flipitnews_Article_classification.ipynb

{
  "cells": [
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      "id": "d60e4d99",
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        "colab": {
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        "id": "d60e4d99",
        "outputId": "f1ef65e9-a855-48a1-bfaa-966a9e6e9ffd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: category_encoders\n",
            "Successfully installed category_encoders-2.6.1\n"
          ]
        }
      ],
      "source": [
        "pip install --upgrade category_encoders"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence_transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mXxZvZnxl_nQ",
        "outputId": "590617bd-0297-479b-da05-5f4a5079bb23"
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      "id": "mXxZvZnxl_nQ",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        }
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    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "c1135262",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1135262",
        "outputId": "0ef2fc7a-a888-4f41-9d1f-9f1b5e2dedf1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "#import spacy\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import nltk\n",
        "import re\n",
        "from nltk.tokenize import word_tokenize\n",
        "nltk.download('punkt')\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "from nltk import WordNetLemmatizer\n",
        "nltk.download('wordnet')\n",
        "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
        "import category_encoders as ce\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score,roc_auc_score\n",
        "from sklearn.metrics import confusion_matrix,classification_report\n",
        "\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.metrics.pairwise import euclidean_distances\n",
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "news_df=pd.read_csv(\"flipitnews-data.csv\")"
      ],
      "metadata": {
        "id": "iMcLPeZdVkQ4"
      },
      "id": "iMcLPeZdVkQ4",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "news_df"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ukimxgR7WF2z",
        "outputId": "2c1337c4-fc99-482d-96bc-2066a22240b6"
      },
      "id": "ukimxgR7WF2z",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Category                                            Article\n",
              "0        Technology  tv future in the hands of viewers with home th...\n",
              "1          Business  worldcom boss  left books alone  former worldc...\n",
              "2            Sports  tigers wary of farrell  gamble  leicester say ...\n",
              "3            Sports  yeading face newcastle in fa cup premiership s...\n",
              "4     Entertainment  ocean s twelve raids box office ocean s twelve...\n",
              "...             ...                                                ...\n",
              "2220       Business  cars pull down us retail figures us retail sal...\n",
              "2221       Politics  kilroy unveils immigration policy ex-chatshow ...\n",
              "2222  Entertainment  rem announce new glasgow concert us band rem h...\n",
              "2223       Politics  how political squabbles snowball it s become c...\n",
              "2224         Sports  souness delight at euro progress boss graeme s...\n",
              "\n",
              "[2225 rows x 2 columns]"
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              "    .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",
              "    [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-9456dd00-fd3f-4652-855d-34cec4e8aa27 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-9456dd00-fd3f-4652-855d-34cec4e8aa27');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Before pre processing, article look like this\")\n",
        "print()\n",
        "news_df['Article'][0]"
      ],
      "metadata": {
        "id": "xECVZMJieJMl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "00f4410d-355b-4ed0-d1f9-a77076f05286"
      },
      "id": "xECVZMJieJMl",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before pre processing, article look like this\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'tv future in the hands of viewers with home theatre systems  plasma high-definition tvs  and digital video recorders moving into the living room  the way people watch tv will be radically different in five years  time.  that is according to an expert panel which gathered at the annual consumer electronics show in las vegas to discuss how these new technologies will impact one of our favourite pastimes. with the us leading the trend  programmes and other content will be delivered to viewers via home networks  through cable  satellite  telecoms companies  and broadband service providers to front rooms and portable devices.  one of the most talked-about technologies of ces has been digital and personal video recorders (dvr and pvr). these set-top boxes  like the us s tivo and the uk s sky+ system  allow people to record  store  play  pause and forward wind tv programmes when they want.  essentially  the technology allows for much more personalised tv. they are also being built-in to high-definition tv sets  which are big business in japan and the us  but slower to take off in europe because of the lack of high-definition programming. not only can people forward wind through adverts  they can also forget about abiding by network and channel schedules  putting together their own a-la-carte entertainment. but some us networks and cable and satellite companies are worried about what it means for them in terms of advertising revenues as well as  brand identity  and viewer loyalty to channels. although the us leads in this technology at the moment  it is also a concern that is being raised in europe  particularly with the growing uptake of services like sky+.  what happens here today  we will see in nine months to a years  time in the uk   adam hume  the bbc broadcast s futurologist told the bbc news website. for the likes of the bbc  there are no issues of lost advertising revenue yet. it is a more pressing issue at the moment for commercial uk broadcasters  but brand loyalty is important for everyone.  we will be talking more about content brands rather than network brands   said tim hanlon  from brand communications firm starcom mediavest.  the reality is that with broadband connections  anybody can be the producer of content.  