COSC442_Final_Project.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    {
      "cell_type": "markdown",
      "source": [
        "# Introduction\n",
        "\n",
        "A Comparative Study of Three Intelligent Techniques for the\n",
        "Disease Prediction Domain\n",
        "\n",
        "---\n",
        "\n",
        "**Done By:** Ali Abdulla (A00786), Mariam Hussein (A00039), and Rawan Albinzayed (A00080)\n",
        "\n",
        "**Institution:** American University of Bahrain\n",
        "\n",
        "**Course:** COSC 442 - Artificial Intelligence\n",
        "\n",
        "**Submission Date:** April 21, 2024\n",
        "\n",
        "---\n",
        "\n",
        "This file contains three implementations of machine learning algorithms (Artificial Neural Networks, Fuzzy Logic, and Support Vector Machines) and are tested in their efficiency in predicting heart disease in patients given a particular dataset. The dataset was collected researchers at UC Irvine and can be found in the following link: [https://archive.ics.uci.edu/dataset/45/heart+disease](https://archive.ics.uci.edu/dataset/45/heart+disease)."
      ],
      "metadata": {
        "id": "EHaLv16fsJVH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Components\n",
        "Import all the following modules and libraries to be used during this project including the dataset."
      ],
      "metadata": {
        "id": "pyTOX4ea7g1b"
      }
    },
    {
      "cell_type": "code",
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        "id": "l-dDiCSB7YU_",
        "outputId": "3532dc05-eccb-4f07-e286-8026e7bcf5a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Shape of DataFrame: (303, 14)\n",
            "age          67.0\n",
            "sex           1.0\n",
            "cp            4.0\n",
            "trestbps    160.0\n",
            "chol        286.0\n",
            "fbs           0.0\n",
            "restecg       2.0\n",
            "thalach     108.0\n",
            "exang         1.0\n",
            "oldpeak       1.5\n",
            "slope         2.0\n",
            "ca            3.0\n",
            "thal          3.0\n",
            "num           2.0\n",
            "Name: 1, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "#Install Required Modules\n",
        "!pip install keras\n",
        "!pip install matplotlib\n",
        "!pip install scikit-fuzzy\n",
        "!pip install sklearn\n",
        "!pip install tensorflow\n",
        "!pip install ucimlrepo\n",
        "\n",
        "#Importing Libraries\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.optimizers import Adam\n",
        "from keras.layers import Dropout\n",
        "from keras.utils import to_categorical\n",
        "from keras import regularizers\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from pandas.plotting import scatter_matrix\n",
        "import skfuzzy as fuzz\n",
        "from skfuzzy import control as ctrl\n",
        "import sklearn\n",
        "from sklearn import model_selection\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import SVC\n",
        "import sys\n",
        "from tensorflow import keras\n",
        "import time\n",
        "\n",
        "#Import the Dataset\n",
        "from ucimlrepo import fetch_ucirepo\n",
        "heart_disease = fetch_ucirepo(id=45)\n",
        "heart_disease = pd.read_csv('./heart_disease.csv')\n",
        "\n",
        "#Verify the dataset has been imported\n",
        "print('Shape of DataFrame: {}'.format(heart_disease.shape))\n",
        "print(heart_disease.loc[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing\n",
        "Clean the dataset by removing any missing variables, any tuples that include missing data, and change the features to be of numerical types.\n",
        "\n"
      ],
      "metadata": {
        "id": "CkOOFzOmEK9X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Remove any missing variables\n",
        "data = heart_disease[~heart_disease.isin(['?'])]\n",
        "\n",
        "#Remove the tuples that have missing data\n",
        "data = data.dropna(axis=0)\n",
        "\n",
        "#Change the dataset to numeric type\n",
        "data = data.apply(pd.to_numeric)\n",
        "data.dtypes\n",
        "\n",
        "#Change the target feature name\n",
        "data = data.rename(columns={\"num\":\"target\"})\n",
        "\n",
        "#Verify\n",
        "print(data.shape)\n",
        "print(data.dtypes)\n",
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "wNaQhyfbESOk",
        "outputId": "0f5ea250-234a-4ee2-8dc8-2645535472b5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(297, 14)\n",
            "age           int64\n",
            "sex           int64\n",
            "cp            int64\n",
            "trestbps      int64\n",
            "chol          int64\n",
            "fbs           int64\n",
            "restecg       int64\n",
            "thalach       int64\n",
            "exang         int64\n",
            "oldpeak     float64\n",
            "slope         int64\n",
            "ca          float64\n",
            "thal        float64\n",
            "target        int64\n",
            "dtype: object\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age         sex          cp    trestbps        chol         fbs  \\\n",
