240525_202104591_김하준.ipynb

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      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "from tensorflow import keras\n",
        "\n",
        "from keras.layers import Dense, Flatten, Dropout\n",
        "from keras.regularizers import l2\n",
        "from keras.models import Sequential\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "\n",
        "pd.options.display.float_format = \"{:.2f}\".format"
      ],
      "metadata": {
        "id": "DOibzC7cNnvD"
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      "execution_count": 23,
      "outputs": []
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          "data": {
            "text/plain": [
              "   gender   age  hypertension  heart_disease smoking_history   bmi  \\\n",
              "0  Female 80.00             0              1           never 25.19   \n",
              "1  Female 54.00             0              0         No Info 27.32   \n",
              "2    Male 28.00             0              0           never 27.32   \n",
              "3  Female 36.00             0              0         current 23.45   \n",
              "4    Male 76.00             1              1         current 20.14   \n",
              "\n",
              "   HbA1c_level  blood_glucose_level  diabetes  \n",
              "0         6.60                  140         0  \n",
              "1         6.60                   80         0  \n",
              "2         5.70                  158         0  \n",
              "3         5.00                  155         0  \n",
              "4         4.80                  155         0  "
            ],
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              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>smoking_history</th>\n",
              "      <th>bmi</th>\n",
              "      <th>HbA1c_level</th>\n",
              "      <th>blood_glucose_level</th>\n",
              "      <th>diabetes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>80.00</td>\n",
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              "      <td>25.19</td>\n",
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              "      <td>54.00</td>\n",
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              "      <td>No Info</td>\n",
              "      <td>27.32</td>\n",
              "      <td>6.60</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>28.00</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>36.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>current</td>\n",
              "      <td>23.45</td>\n",
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              "      <td>155</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>76.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>current</td>\n",
              "      <td>20.14</td>\n",
              "      <td>4.80</td>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 100000,\n  \"fields\": [\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Female\",\n          \"Male\",\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.516839871617023,\n        \"min\": 0.08,\n        \"max\": 80.0,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          29.0,\n          39.0,\n          16.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hypertension\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heart_disease\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoking_history\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"never\",\n          \"No Info\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.636783416648369,\n        \"min\": 10.01,\n        \"max\": 95.69,\n        \"num_unique_values\": 4247,\n        \"samples\": [\n          53.27,\n          32.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HbA1c_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0706720918835437,\n        \"min\": 3.5,\n        \"max\": 9.0,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          6.6,\n          5.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"blood_glucose_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40,\n        \"min\": 80,\n        \"max\": 300,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          140,\n          80\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diabetes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "targetUrl = \"https://raw.githubusercontent.com/oprostep/2024-Spring-ML/main/diabetes_prediction_dataset.csv\"\n",
