Sentiment_Analysis_Model_FINAL_(KNN).ipynb

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  "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\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score"
      ],
      "execution_count": 995,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jAkmz2o3Ps_f"
      },
      "source": [
        "## Importing Dataset\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
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        "id": "oORlzsp1wiG6",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4d503d2-ce4e-423a-a337-1677ed92d799"
      },
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
      "execution_count": 996,
      "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"
          ]
        }
      ]
    },
    {
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          "height": 168
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        "outputId": "ed604923-fe31-42d5-ffda-3c7c3f23a792"
      },
      "source": [
        "dataset = pd.read_csv('feedback-nlp.csv')\n",
        "dataset.head(1)"
      ],
      "execution_count": 997,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                        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   "
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              "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}"
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        "outputId": "36d6e523-fbd2-4c63-d913-702b23829da0"
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        "dataset.shape"
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          "data": {
            "text/plain": [
              "(2000, 8)"
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    {
      "cell_type": "code",
      "source": [
        "df0 = dataset[['Computer Networks (Feedback)', 'Computer Networks (Satisfaction)']]\n",
        "df1 = dataset[['Machine Learning (Feedback)', 'Machine Learning (Satisfaction)']]\n",
        "df2 = dataset[['Operating System (Feedback)', 'Operating System (Satisfaction)']]\n",
        "df3 = dataset[['Computer Organization & Architecture (Feedback)', 'Computer Organization & Architecture (Satisfaction)']]"
      ],
      "metadata": {
        "id": "o8xZrCfEvBNS"
      },
      "execution_count": 999,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding Categorical Target: Computer Networks (Satisfaction)\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le0 = LabelEncoder()\n",
        "le1 = LabelEncoder()\n",
        "le2 = LabelEncoder()\n",
        "le3 = LabelEncoder()\n",
        "\n",
        "df0['Computer Networks (Satisfaction)'] = le0.fit_transform(df0['Computer Networks (Satisfaction)'])\n",
        "df1['Machine Learning (Satisfaction)'] = le1.fit_transform(df1['Machine Learning (Satisfaction)'])\n",
        "df2['Operating System (Satisfaction)'] = le2.fit_transform(df2['Operating System (Satisfaction)'])\n",
        "df3['Computer Organization & Architecture (Satisfaction)'] = le3.fit_transform(df3['Computer Organization & Architecture (Satisfaction)'])"
      ],
      "metadata": {
        "id": "g16oZmJmyu2X",
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "35acc906-ac57-4760-f453-6a6c5351a598"
      },
      "execution_count": 1000,
      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "<ipython-input-1000-fe713886a5da>:8: 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",
            "  df0['Computer Networks (Satisfaction)'] = le0.fit_transform(df0['Computer Networks (Satisfaction)'])\n",
            "<ipython-input-1000-fe713886a5da>:9: 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",
            "  df1['Machine Learning (Satisfaction)'] = le1.fit_transform(df1['Machine Learning (Satisfaction)'])\n",
            "<ipython-input-1000-fe713886a5da>:10: 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",
            "  df2['Operating System (Satisfaction)'] = le2.fit_transform(df2['Operating System (Satisfaction)'])\n",
            "<ipython-input-1000-fe713886a5da>:11: 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",
            "  df3['Computer Organization & Architecture (Satisfaction)'] = le3.fit_transform(df3['Computer Organization & Architecture (Satisfaction)'])\n"
          ]
        }
      ]
    },
    {
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        "df0.head()"
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          "height": 206
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              "                        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  "
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              "\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-43cc6ef0-0544-415c-b8bc-26c17e244fbc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-43cc6ef0-0544-415c-b8bc-26c17e244fbc')\"\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-43cc6ef0-0544-415c-b8bc-26c17e244fbc 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": "df0",
