Sentiment_Analysis_Model_FINAL_(Computer_Networks).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "NE8TiBy1PpHP"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NE8TiBy1PpHP"
      },
      "source": [
        "## Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UA-jPWIqK59Q"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jAkmz2o3Ps_f"
      },
      "source": [
        "## Importing Dataset\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oORlzsp1wiG6",
        "outputId": "f319e5f2-63fb-4ccd-92f7-e8a3176f6f74"
      },
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEyDbEwbOWgp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "b8827329-8430-404c-8f7d-99442dc4acb8"
      },
      "source": [
        "dataset = pd.read_csv('feedback-nlp.csv')\n",
        "dataset.head(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Computer Networks (Feedback)  \\\n",
              "0  The sessions were fine, but there was a lack o...   \n",
              "\n",
              "  Computer Networks (Satisfaction)  \\\n",
              "0                          Neutral   \n",
              "\n",
              "                         Machine Learning (Feedback)  \\\n",
              "0  It was an average experience overall; the facu...   \n",
              "\n",
              "  Machine Learning (Satisfaction)  \\\n",
              "0                         Neutral   \n",
              "\n",
              "                         Operating System (Feedback)  \\\n",
              "0  The faculty did not engage the class effective...   \n",
              "\n",
              "  Operating System (Satisfaction)  \\\n",
              "0                        Negative   \n",
              "\n",
              "     Computer Organization & Architecture (Feedback)  \\\n",
              "0  The teaching style was acceptable, but the cou...   \n",
              "\n",
              "  Computer Organization & Architecture (Satisfaction)  \n",
              "0                                            Neutral   "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3e26e12b-be16-4847-ba61-974dcb772188\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Computer Networks (Feedback)</th>\n",
              "      <th>Computer Networks (Satisfaction)</th>\n",
              "      <th>Machine Learning (Feedback)</th>\n",
              "      <th>Machine Learning (Satisfaction)</th>\n",
              "      <th>Operating System (Feedback)</th>\n",
              "      <th>Operating System (Satisfaction)</th>\n",
              "      <th>Computer Organization &amp; Architecture (Feedback)</th>\n",
              "      <th>Computer Organization &amp; Architecture (Satisfaction)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The sessions were fine, but there was a lack o...</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>It was an average experience overall; the facu...</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>The faculty did not engage the class effective...</td>\n",
              "      <td>Negative</td>\n",
              "      <td>The teaching style was acceptable, but the cou...</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e26e12b-be16-4847-ba61-974dcb772188')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3e26e12b-be16-4847-ba61-974dcb772188 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3e26e12b-be16-4847-ba61-974dcb772188');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"Computer Networks (Feedback)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"It was an average experience overall; the faculty did a satisfactory job, but the course could benefit from better organization.\",\n          \"I found the course to be poorly organized, with insufficient examples and unclear objectives, which hampered my learning experience.\",\n          \"The sessions were fine, but there was a lack of depth in the topics covered, which left some important areas unexplored.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Computer Networks (Satisfaction)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Neutral\",\n          \"Positive\",\n          \"Negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Machine Learning (Feedback)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"The teaching style was acceptable, but the course lacked engagement, and the learning experience felt somewhat monotonous.\",\n          \"The professor was highly knowledgeable and made the course extremely engaging by relating concepts to real-world scenarios.\",\n          \"It was an average experience overall; the faculty did a satisfactory job, but the course could benefit from better organization.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Machine Learning (Satisfaction)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Neutral\",\n          \"Positive\",\n          \"Negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Operating System (Feedback)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"The course content was very comprehensive, and the assignments were challenging yet rewarding, enhancing my understanding of the subject.\",\n          \"The course was very well-structured and the faculty provided in-depth explanations, making it easy to understand even complex topics.\",\n          \"The faculty did not engage the class effectively, and their approach to teaching felt unprepared and disengaging.