        "\n",
        "df = pd.read_csv(targetUrl, sep=',')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "cCT_g3moOWTS",
        "outputId": "072c6543-210f-48bd-a239-954cf939696e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100000 entries, 0 to 99999\n",
            "Data columns (total 9 columns):\n",
            " #   Column               Non-Null Count   Dtype  \n",
            "---  ------               --------------   -----  \n",
            " 0   gender               100000 non-null  object \n",
            " 1   age                  100000 non-null  float64\n",
            " 2   hypertension         100000 non-null  int64  \n",
            " 3   heart_disease        100000 non-null  int64  \n",
            " 4   smoking_history      100000 non-null  object \n",
            " 5   bmi                  100000 non-null  float64\n",
            " 6   HbA1c_level          100000 non-null  float64\n",
            " 7   blood_glucose_level  100000 non-null  int64  \n",
            " 8   diabetes             100000 non-null  int64  \n",
            "dtypes: float64(3), int64(4), object(2)\n",
            "memory usage: 6.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UT-uBSqUeeYM",
        "outputId": "ee0a6373-b4f0-42a2-e455-47a3616ab1ca"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender                 0\n",
              "age                    0\n",
              "hypertension           0\n",
              "heart_disease          0\n",
              "smoking_history        0\n",
              "bmi                    0\n",
              "HbA1c_level            0\n",
              "blood_glucose_level    0\n",
              "diabetes               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 중복 제거\n",
        "duplicate_rows_data = df[df.duplicated()]\n",
        "print(\"number of duplicate rows: \", duplicate_rows_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mg5-jCE8OwkW",
        "outputId": "6ab9ab8b-e8c9-4b37-90be-81b6a0c4e51b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of duplicate rows:  (3854, 9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop_duplicates()\n",
        "df.describe().style.format(\"{:.2f}\")"
      ],
      "metadata": {
        "id": "q1yX6lIOOyme",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "76beb5d2-bbb2-4c5e-97a9-eb0b8c66ccad"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
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              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_e4328\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_e4328_level0_col0\" class=\"col_heading level0 col0\" >age</th>\n",
              "      <th id=\"T_e4328_level0_col1\" class=\"col_heading level0 col1\" >hypertension</th>\n",
              "      <th id=\"T_e4328_level0_col2\" class=\"col_heading level0 col2\" >heart_disease</th>\n",
              "      <th id=\"T_e4328_level0_col3\" class=\"col_heading level0 col3\" >bmi</th>\n",
              "      <th id=\"T_e4328_level0_col4\" class=\"col_heading level0 col4\" >HbA1c_level</th>\n",
              "      <th id=\"T_e4328_level0_col5\" class=\"col_heading level0 col5\" >blood_glucose_level</th>\n",
              "      <th id=\"T_e4328_level0_col6\" class=\"col_heading level0 col6\" >diabetes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_e4328_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
              "      <td id=\"T_e4328_row0_col0\" class=\"data row0 col0\" >96146.00</td>\n",
              "      <td id=\"T_e4328_row0_col1\" class=\"data row0 col1\" >96146.00</td>\n",
              "      <td id=\"T_e4328_row0_col2\" class=\"data row0 col2\" >96146.00</td>\n",
              "      <td id=\"T_e4328_row0_col3\" class=\"data row0 col3\" >96146.00</td>\n",
              "      <td id=\"T_e4328_row0_col4\" class=\"data row0 col4\" >96146.00</td>\n",
              "      <td id=\"T_e4328_row0_col5\" class=\"data row0 col5\" >96146.00</td>\n",
              "      <td id=\"T_e4328_row0_col6\" class=\"data row0 col6\" >96146.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4328_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