              "summary": "{\n  \"name\": \"df0\",\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": 1001
        }
      ]
    },
    {
      "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": "af8ea68b-2945-4e63-df27-8a2dcec8ad63"
      },
      "source": [
        "import re # Regular expression import\n",
        "import nltk\n",
        "\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "all_stopwords = stopwords.words('english') # Storing stop word in all_stopwords\n",
        "all_stopwords.remove('not')"
      ],
      "execution_count": 1002,
      "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",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hXURUSLTOocO"
      },
      "source": [
        "## Store students data post data cleaning\n",
        "corpus0=[] # For Computer Networks\n",
        "corpus1=[] # For Machine Learning\n",
        "corpus2=[] # For Operating System\n",
        "corpus3=[] # For Computer Organization & Architecture\n",
        "\n",
        "# For Computer Networks\n",
        "for i in range(0, 2000): # Runs row by row to clean every data we have in our dataset\n",
        "  review0 = re.sub('[^a-zA-Z]', ' ', df0['Computer Networks (Feedback)'][i]) # Dropping all special symbols\n",
        "  review0 = review0.lower() # Converting to lower case\n",
        "  review0 = review0.split()\n",
        "  review0 = [lemmatizer.lemmatize(word) for word in review0 if not word in set(all_stopwords)]\n",
        "  review0 = ' '.join(review0)\n",
        "  corpus0.append(review0)\n",
        "\n",
        "# For Machine Learning\n",
        "for i in range(0, 2000): # Runs row by row to clean every data we have in our dataset\n",
        "  review1 = re.sub('[^a-zA-Z]', ' ', df1['Machine Learning (Feedback)'][i]) # Dropping all special symbols\n",
        "  review1 = review1.lower() # Converting to lower case\n",
        "  review1 = review1.split()\n",
        "  review1 = [lemmatizer.lemmatize(word) for word in review1 if not word in set(all_stopwords)]\n",
        "  review1 = ' '.join(review1)\n",
        "  corpus1.append(review1)\n",
        "\n",
        "# For Operating System\n",
        "for i in range(0, 2000): # Runs row by row to clean every data we have in our dataset\n",
        "  review2 = re.sub('[^a-zA-Z]', ' ', df2['Operating System (Feedback)'][i]) # Dropping all special symbols\n",
        "  review2 = review2.lower() # Converting to lower case\n",
        "  review2 = review2.split()\n",
        "  review2 = [lemmatizer.lemmatize(word) for word in review2 if not word in set(all_stopwords)]\n",
        "  review2 = ' '.join(review2)\n",
        "  corpus2.append(review2)\n",
        "\n",
        "# For Computer Organization & Architecture\n",
        "for i in range(0, 2000): # Runs row by row to clean every data we have in our dataset\n",
        "  review3 = re.sub('[^a-zA-Z]', ' ', df3['Computer Organization & Architecture (Feedback)'][i]) # Dropping all special symbols\n",
        "  review3 = review3.lower() # Converting to lower case\n",
        "  review3 = review3.split()\n",
        "  review3 = [lemmatizer.lemmatize(word) for word in review3 if not word in set(all_stopwords)]\n",
        "  review3 = ' '.join(review3)\n",
        "  corpus3.append(review3)"
      ],
      "execution_count": 1003,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cmX9tJn98h7M"
      },
      "source": [
        "# corpus"
      ],
      "execution_count": 1004,
      "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 = 11) # We are taking top 'max_features' most frequent tokens and dropping the left overs"
      ],
      "execution_count": 1005,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KCT0kv6hCN9Y"
      },
      "source": [
        "# For Computer Networks\n",
        "X0 = cv.fit_transform(corpus0).toarray()\n",
        "y0 = df0.iloc[:, -1].values\n",
        "\n",
        "# For Machine Learning\n",
        "X1 = cv.fit_transform(corpus1).toarray()\n",
        "y1 = df1.iloc[:, -1].values\n",
        "\n",
        "# For Operating System\n",
        "X2 = cv.fit_transform(corpus2).toarray()\n",
        "y2 = df2.iloc[:, -1].values\n",
        "\n",
        "# For Computer Organization & Architecture\n",
        "X3 = cv.fit_transform(corpus3).toarray()\n",
        "y3 = df3.iloc[:, -1].values"
      ],
      "execution_count": 1006,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X0.shape"
      ],
      "metadata": {
        "id": "M3XV1EuB0vE_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "feb74d0b-736a-466a-c692-1e02436f02d8"
      },
      "execution_count": 1007,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 1007
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y0.shape"
      ],
      "metadata": {
        "id": "5Cp4ILXI0xy5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8026c85e-f6ed-4c86-a14b-31cef147d224"
      },
