 - val_loss: 0.2877 - val_accuracy: 0.8831\n",
            "Epoch 133/400\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.2583 - accuracy: 0.8925 - val_loss: 0.2710 - val_accuracy: 0.9026\n",
            "Epoch 134/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2500 - accuracy: 0.9007 - val_loss: 0.2765 - val_accuracy: 0.8961\n",
            "Epoch 135/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2538 - accuracy: 0.8990 - val_loss: 0.2843 - val_accuracy: 0.8766\n",
            "Epoch 136/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2644 - accuracy: 0.8925 - val_loss: 0.2801 - val_accuracy: 0.8896\n",
            "Epoch 137/400\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.2487 - accuracy: 0.9121 - val_loss: 0.2816 - val_accuracy: 0.9091\n",
            "Epoch 137: early stopping\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f9f71f83d00>"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_loss = pd.DataFrame(model.history.history)[['loss','val_loss']]\n",
        "model_loss.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "c2MJ2sjThf00",
        "outputId": "43c96dbe-1ec1-4854-a7cd-a52ebb921d3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 119
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MODEL EVALUATION"
      ],
      "metadata": {
        "id": "2cqukc7UnOxs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = (model.predict(X_test) > 0.5)*1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1qwON1WRml_0",
        "outputId": "da1dcec2-3ff9-49f7-e5a9-36ab8e58b752"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 0s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification report\n",
        "\n",
        "print(classification_report(y_test,predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6gbACASnmpcG",
        "outputId": "b74b833b-cddc-4e7c-8f04-053e817a5850"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.92      0.93       103\n",
            "           1       0.85      0.88      0.87        51\n",
            "\n",
            "    accuracy                           0.91       154\n",
            "   macro avg       0.89      0.90      0.90       154\n",
            "weighted avg       0.91      0.91      0.91       154\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion matrix\n",
        "\n",
        "print(confusion_matrix(y_test,predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWQZrJzWm0dD",
        "outputId": "c72f18f5-8f82-4543-a91e-820c99a85277"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[95  8]\n",
            " [ 6 45]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ROC curve\n",
        "\n",
        "y_test_pred_probs = model.predict(X_test) \n",
        "\n",
        "FPR, TPR, _ = roc_curve(y_test, y_test_pred_probs)\n",
        "\n",
        "plt.plot(FPR, TPR)\n",
        "plt.plot([0,1],[0,1],'--', color='black') #diagonal line\n",
        "plt.title('ROC Curve')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "Ru2oZtedm3yl",
        "outputId": "263b6a7a-0168-4e34-d911-dfed70bc89a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 0s 4ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'True Positive Rate')"
            ]
          },
          "metadata": {},
          "execution_count": 123
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}