              "      <td id=\"T_e4328_row1_col0\" class=\"data row1 col0\" >41.79</td>\n",
              "      <td id=\"T_e4328_row1_col1\" class=\"data row1 col1\" >0.08</td>\n",
              "      <td id=\"T_e4328_row1_col2\" class=\"data row1 col2\" >0.04</td>\n",
              "      <td id=\"T_e4328_row1_col3\" class=\"data row1 col3\" >27.32</td>\n",
              "      <td id=\"T_e4328_row1_col4\" class=\"data row1 col4\" >5.53</td>\n",
              "      <td id=\"T_e4328_row1_col5\" class=\"data row1 col5\" >138.22</td>\n",
              "      <td id=\"T_e4328_row1_col6\" class=\"data row1 col6\" >0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4328_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
              "      <td id=\"T_e4328_row2_col0\" class=\"data row2 col0\" >22.46</td>\n",
              "      <td id=\"T_e4328_row2_col1\" class=\"data row2 col1\" >0.27</td>\n",
              "      <td id=\"T_e4328_row2_col2\" class=\"data row2 col2\" >0.20</td>\n",
              "      <td id=\"T_e4328_row2_col3\" class=\"data row2 col3\" >6.77</td>\n",
              "      <td id=\"T_e4328_row2_col4\" class=\"data row2 col4\" >1.07</td>\n",
              "      <td id=\"T_e4328_row2_col5\" class=\"data row2 col5\" >40.91</td>\n",
              "      <td id=\"T_e4328_row2_col6\" class=\"data row2 col6\" >0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4328_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
              "      <td id=\"T_e4328_row3_col0\" class=\"data row3 col0\" >0.08</td>\n",
              "      <td id=\"T_e4328_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
              "      <td id=\"T_e4328_row3_col2\" class=\"data row3 col2\" >0.00</td>\n",
              "      <td id=\"T_e4328_row3_col3\" class=\"data row3 col3\" >10.01</td>\n",
              "      <td id=\"T_e4328_row3_col4\" class=\"data row3 col4\" >3.50</td>\n",
              "      <td id=\"T_e4328_row3_col5\" class=\"data row3 col5\" >80.00</td>\n",
              "      <td id=\"T_e4328_row3_col6\" class=\"data row3 col6\" >0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4328_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
              "      <td id=\"T_e4328_row4_col0\" class=\"data row4 col0\" >24.00</td>\n",
              "      <td id=\"T_e4328_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
              "      <td id=\"T_e4328_row4_col2\" class=\"data row4 col2\" >0.00</td>\n",
              "      <td id=\"T_e4328_row4_col3\" class=\"data row4 col3\" >23.40</td>\n",
              "      <td id=\"T_e4328_row4_col4\" class=\"data row4 col4\" >4.80</td>\n",
              "      <td id=\"T_e4328_row4_col5\" class=\"data row4 col5\" >100.00</td>\n",
              "      <td id=\"T_e4328_row4_col6\" class=\"data row4 col6\" >0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4328_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
              "      <td id=\"T_e4328_row5_col0\" class=\"data row5 col0\" >43.00</td>\n",
              "      <td id=\"T_e4328_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
              "      <td id=\"T_e4328_row5_col2\" class=\"data row5 col2\" >0.00</td>\n",
              "      <td id=\"T_e4328_row5_col3\" class=\"data row5 col3\" >27.32</td>\n",
              "      <td id=\"T_e4328_row5_col4\" class=\"data row5 col4\" >5.80</td>\n",
              "      <td id=\"T_e4328_row5_col5\" class=\"data row5 col5\" >140.00</td>\n",
              "      <td id=\"T_e4328_row5_col6\" class=\"data row5 col6\" >0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4328_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
              "      <td id=\"T_e4328_row6_col0\" class=\"data row6 col0\" >59.00</td>\n",
              "      <td id=\"T_e4328_row6_col1\" class=\"data row6 col1\" >0.00</td>\n",
              "      <td id=\"T_e4328_row6_col2\" class=\"data row6 col2\" >0.00</td>\n",
              "      <td id=\"T_e4328_row6_col3\" class=\"data row6 col3\" >29.86</td>\n",
              "      <td id=\"T_e4328_row6_col4\" class=\"data row6 col4\" >6.20</td>\n",
              "      <td id=\"T_e4328_row6_col5\" class=\"data row6 col5\" >159.00</td>\n",
              "      <td id=\"T_e4328_row6_col6\" class=\"data row6 col6\" >0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4328_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