he added:  the challenge now is that it is hard to promote a programme with so much choice.   what this means  said stacey jolna  senior vice president of tv guide tv group  is that the way people find the content they want to watch has to be simplified for tv viewers. it means that networks  in us terms  or channels could take a leaf out of google s book and be the search engine of the future  instead of the scheduler to help people find what they want to watch. this kind of channel model might work for the younger ipod generation which is used to taking control of their gadgets and what they play on them. but it might not suit everyone  the panel recognised. older generations are more comfortable with familiar schedules and channel brands because they know what they are getting. they perhaps do not want so much of the choice put into their hands  mr hanlon suggested.  on the other end  you have the kids just out of diapers who are pushing buttons already - everything is possible and available to them   said mr hanlon.  ultimately  the consumer will tell the market they want.   of the 50 000 new gadgets and technologies being showcased at ces  many of them are about enhancing the tv-watching experience. high-definition tv sets are everywhere and many new models of lcd (liquid crystal display) tvs have been launched with dvr capability built into them  instead of being external boxes. one such example launched at the show is humax s 26-inch lcd tv with an 80-hour tivo dvr and dvd recorder. one of the us s biggest satellite tv companies  directtv  has even launched its own branded dvr at the show with 100-hours of recording capability  instant replay  and a search function. the set can pause and rewind tv for up to 90 hours. and microsoft chief bill gates announced in his pre-show keynote speech a partnership with tivo  called tivotogo  which means people can play recorded programmes on windows pcs and mobile devices. all these reflect the increasing trend of freeing up multimedia so that people can watch what they want  when they want.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Exploration"
      ],
      "metadata": {
        "id": "HtfeWitbWc9L"
      },
      "id": "HtfeWitbWc9L"
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Shape of data is: \",news_df.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXXxVuHMWHD3",
        "outputId": "d26e32cd-1f25-437b-fae8-49a6bd0db1fd"
      },
      "id": "lXXxVuHMWHD3",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of data is:  (2225, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "news_df.describe()"
      ],
      "metadata": {
        "id": "sjTmgy0GsvnP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "7f047045-5eca-47ec-c538-ff3631e5768e"
      },
      "id": "sjTmgy0GsvnP",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Category                                            Article\n",
              "count      2225                                               2225\n",
              "unique        5                                               2126\n",
              "top      Sports  kennedy questions trust of blair lib dem leade...\n",
              "freq        511                                                  2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5ee9e8fd-659c-408e-9d83-ec0c2c32fe48\">\n",
              "    <div 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>Category</th>\n",
              "      <th>Article</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2225</td>\n",
              "      <td>2225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>5</td>\n",
              "      <td>2126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Sports</td>\n",
              "      <td>kennedy questions trust of blair lib dem leade...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>511</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ee9e8fd-659c-408e-9d83-ec0c2c32fe48')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-5ee9e8fd-659c-408e-9d83-ec0c2c32fe48 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-5ee9e8fd-659c-408e-9d83-ec0c2c32fe48');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "news_df.isna().sum()"
      ],
      "metadata": {
        "id": "sQd5O5rFsx_L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "692831a0-d54f-4d77-d177-6223dd4ccb6f"
      },
      "id": "sQd5O5rFsx_L",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Category    0\n",
              "Article     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Category-wise count of articles"
      ],
      "metadata": {
        "id": "9PO4iQo1XIhZ"
      },
      "id": "9PO4iQo1XIhZ"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "fig=sns.countplot(x=\"Category\",data=news_df)\n",
        "fig.bar_label(fig.containers[0])\n",
        "fig.set_xlabel(\"Category\")\n",
        "fig.set_ylabel(\"Number of articles\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "nCaXM2QeWNki",
        "outputId": "3d9d0196-1bab-4e13-bd16-91803363ad13"
      },
      "id": "nCaXM2QeWNki",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Text pre-processing"
      ],
      "metadata": {
        "id": "TVpJ-urYXYBP"
      },
      "id": "TVpJ-urYXYBP"
    },
    {
      "cell_type": "code",
      "source": [
        "stop_words=list(stopwords.words('english'))\n",
        "def sentence_preprocess(sentence):\n",
        "  sentence=re.sub('[^a-zA-z]',' ',sentence)   # remove non alphabets\n",
        "  words=nltk.word_tokenize(sentence)          # tokenization\n",
        "  cleaned_words=[word for word in words if not word in stop_words]    #removing stopwords\n",
        "  lemma=WordNetLemmatizer()\n",
        "  new_text=\" \".join([lemma.lemmatize(word) for word in cleaned_words])  #lemmatization\n",
        "  return new_text\n",
        "news_df['Article']=news_df['Article'].apply(sentence_preprocess)\n"
      ],
      "metadata": {
        "id": "9WpRe43dXNPM"
      },
      "id": "9WpRe43dXNPM",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"After pre processing, article look like this\")\n",
        "print()\n",
        "news_df['Article'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "id": "AanU6Zg6eA1E",
        "outputId": "cb012f8c-4c0b-4508-9d04-a17580683cd3"
      },