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Operating System (Satisfaction)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Negative\",\n          \"Neutral\",\n          \"Positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Computer Organization & Architecture (Feedback)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"The faculty did not engage the class effectively, and their approach to teaching felt unprepared and disengaging.\",\n          \"I really appreciated the interactive teaching approach and the emphasis on practical applications, which helped solidify my learning.\",\n          \"The teaching style was acceptable, but the course lacked engagement, and the learning experience felt somewhat monotonous.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Computer Organization & Architecture (Satisfaction)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Neutral\",\n          \"Negative\",\n          \"Positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5I60cmjOPxSY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5189038-7c9b-4cb8-9f7f-878012fef8a2"
      },
      "source": [
        "dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Computer Networks (Feedback)"
      ],
      "metadata": {
        "id": "7sObM33Hu9cw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_CN = dataset[['Computer Networks (Feedback)', 'Computer Networks (Satisfaction)']]\n",
        "df_CN.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "o8xZrCfEvBNS",
        "outputId": "1cd5289d-4345-4380-9b38-45a1139b91bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Computer Networks (Feedback)  \\\n",
              "0  The sessions were fine, but there was a lack o...   \n",
              "1  The course was very well-structured and the fa...   \n",
              "2  The faculty did not engage the class effective...   \n",
              "3  The faculty did not engage the class effective...   \n",
              "4  The course was decent but could use more examp...   \n",
              "\n",
              "  Computer Networks (Satisfaction)  \n",
              "0                          Neutral  \n",
              "1                         Positive  \n",
              "2                         Negative  \n",
              "3                         Negative  \n",
              "4                          Neutral  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-522a49ac-3320-49cb-8c7d-3373a8162d2a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Computer Networks (Feedback)</th>\n",
              "      <th>Computer Networks (Satisfaction)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The sessions were fine, but there was a lack o...</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The course was very well-structured and the fa...</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The faculty did not engage the class effective...</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The faculty did not engage the class effective...</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The course was decent but could use more examp...</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-522a49ac-3320-49cb-8c7d-3373a8162d2a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-522a49ac-3320-49cb-8c7d-3373a8162d2a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-522a49ac-3320-49cb-8c7d-3373a8162d2a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-09fe69f5-f6cb-4211-8fed-b66c85349759\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-09fe69f5-f6cb-4211-8fed-b66c85349759')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      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",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-09fe69f5-f6cb-4211-8fed-b66c85349759 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_CN",
              "summary": "{\n  \"name\": \"df_CN\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"Computer Networks (Feedback)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"It was an average experience overall; the faculty did a satisfactory job, but the course could benefit from better organization.\",\n          \"I found the course to be poorly organized, with insufficient examples and unclear objectives, which hampered my learning experience.\",\n          \"The sessions were fine, but there was a lack of depth in the topics covered, which left some important areas unexplored.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Computer Networks (Satisfaction)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Neutral\",\n          \"Positive\",\n          \"Negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding Categorical Target: Computer Networks (Satisfaction)\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "df_CN['Computer Networks (Satisfaction)'] = le.fit_transform(df_CN['Computer Networks (Satisfaction)'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g16oZmJmyu2X",
        "outputId": "68bbcb12-5786-4d29-e9b6-3a275887f1df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-e1f365e0e78d>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_CN['Computer Networks (Satisfaction)'] = le.fit_transform(df_CN['Computer Networks (Satisfaction)'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_CN.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ifsWscuYy1j3",