              "      <td id=\"T_e4328_row7_col0\" class=\"data row7 col0\" >80.00</td>\n",
              "      <td id=\"T_e4328_row7_col1\" class=\"data row7 col1\" >1.00</td>\n",
              "      <td id=\"T_e4328_row7_col2\" class=\"data row7 col2\" >1.00</td>\n",
              "      <td id=\"T_e4328_row7_col3\" class=\"data row7 col3\" >95.69</td>\n",
              "      <td id=\"T_e4328_row7_col4\" class=\"data row7 col4\" >9.00</td>\n",
              "      <td id=\"T_e4328_row7_col5\" class=\"data row7 col5\" >300.00</td>\n",
              "      <td id=\"T_e4328_row7_col6\" class=\"data row7 col6\" >1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 총 3 군집으로 분류: 비흡연자(흡연경력x), 비흡연자(흡연경력o), 흡연자\n",
        "def recategorize_smoking(smoking_status):\n",
        "    if smoking_status in ['never', 'No Info']:\n",
        "        return 'non-smoker'\n",
        "    elif smoking_status == 'current':\n",
        "        return 'current'\n",
        "    elif smoking_status in ['ever', 'former', 'not current']:\n",
        "        return 'past_smoker'\n",
        "\n",
        "# 분류 적용\n",
        "df['smoking_history'] = df['smoking_history'].apply(recategorize_smoking)\n",
        "print(df['smoking_history'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5odwtajbfWj",
        "outputId": "40b34b61-99fa-4b14-c2cd-67b02d7572e9"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "smoking_history\n",
            "non-smoker     67285\n",
            "past_smoker    19664\n",
            "current         9197\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = df.copy()"
      ],
      "metadata": {
        "id": "2j8YqEMsbhrU"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# one-hot encoding\n",
        "def perform_one_hot_encoding(df, column_name):\n",
        "    dummies = pd.get_dummies(df[column_name], prefix=column_name)\n",
        "    df = pd.concat([df.drop(column_name, axis=1), dummies], axis=1)\n",
        "\n",
        "    return df\n",
        "\n",
        "# gender와 smoking_history에 적용\n",
        "data = perform_one_hot_encoding(data, 'gender')\n",
        "data = perform_one_hot_encoding(data, 'smoking_history')"
      ],
      "metadata": {
        "id": "Iet0nrIwbkO0"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = data.corr()\n",
        "\n",
        "# Graph I.\n",
        "plt.figure(figsize=(15, 10))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5, fmt='.2f')\n",
        "plt.title(\"Correlation Matrix Heatmap\")\n",
        "plt.show()\n",
        "\n",
        "# Graph II\n",
        "corr = data.corr()\n",
        "target_corr = corr['diabetes'].drop('diabetes')\n",
        "\n",
        "# 내림차순 정렬\n",
        "target_corr_sorted = target_corr.sort_values(ascending=False)\n",
        "\n",
        "sns.set(font_scale=0.8)\n",
        "sns.set_style(\"white\")\n",
        "sns.set_palette(\"PuBuGn_d\")\n",
        "sns.heatmap(target_corr_sorted.to_frame(), cmap=\"coolwarm\", annot=True, fmt='.2f')\n",
        "plt.title('Correlation with Diabetes')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8BCxm8cObXNI",
        "outputId": "7d1a7e93-62d7-41c1-ad76-242b222033ec"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "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": [
        "X = data.iloc[:,:-1].values\n",
        "y = data.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "7WS4LTkCb53l"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=0)"
      ],
      "metadata": {
        "id": "M5vSoi1LchSi"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stand = StandardScaler()\n",
        "X_train = stand.fit_transform(X_train)\n",
        "X_test = stand.transform(X_test)"
      ],
      "metadata": {
        "id": "A9AGt8rvcnGV"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors = 5)\n",
        "knn.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "v6VUi_MjnF85",
        "outputId": "ed308b81-60d8-45a0-8cf3-1b8f30301bd2"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test = knn.predict(X_test)\n",
        "accuracy_score(y_test, y_pred_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VfSZlDvBnJFK",
        "outputId": "897e4471-9964-4f28-e558-6315f0636a39"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(6, activation='relu'))\n",