              "count  297.000000  297.000000  297.000000  297.000000  297.000000  297.000000   \n",
              "mean    54.542088    0.676768    3.158249  131.693603  247.350168    0.144781   \n",
              "std      9.049736    0.468500    0.964859   17.762806   51.997583    0.352474   \n",
              "min     29.000000    0.000000    1.000000   94.000000  126.000000    0.000000   \n",
              "25%     48.000000    0.000000    3.000000  120.000000  211.000000    0.000000   \n",
              "50%     56.000000    1.000000    3.000000  130.000000  243.000000    0.000000   \n",
              "75%     61.000000    1.000000    4.000000  140.000000  276.000000    0.000000   \n",
              "max     77.000000    1.000000    4.000000  200.000000  564.000000    1.000000   \n",
              "\n",
              "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
              "count  297.000000  297.000000  297.000000  297.000000  297.000000  297.000000   \n",
              "mean     0.996633  149.599327    0.326599    1.055556    1.602694    0.676768   \n",
              "std      0.994914   22.941562    0.469761    1.166123    0.618187    0.938965   \n",
              "min      0.000000   71.000000    0.000000    0.000000    1.000000    0.000000   \n",
              "25%      0.000000  133.000000    0.000000    0.000000    1.000000    0.000000   \n",
              "50%      1.000000  153.000000    0.000000    0.800000    2.000000    0.000000   \n",
              "75%      2.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
              "max      2.000000  202.000000    1.000000    6.200000    3.000000    3.000000   \n",
              "\n",
              "             thal      target  \n",
              "count  297.000000  297.000000  \n",
              "mean     4.730640    0.946128  \n",
              "std      1.938629    1.234551  \n",
              "min      3.000000    0.000000  \n",
              "25%      3.000000    0.000000  \n",
              "50%      3.000000    0.000000  \n",
              "75%      7.000000    2.000000  \n",
              "max      7.000000    4.000000  "
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
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              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.542088</td>\n",
              "      <td>0.676768</td>\n",
              "      <td>3.158249</td>\n",
              "      <td>131.693603</td>\n",
              "      <td>247.350168</td>\n",
              "      <td>0.144781</td>\n",
              "      <td>0.996633</td>\n",
              "      <td>149.599327</td>\n",
              "      <td>0.326599</td>\n",
              "      <td>1.055556</td>\n",
              "      <td>1.602694</td>\n",
              "      <td>0.676768</td>\n",
              "      <td>4.730640</td>\n",
              "      <td>0.946128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.049736</td>\n",
              "      <td>0.468500</td>\n",
              "      <td>0.964859</td>\n",
              "      <td>17.762806</td>\n",
              "      <td>51.997583</td>\n",
              "      <td>0.352474</td>\n",
              "      <td>0.994914</td>\n",
              "      <td>22.941562</td>\n",
              "      <td>0.469761</td>\n",
              "      <td>1.166123</td>\n",
              "      <td>0.618187</td>\n",
              "      <td>0.938965</td>\n",
              "      <td>1.938629</td>\n",
              "      <td>1.234551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>56.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>243.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>276.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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    \"samples\": [\n          297.0,\n          1.0555555555555556,\n          1.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"slope\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.44132795462548,\n        \"min\": 0.6181867696375288,\n        \"max\": 297.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          297.0,\n          1.6026936026936027,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ca\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.72638124231159,\n        \"min\": 0.0,\n        \"max\": 297.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          297.0,\n          0.6767676767676768,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 103.52434901368404,\n        \"min\": 1.9386286411484317,\n        \"max\": 297.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.730639730639731,\n          7.0,\n          1.9386286411484317\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.60095155147584,\n        \"min\": 0.0,\n        \"max\": 297.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          297.0,\n          0.9461279461279462,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training and Testing Sets\n",
        "\n",
        "Create two different where the first includes the whole preprocessed dataset without the target feature and the second contains the same but includes the target feature. Then normalize the first array before splitting both of them into training and testing datasets (80% for training and 20% for testing). And then, the target variable of the second array will be converted into a categorical feature."