        "outputId": "88e372f9-3a7c-4aa2-801e-563aa054c1e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Computer Networks (Feedback)  \\\n",
              "0  The sessions were fine, but there was a lack o...   \n",
              "1  The course was very well-structured and the fa...   \n",
              "2  The faculty did not engage the class effective...   \n",
              "3  The faculty did not engage the class effective...   \n",
              "4  The course was decent but could use more examp...   \n",
              "\n",
              "   Computer Networks (Satisfaction)  \n",
              "0                                 1  \n",
              "1                                 2  \n",
              "2                                 0  \n",
              "3                                 0  \n",
              "4                                 1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-30a0f670-905e-4573-b7d7-ae4f814c2953\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Computer Networks (Feedback)</th>\n",
              "      <th>Computer Networks (Satisfaction)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The sessions were fine, but there was a lack o...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The course was very well-structured and the fa...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The faculty did not engage the class effective...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The faculty did not engage the class effective...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The course was decent but could use more examp...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30a0f670-905e-4573-b7d7-ae4f814c2953')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-30a0f670-905e-4573-b7d7-ae4f814c2953 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-30a0f670-905e-4573-b7d7-ae4f814c2953');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e42d7060-acaf-4bd9-97f0-1c8252865448\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e42d7060-acaf-4bd9-97f0-1c8252865448')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      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",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e42d7060-acaf-4bd9-97f0-1c8252865448 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_CN",
              "summary": "{\n  \"name\": \"df_CN\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"Computer Networks (Feedback)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"It was an average experience overall; the faculty did a satisfactory job, but the course could benefit from better organization.\",\n          \"I found the course to be poorly organized, with insufficient examples and unclear objectives, which hampered my learning experience.\",\n          \"The sessions were fine, but there was a lack of depth in the topics covered, which left some important areas unexplored.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Computer Networks (Satisfaction)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j-RW_6DZP-Np"
      },
      "source": [
        "### Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A7yu8yC0OlYB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4837f85-bfec-4262-b278-952b4da393dc"
      },
      "source": [
        "import re # regular expression import\n",
        "import nltk\n",
        "\n",
        "nltk.download('stopwords')\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "ps = PorterStemmer()\n",
        "\n",
        "all_stopwords = stopwords.words('english') # storing stop word in all_stopwords\n",
        "all_stopwords.remove('not')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hXURUSLTOocO"
      },
      "source": [
        "corpus=[] # store students data post data cleaning\n",
        "\n",
        "for i in range(0, 2000): # runs row by row to clean every data we have in our dataset\n",
        "  review = re.sub('[^a-zA-Z]', ' ', df_CN['Computer Networks (Feedback)'][i]) # dropping all special symbols\n",
        "  review = review.lower() # converting to lower case\n",
        "  review = review.split()\n",
        "  review = [ps.stem(word) for word in review if not word in set(all_stopwords)]\n",
        "  review = ' '.join(review)\n",
        "  corpus.append(review)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cmX9tJn98h7M"
      },
      "source": [
        "# corpus"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1TctQ_uZQM9A"
      },
      "source": [
        "### Data Transformation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "byh_MglBOsG0"
      },
      "source": [
        "# convert cleaned data into bag of words\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer(max_features = 8) # we are taking top 'max_features' most frequent tokens and dropping the left overs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_CN.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "jvilB66p0hFs",
        "outputId": "52716930-2005-47e5-9133-a293442fe447"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Computer Networks (Feedback)  \\\n",
              "0  The sessions were fine, but there was a lack o...   \n",
              "1  The course was very well-structured and the fa...   \n",
              "\n",
              "   Computer Networks (Satisfaction)  \n",