        "model.add(Dense(6, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "early_stop = EarlyStopping(monitor='val_loss', patience=3, verbose=1)\n",
        "history = model.fit(X_train, y_train, epochs=100, validation_data = (X_test, y_test), callbacks=[early_stop])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XZX5oEpvcvpO",
        "outputId": "12e5efc4-2f08-44f9-beaa-ac7439de5118"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "2404/2404 [==============================] - 6s 2ms/step - loss: 0.0544 - accuracy: 0.9895 - val_loss: 0.0050 - val_accuracy: 0.9999\n",
            "Epoch 2/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 0.0029 - accuracy: 0.9999 - val_loss: 0.0016 - val_accuracy: 0.9999\n",
            "Epoch 3/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 8.5420e-04 - accuracy: 0.9999 - val_loss: 4.8462e-04 - val_accuracy: 0.9999\n",
            "Epoch 4/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 2.3385e-04 - accuracy: 0.9999 - val_loss: 1.5729e-04 - val_accuracy: 0.9999\n",
            "Epoch 5/100\n",
            "2404/2404 [==============================] - 6s 2ms/step - loss: 1.1282e-04 - accuracy: 0.9999 - val_loss: 9.9077e-05 - val_accuracy: 0.9999\n",
            "Epoch 6/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 1.7557e-04 - accuracy: 0.9999 - val_loss: 1.1217e-04 - val_accuracy: 0.9999\n",
            "Epoch 7/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 6.4059e-05 - accuracy: 1.0000 - val_loss: 3.7719e-05 - val_accuracy: 1.0000\n",
            "Epoch 8/100\n",
            "2404/2404 [==============================] - 7s 3ms/step - loss: 2.6886e-05 - accuracy: 1.0000 - val_loss: 2.2721e-05 - val_accuracy: 1.0000\n",
            "Epoch 9/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 1.6011e-05 - accuracy: 1.0000 - val_loss: 1.3072e-05 - val_accuracy: 1.0000\n",
            "Epoch 10/100\n",
            "2404/2404 [==============================] - 6s 2ms/step - loss: 8.9017e-06 - accuracy: 1.0000 - val_loss: 7.1760e-06 - val_accuracy: 1.0000\n",
            "Epoch 11/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 4.9462e-06 - accuracy: 1.0000 - val_loss: 4.0909e-06 - val_accuracy: 1.0000\n",
            "Epoch 12/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 4.0674e-06 - accuracy: 1.0000 - val_loss: 2.1112e-04 - val_accuracy: 0.9999\n",
            "Epoch 13/100\n",
            "2404/2404 [==============================] - 6s 2ms/step - loss: 5.6538e-05 - accuracy: 1.0000 - val_loss: 2.1905e-06 - val_accuracy: 1.0000\n",
            "Epoch 14/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 1.8216e-06 - accuracy: 1.0000 - val_loss: 1.9024e-06 - val_accuracy: 1.0000\n",
            "Epoch 15/100\n",
            "2404/2404 [==============================] - 6s 2ms/step - loss: 1.4784e-06 - accuracy: 1.0000 - val_loss: 1.4215e-06 - val_accuracy: 1.0000\n",
            "Epoch 16/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 1.0629e-06 - accuracy: 1.0000 - val_loss: 9.2800e-07 - val_accuracy: 1.0000\n",
            "Epoch 17/100\n",
            "2404/2404 [==============================] - 7s 3ms/step - loss: 6.6400e-07 - accuracy: 1.0000 - val_loss: 5.7609e-07 - val_accuracy: 1.0000\n",
            "Epoch 18/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 4.0460e-07 - accuracy: 1.0000 - val_loss: 3.3829e-07 - val_accuracy: 1.0000\n",
            "Epoch 19/100\n",
            "2404/2404 [==============================] - 6s 2ms/step - loss: 9.5760e-06 - accuracy: 1.0000 - val_loss: 4.6891e-05 - val_accuracy: 1.0000\n",
            "Epoch 20/100\n",
            "2404/2404 [==============================] - 6s 3ms/step - loss: 2.7774e-06 - accuracy: 1.0000 - val_loss: 2.5198e-07 - val_accuracy: 1.0000\n",
            "Epoch 21/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 2.0297e-07 - accuracy: 1.0000 - val_loss: 2.0321e-07 - val_accuracy: 1.0000\n",
            "Epoch 22/100\n",
            "2404/2404 [==============================] - 6s 2ms/step - loss: 1.7474e-07 - accuracy: 1.0000 - val_loss: 1.8380e-07 - val_accuracy: 1.0000\n",
            "Epoch 23/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 1.4599e-07 - accuracy: 1.0000 - val_loss: 1.4000e-07 - val_accuracy: 1.0000\n",