      ],
      "metadata": {
        "id": "-INSkzvZGnTo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Create an array that includes the whole preprocessed dataset excluding the target feature\n",
        "X = np.array(data.drop(['target'], axis=1))\n",
        "\n",
        "#Create an array that includes the whole preprocessed dataset including the target feature\n",
        "Y = np.array(data['target'])\n",
        "\n",
        "#Normalize the first array\n",
        "mean = X.mean(axis=0)\n",
        "X -= mean\n",
        "std = X.std(axis=0)\n",
        "X /= std\n",
        "\n",
        "#Split the two array for training (80%) and testing (20%)\n",
        "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, stratify=Y, random_state=42, test_size = 0.2)\n",
        "\n",
        "#Convert dataset to categorical types\n",
        "Y_train = to_categorical(Y_train, num_classes=None)\n",
        "Y_test = to_categorical(Y_test, num_classes=None)\n",
        "\n",
        "#Output the two arrays\n",
        "print(\"Normalized Training Array without Target Feature\")\n",
        "print (X_train)\n",
        "print(\"Normalized Training Array with Target Feature\")\n",
        "print (Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IojkkPhMGrTr",
        "outputId": "12581ec0-0838-4629-d232-94fa71a0380e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Normalized Training Array without Target Feature\n",
            "[[ 0.27205887 -1.44697961 -1.20245913 ...  0.6437811   0.34482438\n",
            "  -0.89422007]\n",
            " [-1.72030648  0.69109474 -0.16428947 ... -0.97658319 -0.72197605\n",
            "  -0.89422007]\n",
            " [-1.27755862  0.69109474 -0.16428947 ... -0.97658319  0.34482438\n",
            "  -0.89422007]\n",
            " ...\n",
            " [-1.49893255 -1.44697961 -0.16428947 ... -0.97658319 -0.72197605\n",
            "  -0.89422007]\n",
            " [ 1.04686761  0.69109474 -0.16428947 ...  0.6437811  -0.72197605\n",
            "   1.17257652]\n",
            " [-0.83481077  0.69109474 -0.16428947 ... -0.97658319 -0.72197605\n",
            "  -0.89422007]]\n",
            "Normalized Training Array with Target Feature\n",
            "[[0. 1. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0.]\n",
            " ...\n",
            " [1. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Artificial Neural Network\n",
        "\n",
        "Using the Sequential API imported from the Keras library, the function called GenerateModel will create a linear stack of layers. The \"Dense\" layer has 16 neurons with an input dimension of 13 for each feature from the postprocessed dataset. The hidden layer called \"Droput\" has a dropout rate of 25%. This is done to prevent overfitting by randomly changing some of the inputs to the value zero. The next layer is the \"Dense\" layer and uses the ReLU activation function. After that, another dropout layer is added for the same reason as earlier. Finally, another dense layer is added, but this time with 5 neurons which represent that classification feature. After this model is created, it then compiled with the loss function \"categorical_crossentropy\" since it is a multiclass classification problem. Finally, the optimizer RMSprop is used as the adaptive learning rate optimization algorithm. The metric used to monitor it during the epochs of the training process is accuracy.\n",
        "\n",
        "Once this model is created, it is fit with the training data obtained earlier and validated with the testing data also obtained earlier.\n"
      ],
      "metadata": {
        "id": "_m_2wv-PIpOp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Time Complexity (Time)\n",
        "start_time_ANN = time.time()\n",
        "\n",
        "# Convert target feature into binary classification\n",
        "Y_train_binary = Y_train.copy()\n",
        "Y_test_binary = Y_test.copy()\n",
        "Y_train_binary[Y_train_binary > 0] = 1\n",
        "Y_test_binary[Y_test_binary > 0] = 1\n",
        "\n",
        "#print(Y_train_binary[:20])\n",
        "\n",
        "def create_binary_model():\n",
        "    # Create Model\n",
        "    model = Sequential()\n",
        "    model.add(Dense(16, input_dim=13, kernel_initializer='normal',  kernel_regularizer=regularizers.l2(0.001),activation='relu'))\n",
        "    model.add(Dropout(0.25))\n",
        "    model.add(Dense(8, kernel_initializer='normal',  kernel_regularizer=regularizers.l2(0.001),activation='relu'))\n",
        "    model.add(Dropout(0.25))\n",
        "    model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "    # Compile Model\n",
        "    adam = Adam(learning_rate=0.001)\n",
        "    model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
        "    return model\n",
        "\n",
        "binary_model = create_binary_model()\n",
        "\n",
        "# Fit the model\n",
        "history = binary_model.fit(X_train, Y_train_binary[:,0], validation_data=(X_test, Y_test_binary[:,0]), epochs=100, batch_size=10, verbose = 1)\n",
        "\n",
        "# Space Complexity (Memory Usage)\n",
        "model_size_ANN = sys.getsizeof(binary_model)\n",
