Text_Classification.ipynb

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      "name": "python3",
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    "language_info": {
      "name": "python"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dnNgAwnjE6qJ",
        "outputId": "55ac8f9e-3434-492f-bd01-0ad07a0fc300"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Import necessary libraries\n",
        "from sklearn.datasets import fetch_20newsgroups\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.neighbors import NearestCentroid, KNeighborsClassifier\n",
        "from sklearn import metrics\n",
        "import nltk\n",
        "from nltk.stem import PorterStemmer\n",
        "nltk.download('punkt')  # Download the required dataset for tokenization\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the 20 Newsgroups dataset and remove headers, footers, and quotes\n",
        "newsgroups = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))"
      ],
      "metadata": {
        "id": "ELwXnMeAFY5N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenization, stemming, and normalization\n",
        "stemmer = PorterStemmer()\n",
        "normalized_text = [' '.join([stemmer.stem(word.lower()) for word in nltk.word_tokenize(text)]) for text in newsgroups.data]\n"
      ],
      "metadata": {
        "id": "iYYllpIVFgyq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(normalized_text)"
      ],
      "metadata": {
        "id": "qxhnbNmfF17m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2418f5fd-36e1-4f54-c569-93a5c5d95c40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "IOPub data rate exceeded.\n",
            "The notebook server will temporarily stop sending output\n",
            "to the client in order to avoid crashing it.\n",
            "To change this limit, set the config variable\n",
            "`--NotebookApp.iopub_data_rate_limit`.\n",
            "\n",
            "Current values:\n",
            "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
            "NotebookApp.rate_limit_window=3.0 (secs)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# TF-IDF Vectorization\n",
        "vectorizer = TfidfVectorizer(stop_words='english', max_features=1000)\n",
        "X = vectorizer.fit_transform(normalized_text)\n",
        "y = newsgroups.target"
      ],
      "metadata": {
        "id": "WXyELU77GIJa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "NqlkVhIsGPCd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "id": "bUWidfD7GTyo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a3263f9-0de5-4936-b7ff-d3c8f63ced6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 612)\t0.20631160511800054\n",
            "  (0, 260)\t0.21685054201354217\n",
            "  (0, 535)\t0.339695468323103\n",
            "  (0, 553)\t0.1561504720747878\n",
            "  (0, 543)\t0.3123662102528663\n",
            "  (0, 981)\t0.3158989866487395\n",
            "  (0, 239)\t0.16556903668583486\n",
            "  (0, 216)\t0.3333650621488756\n",
            "  (0, 422)\t0.3289678964071237\n",
            "  (0, 72)\t0.19357351236498738\n",
            "  (0, 274)\t0.19804651623077338\n",
            "  (0, 270)\t0.1514312669226887\n",
            "  (0, 954)\t0.1376601410595186\n",
            "  (0, 805)\t0.16882483577974217\n",
            "  (0, 776)\t0.13513852924214223\n",
            "  (0, 671)\t0.1511693197202127\n",
            "  (0, 908)\t0.1511038911596793\n",
            "  (0, 955)\t0.12956809938828037\n",
            "  (0, 254)\t0.18909963649019362\n",
            "  (0, 498)\t0.10760914272089893\n",
            "  (0, 744)\t0.17883622726950155\n",
            "  (0, 694)\t0.13918678447785499\n",
            "  (1, 192)\t0.2489091152938963\n",
            "  (1, 189)\t0.2422164047238867\n",
            "  (1, 51)\t0.5344064245347159\n",
            "  :\t:\n",
            "  (15075, 739)\t0.06293092283983617\n",
            "  (15075, 927)\t0.08605229912062949\n",
            "  (15075, 162)\t0.06474206287404076\n",
            "  (15075, 567)\t0.25019989168077356\n",
            "  (15075, 990)\t0.06188734561433976\n",
            "  (15075, 953)\t0.2301338830877742\n",
            "  (15075, 640)\t0.04309128569442434\n",
            "  (15075, 505)\t0.07926394986898654\n",
            "  (15075, 536)\t0.05983974675379932\n",