            "Epoch 24/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 1.0277e-07 - accuracy: 1.0000 - val_loss: 9.0982e-08 - val_accuracy: 1.0000\n",
            "Epoch 25/100\n",
            "2404/2404 [==============================] - 6s 3ms/step - loss: 6.1161e-08 - accuracy: 1.0000 - val_loss: 5.0083e-08 - val_accuracy: 1.0000\n",
            "Epoch 26/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 3.5884e-08 - accuracy: 1.0000 - val_loss: 3.0424e-08 - val_accuracy: 1.0000\n",
            "Epoch 27/100\n",
            "2404/2404 [==============================] - 9s 4ms/step - loss: 2.1342e-08 - accuracy: 1.0000 - val_loss: 1.7817e-08 - val_accuracy: 1.0000\n",
            "Epoch 28/100\n",
            "2404/2404 [==============================] - 7s 3ms/step - loss: 1.1984e-08 - accuracy: 1.0000 - val_loss: 1.0035e-08 - val_accuracy: 1.0000\n",
            "Epoch 29/100\n",
            "2404/2404 [==============================] - 6s 3ms/step - loss: 5.7192e-06 - accuracy: 1.0000 - val_loss: 6.4179e-09 - val_accuracy: 1.0000\n",
            "Epoch 30/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 5.6164e-09 - accuracy: 1.0000 - val_loss: 6.4083e-09 - val_accuracy: 1.0000\n",
            "Epoch 31/100\n",
            "2404/2404 [==============================] - 6s 3ms/step - loss: 5.6066e-09 - accuracy: 1.0000 - val_loss: 6.3935e-09 - val_accuracy: 1.0000\n",
            "Epoch 32/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 5.5841e-09 - accuracy: 1.0000 - val_loss: 6.3487e-09 - val_accuracy: 1.0000\n",
            "Epoch 33/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 5.5200e-09 - accuracy: 1.0000 - val_loss: 6.2237e-09 - val_accuracy: 1.0000\n",
            "Epoch 34/100\n",
            "2404/2404 [==============================] - 6s 2ms/step - loss: 5.3190e-09 - accuracy: 1.0000 - val_loss: 5.8010e-09 - val_accuracy: 1.0000\n",
            "Epoch 35/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 4.7637e-09 - accuracy: 1.0000 - val_loss: 4.8788e-09 - val_accuracy: 1.0000\n",
            "Epoch 36/100\n",
            "2404/2404 [==============================] - 6s 2ms/step - loss: 3.8322e-09 - accuracy: 1.0000 - val_loss: 3.5939e-09 - val_accuracy: 1.0000\n",
            "Epoch 37/100\n",
            "2404/2404 [==============================] - 6s 2ms/step - loss: 2.6655e-09 - accuracy: 1.0000 - val_loss: 2.3784e-09 - val_accuracy: 1.0000\n",
            "Epoch 38/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 1.8000e-09 - accuracy: 1.0000 - val_loss: 1.6870e-09 - val_accuracy: 1.0000\n",
            "Epoch 39/100\n",
            "2404/2404 [==============================] - 6s 3ms/step - loss: 1.2787e-09 - accuracy: 1.0000 - val_loss: 1.2015e-09 - val_accuracy: 1.0000\n",
            "Epoch 40/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 9.3245e-10 - accuracy: 1.0000 - val_loss: 9.0226e-10 - val_accuracy: 1.0000\n",
            "Epoch 41/100\n",
            "2404/2404 [==============================] - 6s 3ms/step - loss: 8.5442e-10 - accuracy: 1.0000 - val_loss: 4.0468e-07 - val_accuracy: 1.0000\n",
            "Epoch 42/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 1.0244e-05 - accuracy: 1.0000 - val_loss: 1.5642e-08 - val_accuracy: 1.0000\n",
            "Epoch 43/100\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 2.6818e-09 - accuracy: 1.0000 - val_loss: 1.4848e-08 - val_accuracy: 1.0000\n",
            "Epoch 43: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hist_df=pd.DataFrame(history.history)\n",
        "\n",
        "y_vloss=hist_df['val_loss']\n",
        "y_loss=hist_df['loss']\n",
        "\n",
        "x_len = np.arange(len(y_loss))\n",
        "plt.plot(x_len, y_vloss, \"o\", c=\"red\", markersize=2, label='Testset_loss')\n",
        "plt.plot(x_len, y_loss, \"o\", c=\"blue\", markersize=2, label='Trainset_loss')\n",
        "\n",
        "plt.legend(loc='upper right')\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "_gMroHZOe8tI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "83a41fa5-a9d8-4b8f-bdb1-56a28e32ccfc"
      },
      "execution_count": 39,
      "outputs": [
        {
          "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": [
        "from sklearn.model_selection import KFold\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "kf = KFold(n_splits=5, random_state = 1, shuffle=True)\n",