        "\n",
        "# Measure elapsed time\n",
        "elapsed_time_ANN = time.time() - start_time_ANN\n",
        "print(binary_model.summary())\n",
        "print(\"Time taken to execute the code: {:.3f} seconds\".format(elapsed_time_ANN))\n",
        "print(\"Space complexity of the model: {:.3f} bytes\".format(model_size_ANN))\n",
        "\n",
        "%matplotlib inline\n",
        "# Model accuracy\n",
        "plt.plot(history.history['accuracy'], color='red')\n",
        "plt.plot(history.history['val_accuracy'], color='blue')\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['train', 'test'])\n",
        "plt.show()\n",
        "\n",
        "# Model Loss\n",
        "plt.plot(history.history['loss'], color='red')\n",
        "plt.plot(history.history['val_loss'], color='blue')\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['train', 'test'])\n",
        "plt.show()\n",
        "\n",
        "# Predictions on test data\n",
        "Y_pred_binary = binary_model.predict(X_test)\n",
        "\n",
        "# Convert predictions to binary labels\n",
        "Y_pred_binary = (Y_pred_binary > 0.5).astype(int)\n",
        "\n",
        "# Convert target feature to binary labels\n",
        "Y_test_binary = (Y_test_binary > 0).astype(int)\n",
        "\n",
        "# **Reshape Y_pred_binary**\n",
        "Y_pred_binary = Y_pred_binary.flatten() # Flatten Y_pred_binary\n",
        "Y_pred_binary = (Y_pred_binary > 0.5).astype(int)  # Apply threshold\n",
        "\n",
        "# Convert Y_test_binary to binary labels (New line)\n",
        "Y_test_binary = np.argmax(Y_test_binary, axis=1)  # Convert to binary labels\n",
        "\n",
        "# Generate classification report\n",
        "classification_report_ann = classification_report(Y_test_binary, Y_pred_binary)\n",
        "print(\"Classification Report (ANN):\\n\", classification_report_ann)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "M7VNxAuJsCod",
        "outputId": "7b62e2b2-e93c-4134-d568-ad18149325f0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - accuracy: 0.5754 - loss: 0.6907 - val_accuracy: 0.8500 - val_loss: 0.6687\n",
            "Epoch 2/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7331 - loss: 0.6731 - val_accuracy: 0.8500 - val_loss: 0.6425\n",
            "Epoch 3/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7623 - loss: 0.6419 - val_accuracy: 0.8333 - val_loss: 0.6029\n",
            "Epoch 4/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7904 - loss: 0.6168 - val_accuracy: 0.8833 - val_loss: 0.5594\n",
            "Epoch 5/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8294 - loss: 0.5724 - val_accuracy: 0.8833 - val_loss: 0.5168\n",
            "Epoch 6/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8144 - loss: 0.5321 - val_accuracy: 0.8833 - val_loss: 0.4701\n",
            "Epoch 7/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8488 - loss: 0.4891 - val_accuracy: 0.8833 - val_loss: 0.4348\n",
            "Epoch 8/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8057 - loss: 0.4894 - val_accuracy: 0.8667 - val_loss: 0.4054\n",
            "Epoch 9/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8488 - loss: 0.4617 - val_accuracy: 0.8667 - val_loss: 0.3816\n",
            "Epoch 10/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.8319 - loss: 0.4499 - val_accuracy: 0.8667 - val_loss: 0.3637\n",
            "Epoch 11/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.8168 - loss: 0.4324 - val_accuracy: 0.8667 - val_loss: 0.3537\n",
            "Epoch 12/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8658 - loss: 0.4126 - val_accuracy: 0.8667 - val_loss: 0.3477\n",
            "Epoch 13/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.8590 - loss: 0.4045 - val_accuracy: 0.8500 - val_loss: 0.3396\n",
            "Epoch 14/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8486 - loss: 0.4051 - val_accuracy: 0.8500 - val_loss: 0.3347\n",
            "Epoch 15/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9034 - loss: 0.3518 - val_accuracy: 0.8500 - val_loss: 0.3348\n",
            "Epoch 16/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.8631 - loss: 0.3950 - val_accuracy: 0.8500 - val_loss: 0.3321\n",
            "Epoch 17/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8637 - loss: 0.4023 - val_accuracy: 0.8500 - val_loss: 0.3305\n",
            "Epoch 18/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.8144 - loss: 0.4289 - val_accuracy: 0.8500 - val_loss: 0.3315\n",
            "Epoch 19/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.8873 - loss: 0.3858 - val_accuracy: 0.8500 - val_loss: 0.3351\n",
            "Epoch 20/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8997 - loss: 0.3687 - val_accuracy: 0.8500 - val_loss: 0.3373\n",
            "Epoch 21/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8441 - loss: 0.4341 - val_accuracy: 0.8500 - val_loss: 0.3388\n",
            "Epoch 22/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8196 - loss: 0.4191 - val_accuracy: 0.8500 - val_loss: 0.3431\n",