              "0                                 1  \n",
              "1                                 2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-71436165-5725-4f99-a82a-603bc36fb16c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Computer Networks (Feedback)</th>\n",
              "      <th>Computer Networks (Satisfaction)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The sessions were fine, but there was a lack o...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The course was very well-structured and the fa...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71436165-5725-4f99-a82a-603bc36fb16c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-71436165-5725-4f99-a82a-603bc36fb16c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-71436165-5725-4f99-a82a-603bc36fb16c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b50ba3ce-fa27-41e6-a388-652201045d57\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b50ba3ce-fa27-41e6-a388-652201045d57')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      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",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b50ba3ce-fa27-41e6-a388-652201045d57 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_CN",
              "summary": "{\n  \"name\": \"df_CN\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"Computer Networks (Feedback)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"It was an average experience overall; the faculty did a satisfactory job, but the course could benefit from better organization.\",\n          \"I found the course to be poorly organized, with insufficient examples and unclear objectives, which hampered my learning experience.\",\n          \"The sessions were fine, but there was a lack of depth in the topics covered, which left some important areas unexplored.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Computer Networks (Satisfaction)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KCT0kv6hCN9Y"
      },
      "source": [
        "X = cv.fit_transform(corpus).toarray()\n",
        "y = df_CN.iloc[:, -1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M3XV1EuB0vE_",
        "outputId": "00f2ea52-fad1-4c89-90f7-f141ed602bc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Cp4ILXI0xy5",
        "outputId": "c7787084-3ba9-40bf-8b27-ef611c43abed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000,)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "# Encoding 'Computer Networks (Satisfaction)'\n",
        "ct = ColumnTransformer(transformers = [('encoder', OneHotEncoder(sparse_output=False), [0])], remainder = 'passthrough')\n",
        "y = np.array(ct.fit_transform(y.reshape(-1,1)))"
      ],
      "metadata": {
        "id": "Lj3iVWr-4Wim"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ByyPjrCGQYm0"
      },
      "source": [
        "### Splitting Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g04rl7BZOuGc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ab9ecda-44d8-4ec1-ded4-aa08ee805f46"
      },
      "source": [
        "#%60 for train, %28 for val, %12 for test\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_val,Y_train,Y_val = train_test_split(X, y, random_state=42, test_size= 0.4)\n",
        "\n",
        "#Split test and val dataset.\n",
        "X_val,X_test,Y_val,Y_test = train_test_split(X_val,Y_val,random_state = 42,test_size = 0.3)\n",
        "print(f\"{X_train.shape}, {X_test.shape}, {X_val.shape}\")\n",
        "\n",
        "# # Dimension Reduction\n",
        "# from sklearn.decomposition import PCA\n",
        "# pca = PCA(n_components = 5)\n",
        "# X_train = pca.fit_transform(X_train)\n",
        "# X_val = pca.fit_transform(X_val)\n",
        "# X_test = pca.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1200, 8), (240, 8), (560, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdQY92UPQmsq"
      },
      "source": [
        "### ANN Definition\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXEUR3trOwrv"
      },
      "source": [
        "import tensorflow as tf\n",
        "from keras.layers import Dense, Dropout\n",
        "from keras.models import Sequential\n",
        "def build_model():\n",
        "    model = Sequential()\n",
        "    #Input layer\n",
        "    model.add(Dense(32, activation = \"relu\", kernel_initializer = \"he_normal\",input_dim = len(X_train[0])))\n",
        "    #Hidden layer 1\n",
        "    model.add(Dense(64, activation = \"relu\", kernel_initializer = \"he_normal\"))\n",
        "    model.add(Dropout(0.3))\n",
        "    #Hidden layer 2\n",
        "    model.add(Dense(128, activation = \"relu\", kernel_initializer = \"he_normal\"))\n",
        "    model.add(Dropout(0.3))\n",
        "    #Hidden layer 3\n",
        "    model.add(Dense(64, activation = \"relu\", kernel_initializer = \"he_normal\"))\n",
        "    model.add(Dropout(0.3))\n",
        "    #Hidden layer 4\n",
        "    model.add(Dense(32, activation = \"relu\", kernel_initializer = \"he_normal\"))\n",
        "    model.add(Dropout(0.3))\n",
        "    #Output layer\n",
        "    model.add(Dense(3, activation = \"softmax\", kernel_initializer = \"he_normal\"))\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = build_model()\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "iSBIJxry5Fzs",