        "\n",
        "cvScores=[]\n",
        "i = 1\n",
        "for train_index, test_index in kf.split(X_train):\n",
        "    X_train_fold, X_validation = X_train[train_index], X_train[test_index]\n",
        "    y_train_fold, y_validation = y[train_index], y[test_index]\n",
        "\n",
        "    model = LogisticRegression()\n",
        "    model.fit(X_train_fold, y_train_fold)\n",
        "    ypred = model.predict(X_validation)\n",
        "    acc = 100*accuracy_score(y_validation, ypred)\n",
        "    print(f'Fold: {i} | Accuracy {acc:.2f}%')\n",
        "    cvScores.append(acc)\n",
        "    i += 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nZItdpJysZne",
        "outputId": "9b6c98ec-db2e-4bfd-91ce-86c479fd1f24"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fold: 1 | Accuracy 79.65%\n",
            "Fold: 2 | Accuracy 80.05%\n",
            "Fold: 3 | Accuracy 79.21%\n",
            "Fold: 4 | Accuracy 79.65%\n",
            "Fold: 5 | Accuracy 80.04%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "layer = Dense(100, activation=\"elu\", kernel_initializer=\"he_normal\", kernel_regularizer=keras.regularizers.l2(0.01))"
      ],
      "metadata": {
        "id": "AgKSJiAsuGA0"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.models.Sequential([\n",
        "    Flatten(input_shape=[12]),\n",
        "    Dense(300, activation=\"elu\", kernel_initializer=\"he_normal\", kernel_regularizer=l2(0.01)),\n",
        "    Dense(100, activation=\"elu\", kernel_initializer=\"he_normal\", kernel_regularizer=l2(0.01)),\n",
        "    Dense(10, activation=\"softmax\", kernel_regularizer=l2(0.01))\n",
        "])\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\", metrics=[\"accuracy\"])\n",
        "n_epochs = 2\n",
        "history = model.fit(X_train, y_train, epochs=n_epochs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Erpj8xMSuHwX",
        "outputId": "56257d0f-af75-4481-c387-f82058c297c7"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "2404/2404 [==============================] - 7s 2ms/step - loss: 0.9731 - accuracy: 0.9979\n",
            "Epoch 2/2\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 0.0879 - accuracy: 0.9999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from functools import partial\n",
        "\n",
        "RegularizedDense = partial(Dense, activation=\"elu\", kernel_initializer=\"he_normal\", kernel_regularizer=l2(0.01))\n",
        "\n",
        "model = Sequential([\n",
        "    Flatten(input_shape=[12]),\n",
        "    RegularizedDense(300),\n",
        "    RegularizedDense(100),\n",
        "    RegularizedDense(10, activation=\"softmax\")\n",
        "    ])\n",
        "\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\", metrics=[\"accuracy\"])\n",
        "n_epochs = 2\n",
        "history = model.fit(X_train, y_train, epochs=n_epochs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mu4k45X8uJVg",
        "outputId": "70a99b6f-4e2f-45fb-a8c3-14fc2c52a9a2"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "2404/2404 [==============================] - 8s 3ms/step - loss: 0.9591 - accuracy: 0.9981\n",
            "Epoch 2/2\n",
            "2404/2404 [==============================] - 5s 2ms/step - loss: 0.0876 - accuracy: 0.9999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "    Flatten(input_shape=[12]),\n",
        "    Dropout(rate=0.2),\n",
        "    Dense(300, activation=\"elu\", kernel_initializer=\"he_normal\"),\n",
        "    Dropout(rate=0.2),\n",
        "    Dense(100, activation=\"elu\", kernel_initializer=\"he_normal\"),\n",
        "    Dropout(rate=0.2),\n",
        "    Dense(10, activation=\"softmax\")\n",
        "])\n",
        "\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\", metrics=[\"accuracy\"])\n",
        "n_epochs = 2\n",
        "history = model.fit(X_train, y_train, epochs=n_epochs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rE3XlKk7uLF7",
        "outputId": "1e43afbd-a9e5-40c0-bd56-3719bf47522b"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "2404/2404 [==============================] - 8s 3ms/step - loss: 0.1874 - accuracy: 0.9299\n",
            "Epoch 2/2\n",
            "2404/2404 [==============================] - 6s 2ms/step - loss: 0.1434 - accuracy: 0.9382\n"
          ]
        }
      ]
    }
  ]
}