      "execution_count": 1008,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000,)"
            ]
          },
          "metadata": {},
          "execution_count": 1008
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ByyPjrCGQYm0"
      },
      "source": [
        "### Splitting Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g04rl7BZOuGc"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train0, X_test0, y_train0, y_test0 = train_test_split(X0, y0, test_size = 0.3, random_state = 42) # For Computer Networks\n",
        "X_train1, X_test1, y_train1, y_test1 = train_test_split(X1, y1, test_size = 0.3, random_state = 42) # For Machine Learning\n",
        "X_train2, X_test2, y_train2, y_test2 = train_test_split(X2, y2, test_size = 0.3, random_state = 42) # For Operating System\n",
        "X_train3, X_test3, y_train3, y_test3 = train_test_split(X3, y3, test_size = 0.3, random_state = 42) # For Computer Organization & Architecture"
      ],
      "execution_count": 1009,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdQY92UPQmsq"
      },
      "source": [
        "### Model Fitting (KNN)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXEUR3trOwrv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "7ceb1d7c-8d65-4b74-efae-f8359ce1a35f"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# For Computer Networks\n",
        "classifier0 = KNeighborsClassifier(n_neighbors=7)\n",
        "classifier0.fit(X_train0, y_train0)\n",
        "\n",
        "# For Machine Learning\n",
        "classifier1 = KNeighborsClassifier(n_neighbors=7)\n",
        "classifier1.fit(X_train1, y_train1)\n",
        "\n",
        "# For Operating System\n",
        "classifier2 = KNeighborsClassifier(n_neighbors=7)\n",
        "classifier2.fit(X_train2, y_train2)\n",
        "\n",
        "# For Computer Organization & Architecture\n",
        "classifier3 = KNeighborsClassifier(n_neighbors=7)\n",
        "classifier3.fit(X_train3, y_train3)"
      ],
      "execution_count": 1010,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=7)"
            ],
            "text/html": [
              "<style>#sk-container-id-58 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-58 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-58 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-58 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-58 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-58 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-58 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-58 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-58 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-58 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-58 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-58 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-58 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-58 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-58 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-58 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-58 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-58 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-58 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-58 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-58 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-58 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-58 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-58 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-58 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-58 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-58\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-58\" type=\"checkbox\" checked><label for=\"sk-estimator-id-58\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 1010
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LUpvhmMXQ0LJ"
      },
      "source": [
        "###Model Performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fB1-BGFuO17X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f527f22-c504-47ae-f63f-f71286126878"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "# For Computer Networks\n",
        "print('For Computer Networks\\n')\n",
        "y_pred0 = classifier0.predict(X_test0)\n",
        "cm0 = confusion_matrix(y_test0, y_pred0)\n",
        "print('Confusion Matrix:')\n",
        "print(cm0)\n",
        "print('----------------------------------------')\n",
        "print('Accuracy Score: %.2f' % accuracy_score(y_test0, y_pred0))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test0, y_pred0, target_names=['Negative', 'Neutral', 'Positive']))\n",
        "print('###############################################################\\n')\n",
        "\n",
        "# For Machine Learning\n",
        "print('For Machine Learning\\n')\n",
        "y_pred1 = classifier1.predict(X_test1)\n",
        "cm1 = confusion_matrix(y_test1, y_pred1)\n",
        "print('Confusion Matrix:')\n",
        "print(cm1)\n",