            "  (15075, 465)\t0.057028364956185296\n",
            "  (15075, 291)\t0.10957835904222035\n",
            "  (15075, 484)\t0.08278650357046825\n",
            "  (15075, 144)\t0.04943591838932358\n",
            "  (15075, 419)\t0.06261687428759338\n",
            "  (15075, 878)\t0.06866488147060958\n",
            "  (15075, 801)\t0.06235468631708528\n",
            "  (15075, 463)\t0.1356511336138814\n",
            "  (15075, 504)\t0.04050148479361388\n",
            "  (15075, 426)\t0.11447117675350699\n",
            "  (15075, 908)\t0.14237233533472396\n",
            "  (15075, 457)\t0.06143324340167398\n",
            "  (15075, 305)\t0.04139896129475628\n",
            "  (15075, 524)\t0.05688634586246672\n",
            "  (15075, 502)\t0.06428698503272437\n",
            "  (15075, 97)\t0.03807947258725731\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train and Test the Naive Bayes Classifier\n",
        "nb_classifier = MultinomialNB()\n",
        "nb_classifier.fit(X_train, y_train)\n",
        "nb_predictions = nb_classifier.predict(X_test)\n",
        "nb_accuracy = metrics.accuracy_score(y_test, nb_predictions)\n",
        "print(\"Naive Bayes Accuracy:\", nb_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MGhGCECKK6DG",
        "outputId": "537ddeb9-cf57-448c-d003-81a3a1b60e0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Naive Bayes Accuracy: 0.5809018567639257\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train and Test the Rocchio Classifier\n",
        "rocchio_classifier = NearestCentroid()\n",
        "rocchio_classifier.fit(X_train, y_train)\n",
        "rocchio_predictions = rocchio_classifier.predict(X_test)\n",
        "rocchio_accuracy = metrics.accuracy_score(y_test, rocchio_predictions)\n",
        "print(\"Rocchio Accuracy:\", rocchio_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zBkTvfFOLoI5",
        "outputId": "1b0de942-3816-44eb-9495-f8ea114e4f8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rocchio Accuracy: 0.5376657824933687\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train and Test the k-Nearest Neighbor (k-NN) Classifier\n",
        "knn_classifier = KNeighborsClassifier(n_neighbors=5)  # You can adjust k as needed\n",
        "knn_classifier.fit(X_train, y_train)\n",
        "knn_predictions = knn_classifier.predict(X_test)\n",
        "knn_accuracy = metrics.accuracy_score(y_test, knn_predictions)\n",
        "print(\"k-NN Accuracy:\", knn_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sOw3I2GzL1cz",
        "outputId": "4a16b103-9082-46b0-ea12-81e75d2ef6a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "k-NN Accuracy: 0.19283819628647214\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "# Calculate F1-scores for each classifier\n",
        "nb_f1 = f1_score(y_test, nb_predictions, average='weighted')\n",
        "rocchio_f1 = f1_score(y_test, rocchio_predictions, average='weighted')\n",
        "knn_f1 = f1_score(y_test, knn_predictions, average='weighted')\n",
        "\n",
        "# Print the F1-scores\n",
        "print(\"Naive Bayes F1-score:\", nb_f1)\n",
        "print(\"Rocchio F1-score:\", rocchio_f1)\n",
        "print(\"k-NN F1-score:\", knn_f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BEPMWeCdMMTi",
        "outputId": "3a30520a-1c7f-4e90-e678-886c2bc67ab2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Naive Bayes F1-score: 0.5734506390013268\n",
            "Rocchio F1-score: 0.5528499670329711\n",
            "k-NN F1-score: 0.2448735718815814\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "3whew67cipjm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifiers = ['Naive Bayes', 'Rocchio', 'K-Nearest Neighbor']\n",
        "f_scores = [nb_f1, rocchio_f1, knn_f1]\n"
      ],
      "metadata": {
        "id": "VeZWKen-iwLh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(classifiers, f_scores, color=['blue', 'green', 'red'])\n",
        "plt.xlabel('Classifiers')\n",
        "plt.ylabel('F-Score')\n",
        "plt.title('Comparison of F-Scores for Different Classifiers')\n",
        "plt.ylim(0, 1)  # Set the y-axis limits as needed\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "dXHW1E6Ii-Vz",
        "outputId": "d5326491-6233-4967-c9b0-01ce98ebe2cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}