      "id": "AanU6Zg6eA1E",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After pre processing, article look like this\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'tv future hand viewer home theatre system plasma high definition tv digital video recorder moving living room way people watch tv radically different five year time according expert panel gathered annual consumer electronics show la vega discus new technology impact one favourite pastime u leading trend programme content delivered viewer via home network cable satellite telecom company broadband service provider front room portable device one talked technology ce digital personal video recorder dvr pvr set top box like u tivo uk sky system allow people record store play pause forward wind tv programme want essentially technology allows much personalised tv also built high definition tv set big business japan u slower take europe lack high definition programming people forward wind advert also forget abiding network channel schedule putting together la carte entertainment u network cable satellite company worried mean term advertising revenue well brand identity viewer loyalty channel although u lead technology moment also concern raised europe particularly growing uptake service like sky happens today see nine month year time uk adam hume bbc broadcast futurologist told bbc news website like bbc issue lost advertising revenue yet pressing issue moment commercial uk broadcaster brand loyalty important everyone talking content brand rather network brand said tim hanlon brand communication firm starcom mediavest reality broadband connection anybody producer content added challenge hard promote programme much choice mean said stacey jolna senior vice president tv guide tv group way people find content want watch simplified tv viewer mean network u term channel could take leaf google book search engine future instead scheduler help people find want watch kind channel model might work younger ipod generation used taking control gadget play might suit everyone panel recognised older generation comfortable familiar schedule channel brand know getting perhaps want much choice put hand mr hanlon suggested end kid diaper pushing button already everything possible available said mr hanlon ultimately consumer tell market want new gadget technology showcased ce many enhancing tv watching experience high definition tv set everywhere many new model lcd liquid crystal display tv launched dvr capability built instead external box one example launched show humax inch lcd tv hour tivo dvr dvd recorder one u biggest satellite tv company directtv even launched branded dvr show hour recording capability instant replay search function set pause rewind tv hour microsoft chief bill gate announced pre show keynote speech partnership tivo called tivotogo mean people play recorded programme window pc mobile device reflect increasing trend freeing multimedia people watch want want'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ordinal Encoding"
      ],
      "metadata": {
        "id": "L73iaO0dZBvq"
      },
      "id": "L73iaO0dZBvq"
    },
    {
      "cell_type": "code",
      "source": [
        "encode=ce.OrdinalEncoder(cols='Category')\n",
        "news_df=encode.fit_transform(news_df)"
      ],
      "metadata": {
        "id": "sup3jHqJZrEE"
      },
      "id": "sup3jHqJZrEE",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "news_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "tVRfPmU2bzxt",
        "outputId": "d67b8b9f-27d4-4798-a97b-4553e39dd578"
      },
      "id": "tVRfPmU2bzxt",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Category                                            Article\n",
              "0            1  tv future hand viewer home theatre system plas...\n",
              "1            2  worldcom bos left book alone former worldcom b...\n",
              "2            3  tiger wary farrell gamble leicester say rushed...\n",
              "3            3  yeading face newcastle fa cup premiership side...\n",
              "4            4  ocean twelve raid box office ocean twelve crim...\n",
              "...        ...                                                ...\n",
              "2220         2  car pull u retail figure u retail sale fell ja...\n",
              "2221         5  kilroy unveils immigration policy ex chatshow ...\n",
              "2222         4  rem announce new glasgow concert u band rem an...\n",
              "2223         5  political squabble snowball become commonplace...\n",
              "2224         3  souness delight euro progress bos graeme soune...\n",
              "\n",
              "[2225 rows x 2 columns]"
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              "      <td>souness delight euro progress bos graeme soune...</td>\n",
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              "<p>2225 rows × 2 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-052765ef-e5d0-4a22-ae3f-76eea3e470e9')\"\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\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",
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              "\n",
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              "\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-052765ef-e5d0-4a22-ae3f-76eea3e470e9');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Label encoded values**\n",
        "1. Technology\n",
        "2. Business\n",
        "3. Sports\n",
        "4. Entertainment\n",
        "5. Politics"
      ],
      "metadata": {
        "id": "FVEXoZ0_aHM2"
      },
      "id": "FVEXoZ0_aHM2"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BERT"
      ],
      "metadata": {
        "id": "3w-sEK0F3Svr"
      },
      "id": "3w-sEK0F3Svr"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "sbert_model = SentenceTransformer('bert-base-nli-mean-tokens')\n",
        "\n",
        "document_embeddings = sbert_model.encode(news_df['Article'])\n",
        "\n",
        "pairwise_similarities=cosine_similarity(document_embeddings)\n",
        "pairwise_differences=euclidean_distances(document_embeddings)\n",
        "\n"
      ],
      "metadata": {
        "id": "x3vl7DNyl0Q1"
      },
      "id": "x3vl7DNyl0Q1",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=np.array(news_df['Category'].values)\n",
        "X_train_bert, X_test_bert, y_train, y_test= train_test_split(document_embeddings,y,test_size=0.3,shuffle=True,stratify=y,random_state=42)\n",
        "\n",
        "print(\"Shape of train data is: \",X_train_bert.shape)\n",
        "print(\"Shape of test data is: \",X_test_bert.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cp2hPojM6Uk0",