            "Epoch 23/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8572 - loss: 0.3755 - val_accuracy: 0.8500 - val_loss: 0.3428\n",
            "Epoch 24/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9017 - loss: 0.3429 - val_accuracy: 0.8500 - val_loss: 0.3455\n",
            "Epoch 25/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8832 - loss: 0.3431 - val_accuracy: 0.8500 - val_loss: 0.3469\n",
            "Epoch 26/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9124 - loss: 0.2859 - val_accuracy: 0.8500 - val_loss: 0.3496\n",
            "Epoch 27/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8894 - loss: 0.3231 - val_accuracy: 0.8333 - val_loss: 0.3492\n",
            "Epoch 28/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8964 - loss: 0.3812 - val_accuracy: 0.8333 - val_loss: 0.3524\n",
            "Epoch 29/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8793 - loss: 0.3449 - val_accuracy: 0.8333 - val_loss: 0.3543\n",
            "Epoch 30/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8713 - loss: 0.3327 - val_accuracy: 0.8333 - val_loss: 0.3549\n",
            "Epoch 31/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8688 - loss: 0.3757 - val_accuracy: 0.8333 - val_loss: 0.3541\n",
            "Epoch 32/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8815 - loss: 0.3221 - val_accuracy: 0.8333 - val_loss: 0.3541\n",
            "Epoch 33/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8865 - loss: 0.3708 - val_accuracy: 0.8333 - val_loss: 0.3579\n",
            "Epoch 34/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8877 - loss: 0.3157 - val_accuracy: 0.8333 - val_loss: 0.3580\n",
            "Epoch 35/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.8403 - loss: 0.3685 - val_accuracy: 0.8333 - val_loss: 0.3604\n",
            "Epoch 36/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8973 - loss: 0.2973 - val_accuracy: 0.8333 - val_loss: 0.3629\n",
            "Epoch 37/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9048 - loss: 0.3155 - val_accuracy: 0.8333 - val_loss: 0.3675\n",
            "Epoch 38/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.8416 - loss: 0.4473 - val_accuracy: 0.8333 - val_loss: 0.3689\n",
            "Epoch 39/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8876 - loss: 0.4127 - val_accuracy: 0.8333 - val_loss: 0.3699\n",
            "Epoch 40/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.8926 - loss: 0.3692 - val_accuracy: 0.8333 - val_loss: 0.3731\n",
            "Epoch 41/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8714 - loss: 0.3370 - val_accuracy: 0.8333 - val_loss: 0.3750\n",
            "Epoch 42/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.8846 - loss: 0.3435 - val_accuracy: 0.8333 - val_loss: 0.3779\n",
            "Epoch 43/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8335 - loss: 0.4210 - val_accuracy: 0.8333 - val_loss: 0.3833\n",
            "Epoch 44/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9253 - loss: 0.2565 - val_accuracy: 0.8333 - val_loss: 0.3854\n",
            "Epoch 45/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.8913 - loss: 0.2675 - val_accuracy: 0.8333 - val_loss: 0.3864\n",
            "Epoch 46/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.8918 - loss: 0.3140 - val_accuracy: 0.8333 - val_loss: 0.3902\n",
            "Epoch 47/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.8638 - loss: 0.3582 - val_accuracy: 0.8333 - val_loss: 0.3914\n",
            "Epoch 48/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.8666 - loss: 0.3718 - val_accuracy: 0.8333 - val_loss: 0.3938\n",
            "Epoch 49/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8979 - loss: 0.2869 - val_accuracy: 0.8333 - val_loss: 0.3927\n",
            "Epoch 50/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.8999 - loss: 0.3312 - val_accuracy: 0.8333 - val_loss: 0.3943\n",
            "Epoch 51/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - accuracy: 0.8394 - loss: 0.4254 - val_accuracy: 0.8333 - val_loss: 0.3973\n",
            "Epoch 52/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8904 - loss: 0.2937 - val_accuracy: 0.8333 - val_loss: 0.4007\n",
            "Epoch 53/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.8810 - loss: 0.3286 - val_accuracy: 0.8333 - val_loss: 0.4009\n",
            "Epoch 54/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.8767 - loss: 0.3577 - val_accuracy: 0.8333 - val_loss: 0.4009\n",
            "Epoch 55/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8735 - loss: 0.3205 - val_accuracy: 0.8333 - val_loss: 0.3998\n",
            "Epoch 56/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8989 - loss: 0.3234 - val_accuracy: 0.8333 - val_loss: 0.4012\n",
            "Epoch 57/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8887 - loss: 0.3443 - val_accuracy: 0.8333 - val_loss: 0.4088\n",
            "Epoch 58/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8817 - loss: 0.3222 - val_accuracy: 0.8333 - val_loss: 0.4050\n",
            "Epoch 59/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8430 - loss: 0.3397 - val_accuracy: 0.8333 - val_loss: 0.4069\n",