        "outputId": "29d603cd-b49d-4808-cccf-cf599f0caa83"
      },
      "execution_count": null,
      "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": "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;34m32\u001b[0m)                  │             \u001b[38;5;34m288\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m2,112\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\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;34m128\u001b[0m)                 │           \u001b[38;5;34m8,320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m8,256\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │           \u001b[38;5;34m2,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)                   │              \u001b[38;5;34m99\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\">32</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">288</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\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,112</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\">64</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\">128</span>)                 │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,320</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\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_3 (<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\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_2 (<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\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (<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\">32</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_3 (<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\">32</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (<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\">3</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">99</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m21,155\u001b[0m (82.64 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\">21,155</span> (82.64 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m21,155\u001b[0m (82.64 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\">21,155</span> (82.64 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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = tf.keras.optimizers.Adam(learning_rate = 0.0001),\n",
        "             loss = \"categorical_crossentropy\",metrics = [\"accuracy\"])\n",
        "\n",
        "history = model.fit(X_train, Y_train, batch_size = 64, epochs = 200,\n",
        "                         validation_data=(X_val,Y_val), callbacks = tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\",patience=3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9UE-SF3-5I9-",
        "outputId": "72be9704-2fa3-4c85-9830-4b88b453499a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 51ms/step - accuracy: 0.3762 - loss: 1.4389 - val_accuracy: 0.5589 - val_loss: 1.0726\n",
            "Epoch 2/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.3383 - loss: 1.3835 - val_accuracy: 0.5482 - val_loss: 1.0420\n",
            "Epoch 3/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.3880 - loss: 1.2831 - val_accuracy: 0.4643 - val_loss: 1.0261\n",
            "Epoch 4/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - accuracy: 0.4242 - loss: 1.2013 - val_accuracy: 0.6464 - val_loss: 1.0153\n",
            "Epoch 5/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - accuracy: 0.3892 - loss: 1.2163 - val_accuracy: 0.5696 - val_loss: 1.0038\n",
            "Epoch 6/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - accuracy: 0.4001 - loss: 1.1988 - val_accuracy: 0.6339 - val_loss: 0.9940\n",
            "Epoch 7/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - accuracy: 0.4359 - loss: 1.1424 - val_accuracy: 0.6339 - val_loss: 0.9837\n",
            "Epoch 8/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - accuracy: 0.3967 - loss: 1.1574 - val_accuracy: 0.6339 - val_loss: 0.9731\n",
            "Epoch 9/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.4481 - loss: 1.0693 - val_accuracy: 0.6339 - val_loss: 0.9626\n",
            "Epoch 10/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - accuracy: 0.4352 - loss: 1.0877 - val_accuracy: 0.6339 - val_loss: 0.9519\n",
            "Epoch 11/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - accuracy: 0.5023 - loss: 1.0347 - val_accuracy: 0.7107 - val_loss: 0.9379\n",
            "Epoch 12/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - accuracy: 0.4449 - loss: 1.0661 - val_accuracy: 0.7107 - val_loss: 0.9238\n",
            "Epoch 13/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5066 - loss: 1.0048 - val_accuracy: 0.7804 - val_loss: 0.9084\n",
            "Epoch 14/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.4886 - loss: 1.0294 - val_accuracy: 0.7804 - val_loss: 0.8937\n",
            "Epoch 15/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 23ms/step - accuracy: 0.5079 - loss: 1.0103 - val_accuracy: 0.7804 - val_loss: 0.8795\n",
            "Epoch 16/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 24ms/step - accuracy: 0.5383 - loss: 0.9858 - val_accuracy: 0.7804 - val_loss: 0.8631\n",
            "Epoch 17/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.5168 - loss: 0.9887 - val_accuracy: 0.7804 - val_loss: 0.8481\n",