        "print('----------------------------------------')\n",
        "print('Accuracy Score: %.2f' % accuracy_score(y_test1, y_pred1))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test1, y_pred1, target_names=['Negative', 'Neutral', 'Positive']))\n",
        "print('###############################################################\\n')\n",
        "\n",
        "# For Operating System\n",
        "print('For Operating System\\n')\n",
        "y_pred2 = classifier2.predict(X_test2)\n",
        "cm2 = confusion_matrix(y_test2, y_pred2)\n",
        "print('Confusion Matrix:')\n",
        "print(cm2)\n",
        "print('----------------------------------------')\n",
        "print('Accuracy Score: %.2f' % accuracy_score(y_test2, y_pred2))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test2, y_pred2, target_names=['Negative', 'Neutral', 'Positive']))\n",
        "print('###############################################################\\n')\n",
        "\n",
        "# For Computer Organization & Architecture\n",
        "print('For Computer Organization & Architecture\\n')\n",
        "y_pred3 = classifier3.predict(X_test3)\n",
        "cm3 = confusion_matrix(y_test3, y_pred3)\n",
        "print('Confusion Matrix:')\n",
        "print(cm3)\n",
        "print('----------------------------------------')\n",
        "print('Accuracy Score: %.2f' % accuracy_score(y_test3, y_pred3))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test3, y_pred3, target_names=['Negative', 'Neutral', 'Positive']))"
      ],
      "execution_count": 1011,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For Computer Networks\n",
            "\n",
            "Confusion Matrix:\n",
            "[[166   0  37]\n",
            " [  0 217   0]\n",
            " [  0   0 180]]\n",
            "----------------------------------------\n",
            "Accuracy Score: 0.94\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       1.00      0.82      0.90       203\n",
            "     Neutral       1.00      1.00      1.00       217\n",
            "    Positive       0.83      1.00      0.91       180\n",
            "\n",
            "    accuracy                           0.94       600\n",
            "   macro avg       0.94      0.94      0.94       600\n",
            "weighted avg       0.95      0.94      0.94       600\n",
            "\n",
            "###############################################################\n",
            "\n",
            "For Machine Learning\n",
            "\n",
            "Confusion Matrix:\n",
            "[[203   0   0]\n",
            " [  0 208   0]\n",
            " [ 35   0 154]]\n",
            "----------------------------------------\n",
            "Accuracy Score: 0.94\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.85      1.00      0.92       203\n",
            "     Neutral       1.00      1.00      1.00       208\n",
            "    Positive       1.00      0.81      0.90       189\n",
            "\n",
            "    accuracy                           0.94       600\n",
            "   macro avg       0.95      0.94      0.94       600\n",
            "weighted avg       0.95      0.94      0.94       600\n",
            "\n",
            "###############################################################\n",
            "\n",
            "For Operating System\n",
            "\n",
            "Confusion Matrix:\n",
            "[[164   0  54]\n",
            " [  0 178   0]\n",
            " [  0   0 204]]\n",
            "----------------------------------------\n",
            "Accuracy Score: 0.91\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       1.00      0.75      0.86       218\n",
            "     Neutral       1.00      1.00      1.00       178\n",
            "    Positive       0.79      1.00      0.88       204\n",
            "\n",
            "    accuracy                           0.91       600\n",
            "   macro avg       0.93      0.92      0.91       600\n",
            "weighted avg       0.93      0.91      0.91       600\n",
            "\n",
            "###############################################################\n",
            "\n",
            "For Computer Organization & Architecture\n",
            "\n",
            "Confusion Matrix:\n",
            "[[193   0   0]\n",
            " [  0 214   0]\n",
            " [ 32   0 161]]\n",
            "----------------------------------------\n",
            "Accuracy Score: 0.95\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.86      1.00      0.92       193\n",
            "     Neutral       1.00      1.00      1.00       214\n",
            "    Positive       1.00      0.83      0.91       193\n",
            "\n",
            "    accuracy                           0.95       600\n",
            "   macro avg       0.95      0.94      0.94       600\n",
            "weighted avg       0.95      0.95      0.95       600\n",
            "\n"
          ]
        }
      ]
    }
  ]
}