            "Epoch 60/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8391 - loss: 0.3375 - val_accuracy: 0.8333 - val_loss: 0.4083\n",
            "Epoch 61/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - accuracy: 0.8781 - loss: 0.3409 - val_accuracy: 0.8333 - val_loss: 0.4082\n",
            "Epoch 62/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.8964 - loss: 0.2810 - val_accuracy: 0.8333 - val_loss: 0.4084\n",
            "Epoch 63/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8898 - loss: 0.2983 - val_accuracy: 0.8333 - val_loss: 0.4139\n",
            "Epoch 64/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8863 - loss: 0.3242 - val_accuracy: 0.8333 - val_loss: 0.4160\n",
            "Epoch 65/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9161 - loss: 0.2600 - val_accuracy: 0.8333 - val_loss: 0.4232\n",
            "Epoch 66/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9047 - loss: 0.2427 - val_accuracy: 0.8333 - val_loss: 0.4262\n",
            "Epoch 67/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8666 - loss: 0.3785 - val_accuracy: 0.8333 - val_loss: 0.4206\n",
            "Epoch 68/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8931 - loss: 0.3032 - val_accuracy: 0.8333 - val_loss: 0.4205\n",
            "Epoch 69/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8573 - loss: 0.3617 - val_accuracy: 0.8333 - val_loss: 0.4150\n",
            "Epoch 70/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8787 - loss: 0.3327 - val_accuracy: 0.8333 - val_loss: 0.4170\n",
            "Epoch 71/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8729 - loss: 0.3463 - val_accuracy: 0.8333 - val_loss: 0.4152\n",
            "Epoch 72/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8843 - loss: 0.2816 - val_accuracy: 0.8333 - val_loss: 0.4211\n",
            "Epoch 73/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9103 - loss: 0.2761 - val_accuracy: 0.8333 - val_loss: 0.4240\n",
            "Epoch 74/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8997 - loss: 0.2879 - val_accuracy: 0.8333 - val_loss: 0.4307\n",
            "Epoch 75/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8737 - loss: 0.2924 - val_accuracy: 0.8333 - val_loss: 0.4352\n",
            "Epoch 76/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8882 - loss: 0.3043 - val_accuracy: 0.8333 - val_loss: 0.4433\n",
            "Epoch 77/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8691 - loss: 0.3345 - val_accuracy: 0.8333 - val_loss: 0.4418\n",
            "Epoch 78/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8792 - loss: 0.2845 - val_accuracy: 0.8333 - val_loss: 0.4398\n",
            "Epoch 79/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8747 - loss: 0.2902 - val_accuracy: 0.8333 - val_loss: 0.4429\n",
            "Epoch 80/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8786 - loss: 0.3721 - val_accuracy: 0.8333 - val_loss: 0.4468\n",
            "Epoch 81/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8758 - loss: 0.3253 - val_accuracy: 0.8333 - val_loss: 0.4474\n",
            "Epoch 82/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9076 - loss: 0.3457 - val_accuracy: 0.8333 - val_loss: 0.4472\n",
            "Epoch 83/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8433 - loss: 0.3849 - val_accuracy: 0.8333 - val_loss: 0.4450\n",
            "Epoch 84/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9089 - loss: 0.2557 - val_accuracy: 0.8333 - val_loss: 0.4469\n",
            "Epoch 85/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8697 - loss: 0.3432 - val_accuracy: 0.8333 - val_loss: 0.4536\n",
            "Epoch 86/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8843 - loss: 0.3660 - val_accuracy: 0.8333 - val_loss: 0.4577\n",
            "Epoch 87/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8770 - loss: 0.3258 - val_accuracy: 0.8333 - val_loss: 0.4570\n",
            "Epoch 88/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8895 - loss: 0.3218 - val_accuracy: 0.8333 - val_loss: 0.4533\n",
            "Epoch 89/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8702 - loss: 0.3085 - val_accuracy: 0.8333 - val_loss: 0.4531\n",
            "Epoch 90/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8248 - loss: 0.3745 - val_accuracy: 0.8333 - val_loss: 0.4544\n",
            "Epoch 91/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8793 - loss: 0.3430 - val_accuracy: 0.8333 - val_loss: 0.4532\n",
            "Epoch 92/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9040 - loss: 0.2756 - val_accuracy: 0.8333 - val_loss: 0.4538\n",
            "Epoch 93/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8730 - loss: 0.3533 - val_accuracy: 0.8333 - val_loss: 0.4548\n",
            "Epoch 94/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8763 - loss: 0.2985 - val_accuracy: 0.8333 - val_loss: 0.4615\n",
            "Epoch 95/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8598 - loss: 0.3521 - val_accuracy: 0.8333 - val_loss: 0.4599\n",
            "Epoch 96/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8852 - loss: 0.3443 - val_accuracy: 0.8333 - val_loss: 0.4688\n",