            "Epoch 18/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5261 - loss: 0.9776 - val_accuracy: 0.7804 - val_loss: 0.8320\n",
            "Epoch 19/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5463 - loss: 0.9362 - val_accuracy: 0.7804 - val_loss: 0.8135\n",
            "Epoch 20/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5744 - loss: 0.9134 - val_accuracy: 0.7804 - val_loss: 0.7975\n",
            "Epoch 21/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5874 - loss: 0.9099 - val_accuracy: 0.7804 - val_loss: 0.7810\n",
            "Epoch 22/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5871 - loss: 0.8784 - val_accuracy: 0.7804 - val_loss: 0.7604\n",
            "Epoch 23/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5656 - loss: 0.9135 - val_accuracy: 0.7804 - val_loss: 0.7426\n",
            "Epoch 24/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5920 - loss: 0.8708 - val_accuracy: 0.7804 - val_loss: 0.7241\n",
            "Epoch 25/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.6329 - loss: 0.8374 - val_accuracy: 0.7804 - val_loss: 0.7056\n",
            "Epoch 26/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6167 - loss: 0.8501 - val_accuracy: 0.7804 - val_loss: 0.6895\n",
            "Epoch 27/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.6326 - loss: 0.8046 - val_accuracy: 0.7804 - val_loss: 0.6712\n",
            "Epoch 28/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.6896 - loss: 0.7282 - val_accuracy: 0.7804 - val_loss: 0.6484\n",
            "Epoch 29/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.6283 - loss: 0.7970 - val_accuracy: 0.7804 - val_loss: 0.6293\n",
            "Epoch 30/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6695 - loss: 0.7416 - val_accuracy: 0.7804 - val_loss: 0.6095\n",
            "Epoch 31/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6626 - loss: 0.7185 - val_accuracy: 0.7804 - val_loss: 0.5895\n",
            "Epoch 32/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.6874 - loss: 0.7205 - val_accuracy: 0.7804 - val_loss: 0.5669\n",
            "Epoch 33/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6854 - loss: 0.7058 - val_accuracy: 0.7804 - val_loss: 0.5472\n",
            "Epoch 34/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.6757 - loss: 0.7051 - val_accuracy: 0.8643 - val_loss: 0.5279\n",
            "Epoch 35/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7070 - loss: 0.6775 - val_accuracy: 0.9321 - val_loss: 0.5081\n",
            "Epoch 36/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6873 - loss: 0.7002 - val_accuracy: 0.9321 - val_loss: 0.4905\n",
            "Epoch 37/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7335 - loss: 0.6240 - val_accuracy: 0.9321 - val_loss: 0.4705\n",
            "Epoch 38/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7291 - loss: 0.6498 - val_accuracy: 0.9321 - val_loss: 0.4537\n",
            "Epoch 39/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7318 - loss: 0.5820 - val_accuracy: 0.9321 - val_loss: 0.4340\n",
            "Epoch 40/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7541 - loss: 0.5808 - val_accuracy: 0.9321 - val_loss: 0.4141\n",
            "Epoch 41/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7530 - loss: 0.5961 - val_accuracy: 0.9321 - val_loss: 0.3991\n",
            "Epoch 42/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7534 - loss: 0.6215 - val_accuracy: 0.9321 - val_loss: 0.3861\n",
            "Epoch 43/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7585 - loss: 0.5538 - val_accuracy: 0.9321 - val_loss: 0.3719\n",
            "Epoch 44/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7692 - loss: 0.5325 - val_accuracy: 0.9321 - val_loss: 0.3572\n",
            "Epoch 45/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7598 - loss: 0.5725 - val_accuracy: 0.9321 - val_loss: 0.3433\n",
            "Epoch 46/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7895 - loss: 0.5180 - val_accuracy: 0.9321 - val_loss: 0.3290\n",
            "Epoch 47/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7799 - loss: 0.5151 - val_accuracy: 0.9321 - val_loss: 0.3158\n",
            "Epoch 48/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8037 - loss: 0.4725 - val_accuracy: 0.9321 - val_loss: 0.3024\n",
            "Epoch 49/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7880 - loss: 0.4885 - val_accuracy: 0.9321 - val_loss: 0.2908\n",
            "Epoch 50/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8071 - loss: 0.4516 - val_accuracy: 0.9321 - val_loss: 0.2782\n",
            "Epoch 51/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8148 - loss: 0.4503 - val_accuracy: 0.9321 - val_loss: 0.2671\n",
            "Epoch 52/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8154 - loss: 0.4546 - val_accuracy: 0.9321 - val_loss: 0.2577\n",
            "Epoch 53/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8490 - loss: 0.4013 - val_accuracy: 0.9321 - val_loss: 0.2464\n",
            "Epoch 54/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8233 - loss: 0.4184 - val_accuracy: 0.9321 - val_loss: 0.2371\n",
            "Epoch 55/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8342 - loss: 0.4026 - val_accuracy: 0.9321 - val_loss: 0.2300\n",
            "Epoch 56/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8472 - loss: 0.3660 - val_accuracy: 0.9321 - val_loss: 0.2214\n",