        "outputId": "6d02ab85-aa25-4a0e-a0c7-569a186f3d42"
      },
      "id": "Cp2hPojM6Uk0",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of train data is:  (1557, 768)\n",
            "Shape of test data is:  (668, 768)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt=DecisionTreeClassifier()\n",
        "\n",
        "#model training\n",
        "y_pred_dt,y_pred_proba_dt=model_training(dt,X_train_bert, X_test_bert, y_train)\n",
        "\n",
        "#model evaluation\n",
        "evaluate_model(dt,y_pred_dt,y_pred_proba_dt,X_train_bert, X_test_bert, y_train,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yip1u9ji6o7T",
        "outputId": "b9f898de-7473-427c-b881-9e1eb12b4fbe"
      },
      "id": "Yip1u9ji6o7T",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy:  1.0\n",
            "Testing accuracy:  0.74\n",
            "ROC AUC score:  0.835\n",
            "Precision score:  0.74\n",
            "Recall score:  0.74\n",
            "f1 score:  0.739\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn=KNeighborsClassifier(n_neighbors=3)\n",
        "\n",
        "#model training\n",
        "y_pred_knn,y_pred_proba_knn=model_training(knn,X_train_bert, X_test_bert, y_train)\n",
        "\n",
        "#model evaluation\n",
        "evaluate_model(knn,y_pred_knn,y_pred_proba_knn,X_train_bert, X_test_bert, y_train,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nRbTQJdA7G_U",
        "outputId": "b6cee8cb-95fa-45fc-aa16-ade4218fdc85"
      },
      "id": "nRbTQJdA7G_U",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy:  0.96\n",
            "Testing accuracy:  0.934\n",
            "ROC AUC score:  0.985\n",
            "Precision score:  0.934\n",
            "Recall score:  0.934\n",
            "f1 score:  0.934\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf=RandomForestClassifier()\n",
        "\n",
        "#model training\n",
        "y_pred_rf,y_pred_proba_rf=model_training(rf,X_train_bert, X_test_bert, y_train)\n",
        "\n",
        "#model evaluation\n",
        "evaluate_model(rf,y_pred_rf,y_pred_proba_rf,X_train_bert, X_test_bert, y_train,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "weFtQ-TZ7G3o",
        "outputId": "199e126b-46e8-42a3-af3e-3a5b145f81b4"
      },
      "id": "weFtQ-TZ7G3o",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy:  1.0\n",
            "Testing accuracy:  0.939\n",
            "ROC AUC score:  0.992\n",
            "Precision score:  0.941\n",
            "Recall score:  0.939\n",
            "f1 score:  0.939\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion=confusion_matrix(y_test,y_pred_knn)\n",
        "plt.figure(figsize=(10,5))\n",
        "sns.heatmap(confusion,annot=True,fmt='d',cbar=False,cmap='Blues')\n",
        "plt.xlabel(\"Predicted label\")\n",
        "plt.ylabel(\"Actual label\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "NlkN7yo96orm",
        "outputId": "b7d8f51c-0c0d-4a96-8ae5-c11a9eaa46af"
      },
      "id": "NlkN7yo96orm",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def most_similar(doc_id,similarity_matrix,matrix):\n",
        "    print (f'Document: {news_df.iloc[doc_id][\"Article\"]}')\n",
        "    print ('\\n')\n",
        "    print (f'Similar Documents using {matrix}:')\n",
        "    if matrix=='Cosine Similarity':\n",
        "        similar_ix=np.argsort(similarity_matrix[doc_id])[::-1]\n",
        "    elif matrix=='Euclidean Distance':\n",
        "        similar_ix=np.argsort(similarity_matrix[doc_id])\n",
        "    for ix in similar_ix:\n",
        "        if ix==doc_id:\n",
        "            continue\n",
        "        print('\\n')\n",
        "        print (f'Document: {[ix]}')\n",
        "        print (f'{matrix} : {similarity_matrix[doc_id][ix]}')"
      ],
      "metadata": {
        "id": "oPsn5XXis4xh"
      },
      "id": "oPsn5XXis4xh",
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "pairwise_similarities=cosine_similarity(document_embeddings)\n",
        "pairwise_differences=euclidean_distances(document_embeddings)\n",
        "\n",
        "most_similar(0,pairwise_similarities,'Cosine Similarity')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eE81keBcsrYP",
        "outputId": "27218650-0d33-477d-d94e-f5de622e973a"
      },
      "id": "eE81keBcsrYP",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "\n",
            "\n",
            "Document: [1455]\n",
            "Cosine Similarity : 0.6681653261184692\n",
            "\n",
            "\n",
            "Document: [42]\n",
            "Cosine Similarity : 0.668157160282135\n",
            "\n",
            "\n",
            "Document: [1924]\n",
            "Cosine Similarity : 0.6680857539176941\n",
            "\n",
            "\n",
            "Document: [1448]\n",
            "Cosine Similarity : 0.6680619120597839\n",
            "\n",
            "\n",
            "Document: [1485]\n",
            "Cosine Similarity : 0.6680582165718079\n",
            "\n",
            "\n",
            "Document: [1882]\n",
            "Cosine Similarity : 0.6680390238761902\n",
            "\n",
            "\n",
            "Document: [1418]\n",
            "Cosine Similarity : 0.6680386066436768\n",
            "\n",
            "\n",
            "Document: [470]\n",
            "Cosine Similarity : 0.6679995656013489\n",
            "\n",
            "\n",
            "Document: [1984]\n",
            "Cosine Similarity : 0.6679474115371704\n",
            "\n",
            "\n",
            "Document: [1125]\n",
            "Cosine Similarity : 0.6679161787033081\n",
            "\n",
            "\n",
            "Document: [596]\n",
            "Cosine Similarity : 0.6678744554519653\n",
            "\n",
            "\n",
            "Document: [1236]\n",
            "Cosine Similarity : 0.6678570508956909\n",
            "\n",
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            "Document: [591]\n",
            "Cosine Similarity : 0.6675894856452942\n",
            "\n",
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            "Document: [1888]\n",
            "Cosine Similarity : 0.6675307750701904\n",
            "\n",
            "\n",
            "Document: [1115]\n",
            "Cosine Similarity : 0.6674166917800903\n",
            "\n",
            "\n",
            "Document: [621]\n",
            "Cosine Similarity : 0.6673787832260132\n",
            "\n",