            "Epoch 97/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8941 - loss: 0.2711 - val_accuracy: 0.8333 - val_loss: 0.4714\n",
            "Epoch 98/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8650 - loss: 0.3861 - val_accuracy: 0.8333 - val_loss: 0.4668\n",
            "Epoch 99/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9149 - loss: 0.2408 - val_accuracy: 0.8500 - val_loss: 0.4684\n",
            "Epoch 100/100\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9044 - loss: 0.2786 - val_accuracy: 0.8500 - val_loss: 0.4728\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)                  │             \u001b[38;5;34m224\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)                   │             \u001b[38;5;34m136\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m9\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">136</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m740\u001b[0m (2.89 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">740</span> (2.89 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m369\u001b[0m (1.44 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">369</span> (1.44 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m371\u001b[0m (1.45 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">371</span> (1.45 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "None\n",
            "Time taken to execute the code: 37.580 seconds\n",
            "Space complexity of the model: 48.000 bytes\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
            "Classification Report (ANN):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.15      0.12      0.14        32\n",
            "           1       0.15      0.45      0.23        11\n",
            "           2       0.00      0.00      0.00         7\n",
            "           3       0.00      0.00      0.00         7\n",
            "           4       0.00      0.00      0.00         3\n",
            "\n",
            "    accuracy                           0.15        60\n",
            "   macro avg       0.06      0.12      0.07        60\n",
            "weighted avg       0.11      0.15      0.11        60\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Support Vector Machines"
      ],
      "metadata": {
        "id": "GE39BAQcvLup"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Start measuring time\n",
        "start_time_SVM = time.time()\n",
        "\n",
        "# Data splitting (ensure the same random state for reproducibility)\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Data preprocessing (scaling)\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Convert target feature into 1D array for multiclass labels\n",
        "Y_train_1d = Y_train.ravel()\n",
        "Y_test_1d = Y_test.ravel()\n",
        "\n",
        "# Check the shape of input features and target variable\n",
        "if X_train_scaled.shape[0] != Y_train_1d.shape[0]:\n",
        "    raise ValueError(\"Number of samples in X_train_scaled and Y_train_1d are not consistent.\")\n",
        "\n",
        "# Define the parameter grid for grid search\n",
        "param_grid = {'C': [0.1, 1, 10, 100],\n",
        "              'gamma': [1, 0.1, 0.01, 0.001],\n",
        "              'kernel': ['rbf', 'linear', 'poly', 'sigmoid']}\n",
        "\n",
        "# Create SVM classifier\n",
        "svm_classifier = SVC()\n",
        "\n",
        "# Instantiate GridSearchCV\n",
        "grid_search = GridSearchCV(svm_classifier, param_grid, cv=5, scoring='accuracy')\n",
        "\n",
        "# Perform Grid Search Cross-Validation\n",
        "grid_search.fit(X_train_scaled, Y_train_1d)\n",
        "\n",
        "# Print the best parameters and best score\n",
        "print(\"Best Parameters:\", grid_search.best_params_)\n",
        "print(\"Best Score:\", grid_search.best_score_)\n",
        "\n",
        "# Get the best estimator\n",
        "best_svm_classifier = grid_search.best_estimator_\n",
        "\n",
        "# Predictions on test data using the best estimator\n",
        "svm_predictions_test = best_svm_classifier.predict(X_test_scaled)\n",
        "\n",
        "# Evaluation\n",
        "print(\"\\nSVM Classifier - Test Accuracy (after tuning):\", best_svm_classifier.score(X_test_scaled, Y_test_1d))\n",
        "print(\"\\nClassification Report (Test Data):\\n\", classification_report(Y_test_1d, svm_predictions_test))\n",
        "\n",
        "# Measure memory usage\n",
        "model_size_SVM = sys.getsizeof(best_svm_classifier)\n",
        "\n",
        "# Measure elapsed time\n",
        "elapsed_time_SVM = time.time() - start_time_SVM\n",
        "\n",
        "print(\"Time taken to execute the code: {:.3f} seconds\".format(elapsed_time_SVM))\n",
        "print(\"Space complexity of the model: {:.3f} bytes\".format(model_size_SVM))\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "# Compute confusion matrix\n",
        "conf_matrix = confusion_matrix(Y_test_1d, svm_predictions_test)\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Label\")\n",
        "plt.ylabel(\"True Label\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1wu_nDkquJ8d",