            "Epoch 57/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8562 - loss: 0.3488 - val_accuracy: 0.9321 - val_loss: 0.2119\n",
            "Epoch 58/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.8378 - loss: 0.3667 - val_accuracy: 0.9321 - val_loss: 0.2029\n",
            "Epoch 59/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8401 - loss: 0.3813 - val_accuracy: 0.9321 - val_loss: 0.1970\n",
            "Epoch 60/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8454 - loss: 0.3724 - val_accuracy: 0.9321 - val_loss: 0.1923\n",
            "Epoch 61/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.8551 - loss: 0.3558 - val_accuracy: 0.9321 - val_loss: 0.1857\n",
            "Epoch 62/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.8650 - loss: 0.3347 - val_accuracy: 0.9321 - val_loss: 0.1806\n",
            "Epoch 63/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8665 - loss: 0.3144 - val_accuracy: 0.9321 - val_loss: 0.1749\n",
            "Epoch 64/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.8630 - loss: 0.3097 - val_accuracy: 0.9321 - val_loss: 0.1703\n",
            "Epoch 65/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8709 - loss: 0.2910 - val_accuracy: 0.9321 - val_loss: 0.1656\n",
            "Epoch 66/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8740 - loss: 0.3203 - val_accuracy: 0.9321 - val_loss: 0.1606\n",
            "Epoch 67/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8884 - loss: 0.2978 - val_accuracy: 0.9321 - val_loss: 0.1568\n",
            "Epoch 68/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8757 - loss: 0.2904 - val_accuracy: 0.9321 - val_loss: 0.1527\n",
            "Epoch 69/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.8569 - loss: 0.3098 - val_accuracy: 0.9321 - val_loss: 0.1502\n",
            "Epoch 70/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8888 - loss: 0.2631 - val_accuracy: 0.9232 - val_loss: 0.1469\n",
            "Epoch 71/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8903 - loss: 0.2628 - val_accuracy: 0.9232 - val_loss: 0.1438\n",
            "Epoch 72/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.8873 - loss: 0.2672 - val_accuracy: 0.9232 - val_loss: 0.1410\n",
            "Epoch 73/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9062 - loss: 0.2397 - val_accuracy: 0.9232 - val_loss: 0.1380\n",
            "Epoch 74/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9057 - loss: 0.2309 - val_accuracy: 0.9232 - val_loss: 0.1355\n",
            "Epoch 75/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9152 - loss: 0.2340 - val_accuracy: 0.9232 - val_loss: 0.1330\n",
            "Epoch 76/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8984 - loss: 0.2321 - val_accuracy: 0.9232 - val_loss: 0.1311\n",
            "Epoch 77/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8975 - loss: 0.2364 - val_accuracy: 0.9232 - val_loss: 0.1294\n",
            "Epoch 78/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9132 - loss: 0.2256 - val_accuracy: 0.9232 - val_loss: 0.1274\n",
            "Epoch 79/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8909 - loss: 0.2404 - val_accuracy: 0.9232 - val_loss: 0.1263\n",
            "Epoch 80/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8966 - loss: 0.2316 - val_accuracy: 0.9232 - val_loss: 0.1247\n",
            "Epoch 81/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9064 - loss: 0.2336 - val_accuracy: 0.9232 - val_loss: 0.1237\n",
            "Epoch 82/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8906 - loss: 0.2304 - val_accuracy: 0.9232 - val_loss: 0.1225\n",
            "Epoch 83/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.9096 - loss: 0.2035 - val_accuracy: 0.9232 - val_loss: 0.1208\n",
            "Epoch 84/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9050 - loss: 0.2145 - val_accuracy: 0.9232 - val_loss: 0.1197\n",
            "Epoch 85/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.9021 - loss: 0.2242 - val_accuracy: 0.9232 - val_loss: 0.1192\n",
            "Epoch 86/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.8984 - loss: 0.2132 - val_accuracy: 0.9232 - val_loss: 0.1187\n",
            "Epoch 87/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9334 - loss: 0.1750 - val_accuracy: 0.9232 - val_loss: 0.1179\n",
            "Epoch 88/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.9140 - loss: 0.1938 - val_accuracy: 0.9232 - val_loss: 0.1172\n",
            "Epoch 89/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9158 - loss: 0.1845 - val_accuracy: 0.9232 - val_loss: 0.1167\n",
            "Epoch 90/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9243 - loss: 0.1759 - val_accuracy: 0.9232 - val_loss: 0.1157\n",
            "Epoch 91/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - accuracy: 0.9136 - loss: 0.1834 - val_accuracy: 0.9232 - val_loss: 0.1148\n",
            "Epoch 92/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.8993 - loss: 0.2163 - val_accuracy: 0.9232 - val_loss: 0.1137\n",
            "Epoch 93/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - accuracy: 0.9228 - loss: 0.1659 - val_accuracy: 0.9232 - val_loss: 0.1134\n",
            "Epoch 94/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - accuracy: 0.9055 - loss: 0.1753 - val_accuracy: 0.9232 - val_loss: 0.1132\n",