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            "Cosine Similarity : 0.6673468351364136\n",
            "\n",
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            "Cosine Similarity : 0.6673324108123779\n",
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            "Cosine Similarity : 0.6672728061676025\n",
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            "Cosine Similarity : 0.6672725677490234\n",
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            "Cosine Similarity : 0.6671274304389954\n",
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            "Cosine Similarity : 0.6668521761894226\n",
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            "Cosine Similarity : 0.666793704032898\n",
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            "Cosine Similarity : 0.6667683124542236\n",
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            "Document: [157]\n",
            "Cosine Similarity : 0.6666088104248047\n",
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            "Document: [156]\n",
            "Cosine Similarity : 0.6665632128715515\n",
            "\n",
            "\n",
            "Document: [869]\n",
            "Cosine Similarity : 0.6665462255477905\n",
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            "\n",
            "Document: [521]\n",
            "Cosine Similarity : 0.6665098071098328\n",
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            "Document: [320]\n",
            "Cosine Similarity : 0.6664178371429443\n",
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            "Document: [1873]\n",
            "Cosine Similarity : 0.6663856506347656\n",
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            "Cosine Similarity : 0.6663734912872314\n",
            "\n",
            "\n",
            "Document: [1588]\n",
            "Cosine Similarity : 0.6662555932998657\n",
            "\n",
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            "Cosine Similarity : 0.6661744713783264\n",
            "\n",
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            "Cosine Similarity : 0.6661697626113892\n",
            "\n",
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            "Cosine Similarity : 0.6661204695701599\n",
            "\n",
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            "Cosine Similarity : 0.6661095023155212\n",
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            "Cosine Similarity : 0.6658320426940918\n",
            "\n",
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            "Document: [12]\n",
            "Cosine Similarity : 0.6656484007835388\n",
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            "Cosine Similarity : 0.6656466722488403\n",
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            "Document: [1067]\n",
            "Cosine Similarity : 0.6655701398849487\n",
            "\n",
            "\n",
            "Document: [817]\n",
            "Cosine Similarity : 0.6655052304267883\n",
            "\n",
            "\n",
            "Document: [677]\n",
            "Cosine Similarity : 0.6654433608055115\n",
            "\n",
            "\n",
            "Document: [1642]\n",
            "Cosine Similarity : 0.6653031706809998\n",
            "\n",
            "\n",
            "Document: [274]\n",
            "Cosine Similarity : 0.6652935743331909\n",
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            "Document: [2004]\n",
            "Cosine Similarity : 0.6651408076286316\n",
            "\n",
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            "Cosine Similarity : 0.6651105880737305\n",
            "\n",
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            "Cosine Similarity : 0.6651068329811096\n",
            "\n",
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            "Cosine Similarity : 0.664967954158783\n",
            "\n",
            "\n",
            "Document: [1383]\n",
            "Cosine Similarity : 0.6649374961853027\n",
            "\n",
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            "Cosine Similarity : 0.6649150848388672\n",
            "\n",
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            "Document: [2157]\n",
            "Cosine Similarity : 0.6649115085601807\n",
            "\n",
            "\n",
            "Document: [1907]\n",
            "Cosine Similarity : 0.6647546291351318\n",
            "\n",
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            "Document: [15]\n",
            "Cosine Similarity : 0.6646121740341187\n",
            "\n",
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            "Cosine Similarity : 0.6646054983139038\n",
            "\n",
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            "Document: [1689]\n",
            "Cosine Similarity : 0.6645685434341431\n",
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            "Cosine Similarity : 0.6645195484161377\n",
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            "\n",
            "Document: [1938]\n",
            "Cosine Similarity : 0.6644822359085083\n",
            "\n",
            "\n",
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            "Cosine Similarity : 0.6644653081893921\n",
            "\n",
            "\n",
            "Document: [1574]\n",
            "Cosine Similarity : 0.6644591689109802\n",
            "\n",
            "\n",
            "Document: [791]\n",
            "Cosine Similarity : 0.6644490361213684\n",
            "\n",
            "\n",
            "Document: [657]\n",
            "Cosine Similarity : 0.6644068360328674\n",
            "\n",
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            "Document: [291]\n",
            "Cosine Similarity : 0.6644068360328674\n",
            "\n",
            "\n",
            "Document: [49]\n",
            "Cosine Similarity : 0.6642324328422546\n",
            "\n",
            "\n",
            "Document: [73]\n",
            "Cosine Similarity : 0.6642084121704102\n",
            "\n",
            "\n",
            "Document: [1712]\n",
            "Cosine Similarity : 0.6641144752502441\n",
            "\n",
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            "Cosine Similarity : 0.6640920639038086\n",
            "\n",
            "\n",
            "Document: [2180]\n",
            "Cosine Similarity : 0.6640917658805847\n",
            "\n",