        "outputId": "283c62b9-17cf-4735-b6c2-f6f2aefb07b4"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters: {'C': 0.1, 'gamma': 1, 'kernel': 'linear'}\n",
            "Best Score: 0.5823581560283688\n",
            "\n",
            "SVM Classifier - Test Accuracy (after tuning): 0.6333333333333333\n",
            "\n",
            "Classification Report (Test Data):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.97      0.86        36\n",
            "           1       0.33      0.11      0.17         9\n",
            "           2       0.33      0.40      0.36         5\n",
            "           3       0.00      0.00      0.00         7\n",
            "           4       0.00      0.00      0.00         3\n",
            "\n",
            "    accuracy                           0.63        60\n",
            "   macro avg       0.29      0.30      0.28        60\n",
            "weighted avg       0.54      0.63      0.57        60\n",
            "\n",
            "Time taken to execute the code: 8.246 seconds\n",
            "Space complexity of the model: 48.000 bytes\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Random Forest\n"
      ],
      "metadata": {
        "id": "E8cu9sifuBVj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Start measuring time\n",
        "start_time_RF = time.time()\n",
        "\n",
        "# Data splitting (ensure the same random state for reproducibility)\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Data preprocessing (scaling)\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Convert target feature into 1D array for multiclass labels\n",
        "Y_train_1d = Y_train.ravel()\n",
        "Y_test_1d = Y_test.ravel()\n",
        "\n",
        "# Define the parameter grid for randomized search\n",
        "param_dist = {'n_estimators': [50, 100, 200],\n",
        "              'max_depth': [None, 10, 20, 30],\n",
        "              'min_samples_split': [2, 5, 10],\n",
        "              'min_samples_leaf': [1, 2, 4],\n",
        "              'bootstrap': [True, False],\n",
        "              'criterion': ['gini', 'entropy']}\n",
        "\n",
        "# Create Random Forest classifier\n",
        "rf_classifier = RandomForestClassifier()\n",
        "\n",
        "# Instantiate RandomizedSearchCV\n",
        "random_search = RandomizedSearchCV(rf_classifier, param_dist, n_iter=100, cv=5, scoring='accuracy', random_state=42)\n",
        "\n",
        "# Perform Randomized Search Cross-Validation\n",
        "random_search.fit(X_train_scaled, Y_train_1d)\n",
        "\n",
        "# Print the best parameters and best score\n",
        "print(\"Best Parameters:\", random_search.best_params_)\n",
        "print(\"Best Score:\", random_search.best_score_)\n",
        "\n",
        "# Get the best estimator\n",
        "best_rf_classifier = random_search.best_estimator_\n",
        "\n",
        "# Predictions on test data using the best estimator\n",
        "rf_predictions_test = best_rf_classifier.predict(X_test_scaled)\n",
        "\n",
        "# Evaluation\n",
        "print(\"\\nRandom Forest Classifier - Test Accuracy (after tuning):\", best_rf_classifier.score(X_test_scaled, Y_test_1d))\n",
        "print(\"\\nClassification Report (Test Data):\\n\", classification_report(Y_test_1d, rf_predictions_test))\n",
        "\n",
        "# Measure memory usage\n",
        "model_size_RF = sys.getsizeof(best_rf_classifier)\n",
        "\n",
        "# Measure elapsed time\n",
        "elapsed_time_RF = time.time() - start_time_RF\n",
        "\n",
        "print(\"Time taken to execute the code: {:.3f} seconds\".format(elapsed_time_RF))\n",
        "print(\"Space complexity of the model: {:.3f} bytes\".format(model_size_RF))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w34DKRH68Tfc",
        "outputId": "835c3bf4-ddbf-49f8-d661-a0404ff122ce"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters: {'n_estimators': 200, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_depth': 20, 'criterion': 'gini', 'bootstrap': True}\n",
            "Best Score: 0.5822695035460994\n",
            "\n",
            "Random Forest Classifier - Test Accuracy (after tuning): 0.6333333333333333\n",
            "\n",
            "Classification Report (Test Data):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      1.00      0.85        36\n",
            "           1       0.00      0.00      0.00         9\n",
            "           2       0.25      0.20      0.22         5\n",
            "           3       0.33      0.14      0.20         7\n",
            "           4       0.00      0.00      0.00         3\n",
            "\n",
            "    accuracy                           0.63        60\n",
            "   macro avg       0.26      0.27      0.25        60\n",
            "weighted avg       0.50      0.63      0.55        60\n",
            "\n",
            "Time taken to execute the code: 99.243 seconds\n",
            "Space complexity of the model: 48.000 bytes\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        }
      ]
    }
  ]
}