            "Epoch 95/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.9227 - loss: 0.1785 - val_accuracy: 0.9232 - val_loss: 0.1129\n",
            "Epoch 96/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9224 - loss: 0.1763 - val_accuracy: 0.9232 - val_loss: 0.1122\n",
            "Epoch 97/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9042 - loss: 0.1928 - val_accuracy: 0.9232 - val_loss: 0.1125\n",
            "Epoch 98/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9151 - loss: 0.1754 - val_accuracy: 0.9232 - val_loss: 0.1121\n",
            "Epoch 99/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9365 - loss: 0.1396 - val_accuracy: 0.9232 - val_loss: 0.1117\n",
            "Epoch 100/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9288 - loss: 0.1572 - val_accuracy: 0.9232 - val_loss: 0.1112\n",
            "Epoch 101/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9174 - loss: 0.1752 - val_accuracy: 0.9232 - val_loss: 0.1108\n",
            "Epoch 102/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9201 - loss: 0.1730 - val_accuracy: 0.9232 - val_loss: 0.1102\n",
            "Epoch 103/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9258 - loss: 0.1753 - val_accuracy: 0.9232 - val_loss: 0.1097\n",
            "Epoch 104/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9313 - loss: 0.1534 - val_accuracy: 0.9232 - val_loss: 0.1097\n",
            "Epoch 105/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9378 - loss: 0.1485 - val_accuracy: 0.9232 - val_loss: 0.1094\n",
            "Epoch 106/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9162 - loss: 0.1850 - val_accuracy: 0.9232 - val_loss: 0.1090\n",
            "Epoch 107/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.9329 - loss: 0.1540 - val_accuracy: 0.9232 - val_loss: 0.1092\n",
            "Epoch 108/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9120 - loss: 0.1663 - val_accuracy: 0.9232 - val_loss: 0.1087\n",
            "Epoch 109/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9070 - loss: 0.1762 - val_accuracy: 0.9232 - val_loss: 0.1088\n",
            "Epoch 110/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9195 - loss: 0.1666 - val_accuracy: 0.9232 - val_loss: 0.1093\n",
            "Epoch 111/200\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.9320 - loss: 0.1340 - val_accuracy: 0.9232 - val_loss: 0.1088\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig = plt.figure(figsize = (12,5))\n",
        "\n",
        "epochs = len(history.history[\"accuracy\"])\n",
        "plt.subplot(1,2,1)\n",
        "plt.title(\"Train Accuracy and Val Accuracy\")\n",
        "plt.plot(range(epochs),history.history[\"accuracy\"])\n",
        "plt.plot(range(epochs),history.history[\"val_accuracy\"])\n",
        "plt.legend([\"accuracy\",\"val_accuracy\"])\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.title(\"Train Loss and Val Loss\")\n",
        "plt.plot(range(epochs),history.history[\"loss\"])\n",
        "plt.plot(range(epochs),history.history[\"val_loss\"])\n",
        "plt.legend([\"loss\",\"val_loss\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "77Rin5st5PZK",
        "outputId": "e7be7e9b-03ab-4b75-aa64-951722313c2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79b3bf0ba740>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LUpvhmMXQ0LJ"
      },
      "source": [
        "## Model Performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fB1-BGFuO17X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e114b25d-477f-4009-b778-d3c7285d553a"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, average_precision_score\n",
        "\n",
        "# Step 1: Evaluate the model on the test dataset\n",
        "X_test_reshaped = X_test.reshape(-1, X_test.shape[1])\n",
        "loss, accuracy = model.evaluate(X_test_reshaped, Y_test, verbose=0)\n",
        "\n",
        "print(f\"Model Loss: {loss:.4f}\")\n",
        "print(f\"Model Accuracy: {accuracy:.4f}\")\n",
        "\n",
        "# Step 2: Generate predictions\n",
        "Y_pred = model.predict(X_test_reshaped)\n",
        "Y_pred_classes = Y_pred.argmax(axis=1)\n",
        "Y_test_classes = Y_test.argmax(axis=1)\n",
        "\n",
        "# Step 3: Generate a detailed classification report\n",
        "print('\\n------------------------------------------------\\n')\n",
        "report = classification_report(Y_test_classes, Y_pred_classes, target_names=['Class 0', 'Class 1', 'Class 2'])\n",
        "print(\"\\nClassification Report:\\n\", report)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Loss: 0.1109\n",
            "Model Accuracy: 0.9250\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\n",
            "------------------------------------------------\n",
            "\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     Class 0       0.81      1.00      0.89        76\n",
            "     Class 1       1.00      0.79      0.88        85\n",
            "     Class 2       1.00      1.00      1.00        79\n",
            "\n",
            "    accuracy                           0.93       240\n",
            "   macro avg       0.94      0.93      0.93       240\n",
            "weighted avg       0.94      0.93      0.92       240\n",
            "\n"
          ]
        }
      ]
    }
  ]
}