            "\n",
            "Document: [108]\n",
            "Cosine Similarity : 0.6640681028366089\n",
            "\n",
            "\n",
            "Document: [251]\n",
            "Cosine Similarity : 0.6639928817749023\n",
            "\n",
            "\n",
            "Document: [1242]\n",
            "Cosine Similarity : 0.6639649868011475\n",
            "\n",
            "\n",
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            "Cosine Similarity : 0.6638851165771484\n",
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            "Cosine Similarity : 0.6636835336685181\n",
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            "Cosine Similarity : 0.6630461812019348\n",
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            "Cosine Similarity : 0.6630043983459473\n",
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            "Cosine Similarity : 0.6628583073616028\n",
            "\n",
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            "Cosine Similarity : 0.6627329587936401\n",
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            "Cosine Similarity : 0.6583585739135742\n",
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            "\n",
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            "Cosine Similarity : 0.5877773761749268\n",
            "\n",
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            "Cosine Similarity : 0.5874539613723755\n",
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            "Cosine Similarity : 0.5873273611068726\n",
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            "Cosine Similarity : 0.5866073966026306\n",
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            "Cosine Similarity : 0.5865646600723267\n",
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            "Cosine Similarity : 0.5858719944953918\n",
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            "Cosine Similarity : 0.5858590602874756\n",
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            "Cosine Similarity : 0.5856868028640747\n",
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            "Cosine Similarity : 0.5852406024932861\n",
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            "Cosine Similarity : 0.5851738452911377\n",
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            "Cosine Similarity : 0.5846661329269409\n",
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            "Cosine Similarity : 0.5837254524230957\n",
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            "Cosine Similarity : 0.5807095766067505\n",
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            "Cosine Similarity : 0.5806763172149658\n",
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            "Cosine Similarity : 0.5769136548042297\n",
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            "Cosine Similarity : 0.5763524770736694\n",
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            "Cosine Similarity : 0.5763298869132996\n",
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            "Cosine Similarity : 0.5759323835372925\n",
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            "Cosine Similarity : 0.5753400325775146\n",
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            "Cosine Similarity : 0.5747595429420471\n",
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            "Cosine Similarity : 0.5745515823364258\n",
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            "Cosine Similarity : 0.5743943452835083\n",
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            "Cosine Similarity : 0.5740485191345215\n",
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            "Cosine Similarity : 0.5740454792976379\n",
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            "Cosine Similarity : 0.5737535953521729\n",
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            "Cosine Similarity : 0.5736338496208191\n",
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            "Cosine Similarity : 0.5731609463691711\n",
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            "Cosine Similarity : 0.5726426243782043\n",
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            "Cosine Similarity : 0.5722672939300537\n",
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            "Cosine Similarity : 0.5721433162689209\n",
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            "Cosine Similarity : 0.5721026659011841\n",
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            "Cosine Similarity : 0.5721026659011841\n",
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            "Cosine Similarity : 0.5704512596130371\n",
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            "Cosine Similarity : 0.5702513456344604\n",
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            "Cosine Similarity : 0.5700458288192749\n",
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            "Cosine Similarity : 0.570002555847168\n",
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            "Cosine Similarity : 0.569635272026062\n",
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            "Cosine Similarity : 0.5693615674972534\n",
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            "Cosine Similarity : 0.5693583488464355\n",
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            "Cosine Similarity : 0.568877100944519\n",
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            "Cosine Similarity : 0.5688128471374512\n",
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            "Cosine Similarity : 0.5687273144721985\n",
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            "Cosine Similarity : 0.5685432553291321\n",
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            "Cosine Similarity : 0.5683789253234863\n",
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            "Cosine Similarity : 0.568364143371582\n",
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            "Cosine Similarity : 0.5683580636978149\n",
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            "Cosine Similarity : 0.5671709775924683\n",
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            "Cosine Similarity : 0.5668637156486511\n",
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            "Cosine Similarity : 0.5668637156486511\n",
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            "Cosine Similarity : 0.5668209791183472\n",
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            "Cosine Similarity : 0.5667718052864075\n",
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            "Cosine Similarity : 0.5666506290435791\n",
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            "Cosine Similarity : 0.5664582848548889\n",
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            "Cosine Similarity : 0.5661709308624268\n",
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            "Cosine Similarity : 0.5658338665962219\n",
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            "Cosine Similarity : 0.5657150745391846\n",
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            "Cosine Similarity : 0.5656030178070068\n",
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            "Cosine Similarity : 0.5655159950256348\n",
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            "Cosine Similarity : 0.5648064613342285\n",
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            "Cosine Similarity : 0.564486026763916\n",
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            "Cosine Similarity : 0.5644349455833435\n",
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            "Cosine Similarity : 0.564237654209137\n",
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            "Cosine Similarity : 0.563785970211029\n",
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            "Cosine Similarity : 0.5622446537017822\n",
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            "Cosine Similarity : 0.5622446537017822\n",
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            "Cosine Similarity : 0.5621505975723267\n",
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            "Cosine Similarity : 0.5614710450172424\n",
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            "Cosine Similarity : 0.5614666938781738\n",
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            "Cosine Similarity : 0.5612783432006836\n",
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            "Cosine Similarity : 0.5608371496200562\n",
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            "Cosine Similarity : 0.5608371496200562\n",
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            "Cosine Similarity : 0.5606362223625183\n",
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            "Cosine Similarity : 0.5605952143669128\n",
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            "Cosine Similarity : 0.5604194402694702\n",
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            "Cosine Similarity : 0.5602291226387024\n",
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            "Cosine Similarity : 0.5598341226577759\n",
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            "Cosine Similarity : 0.5597468614578247\n",
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            "Cosine Similarity : 0.5596766471862793\n",
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            "Cosine Similarity : 0.5592859983444214\n",
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            "Cosine Similarity : 0.5590987205505371\n",
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            "Cosine Similarity : 0.5582988262176514\n",
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            "Cosine Similarity : 0.55762779712677\n",
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            "Cosine Similarity : 0.5565497279167175\n",
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            "Cosine Similarity : 0.5551643967628479\n",
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            "Cosine Similarity : 0.5543813705444336\n",
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            "Cosine Similarity : 0.5543133020401001\n",
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            "Cosine Similarity : 0.5534164905548096\n",
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            "Cosine Similarity : 0.5533177852630615\n",
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            "Cosine Similarity : 0.550936222076416\n",
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            "Cosine Similarity : 0.5499266386032104\n",
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            "Cosine Similarity : 0.5482711791992188\n",
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            "Cosine Similarity : 0.5478459596633911\n",
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            "Cosine Similarity : 0.5470983386039734\n",
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            "Cosine Similarity : 0.543468177318573\n",
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            "Cosine Similarity : 0.5430914759635925\n",
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            "Cosine Similarity : 0.5429219603538513\n",
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            "Cosine Similarity : 0.5428482294082642\n",
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            "Cosine Similarity : 0.5418066382408142\n",
            "\n",
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            "Document: [421]\n",
            "Cosine Similarity : 0.5408646464347839\n",
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            "Cosine Similarity : 0.5399053692817688\n",
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            "Cosine Similarity : 0.5365152359008789\n",
            "\n",
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            "Document: [1523]\n",
            "Cosine Similarity : 0.5360337495803833\n",
            "\n",
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            "Document: [1730]\n",
            "Cosine Similarity : 0.5331832766532898\n",
            "\n",
            "\n",
            "Document: [2162]\n",
            "Cosine Similarity : 0.5328482389450073\n",
            "\n",
            "\n",
            "Document: [830]\n",
            "Cosine Similarity : 0.5324665307998657\n",
            "\n",
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            "Document: [223]\n",
            "Cosine Similarity : 0.5315459966659546\n",
            "\n",
            "\n",
            "Document: [1248]\n",
            "Cosine Similarity : 0.5307527780532837\n",
            "\n",
            "\n",
            "Document: [1584]\n",
            "Cosine Similarity : 0.5289299488067627\n",
            "\n",
            "\n",
            "Document: [1319]\n",
            "Cosine Similarity : 0.5198042392730713\n",
            "\n",
            "\n",
            "Document: [1428]\n",
            "Cosine Similarity : 0.5194414258003235\n",
            "\n",
            "\n",
            "Document: [996]\n",
            "Cosine Similarity : 0.5129544734954834\n",
            "\n",
            "\n",
            "Document: [876]\n",
            "Cosine Similarity : 0.5007770657539368\n",
            "\n",
            "\n",
            "Document: [2045]\n",
            "Cosine Similarity : 0.49168604612350464\n",
            "\n",
            "\n",
            "Document: [1711]\n",
            "Cosine Similarity : 0.49127197265625\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bag of Words TF-IDF"
      ],
      "metadata": {
        "id": "9uk-FrW-bIJM"
      },
      "id": "9uk-FrW-bIJM"
    },
    {
      "cell_type": "code",
      "source": [
        "choice = int(input(\"Choose: \\n 1. Bag of Words \\n 2. TF-IDF \\n\"))\n",
        "if choice == 1:\n",
        "  cv=CountVectorizer(max_features=5000)\n",
        "  X=cv.fit_transform(news_df.Article).toarray()\n",
        "  y=np.array(news_df['Category'].values)\n",
        "elif choice == 2:\n",
        "  tf_idf=TfidfVectorizer()\n",
        "  X=tf_idf.fit_transform(news_df.Article).toarray()\n",
        "  y=np.array(news_df['Category'].values)\n",
        "else:\n",
        "  print('wrong input')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lNJfTWY2dsWk",
        "outputId": "42cf8f74-efa2-4089-cea6-a87fbcd42b75"
      },
      "id": "lNJfTWY2dsWk",
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Choose: \n",
            " 1. Bag of Words \n",
            " 2. TF-IDF \n",
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train Test Split"
      ],
      "metadata": {
        "id": "v5x3MFy5fvBb"
      },
      "id": "v5x3MFy5fvBb"
    },
    {
      "cell_type": "code",
      "source": [
        "len(X[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RvaO-6GOYqU9",
        "outputId": "fe08bb9b-a9bd-4b82-f8c6-886533e4ad39"
      },
      "id": "RvaO-6GOYqU9",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17096"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test= train_test_split(X,y,test_size=0.3,shuffle=True,stratify=y,random_state=42)\n",
        "\n",
        "print(\"Shape of train data is: \",X_train.shape)\n",
        "print(\"Shape of test data is: \",X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PsBdT8fgBF6",
        "outputId": "9524fb69-8c9b-405a-9471-83c41b9c44b7"
      },
      "id": "6PsBdT8fgBF6",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of train data is:  (636, 5000)\n",
            "Shape of test data is:  (273, 5000)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Naive Bayes Classifier"
      ],
      "metadata": {
        "id": "NeCkBQRmhI7m"
      },
      "id": "NeCkBQRmhI7m"
    },
    {
      "cell_type": "code",
      "source": [
        "nb=MultinomialNB()\n",
        "nb.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "flM6iRl2ibPw",
        "outputId": "63f6b197-fca8-4a82-dd53-d4dbbe21a966"
      },
      "id": "flM6iRl2ibPw",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nb_train=accuracy_score(y_train,nb.predict(X_train))\n",
        "nb_test=accuracy_score(y_test,nb.predict(X_test))\n",
        "print(\"Training accuracy: \",nb_train.round(3))\n",
        "print(\"Testing accuracy: \",nb_test.round(3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H8IQqw2fifzg",
        "outputId": "541cf3bd-e71d-449a-8edf-25bf440d5aa6"
      },
      "id": "H8IQqw2fifzg",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy:  0.988\n",
            "Testing accuracy:  0.97\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Making Prediction and computing ROC-AUC score\n",
        "y_pred_NB=nb.predict(X_test)\n",
        "y_pred_proba=nb.predict_proba(X_test)\n",
        "rocauc_score= roc_auc_score(y_test,y_pred_proba,multi_class=\"ovr\")\n",
        "print(\"ROC AUC score: \",rocauc_score.round(3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QyJEzxBvi6t3",
        "outputId": "c0e60d89-0ace-4b34-cbe0-0a9135ee4726"
      },
      "id": "QyJEzxBvi6t3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC AUC score:  0.999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision=precision_score(y_test,y_pred_NB,average='weighted')\n",
        "recall=recall_score(y_test,y_pred_NB,average='weighted')\n",
        "f1=f1_score(y_test,y_pred_NB,average='weighted')\n",
        "print(\"Precision score: \",precision.round(3))\n",
        "print(\"Recall score: \",recall.round(3))\n",
        "print(\"f1 score: \",f1.round(3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2QLvxTHjswe",
        "outputId": "dcf822fb-afd4-4bfc-fb5e-2bd73e14ce3b"
      },
      "id": "H2QLvxTHjswe",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision score:  0.971\n",
            "Recall score:  0.97\n",
            "f1 score:  0.97\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Confusion Matrix"
      ],
      "metadata": {
        "id": "i93nTK9hlMl9"
      },
      "id": "i93nTK9hlMl9"
    },
    {
      "cell_type": "code",
      "source": [
        "confusion=confusion_matrix(y_test,y_pred_NB)\n",
        "plt.figure(figsize=(10,5))\n",
        "sns.heatmap(confusion,annot=True,fmt='d',cbar=False,cmap='Blues')\n",
        "plt.xlabel(\"Predicted label\")\n",
        "plt.ylabel(\"Actual label\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "W3_OYcg1lrEn",
        "outputId": "1c57b597-ca78-4b45-c920-986ba00ec53b"
      },
      "id": "W3_OYcg1lrEn",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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Skpb9sERbN2/SpKnT7Y7mdmwtRg0aNNDWrVuvWYz+bjUJN+erqHmSpN7PdM82PuKdUXq4Ix/qyL361Urrp0m9s56PefkhSdLs77fquZF/f9vaMwnJ6jBopt56/j4t/ehZFSzgoZgjp/XY4C+06+DJfMsN99S23YM6d/asJk+coPj4OIVVq67J06YrmNOb4GTMNbjC2bNnNeyNwYqPi1OhgABVqRKmSVOnq3HTZnZHczsOy8bm8csvvygpKUlt27a96vakpCRt2bJFLVu2zNNxOZUOrhLc6g27I8AQ59aOsjsCADhVRiZ/r8E1/L1yd1q+rStGd99993W3+/v757kUAQAAAEBe3dK36wYAAAAAV6AYAQAAADAexQgAAACA8ShGAAAAAIxHMQIAAABgPIoRAAAAAONRjAAAAAAYj2IEAAAAwHgUIwAAAADGoxgBAAAAMB7FCAAAAIDxKEYAAAAAjEcxAgAAAGA8ihEAAAAA41GMAAAAABiPYgQAAADAeBQjAAAAAMajGAEAAAAwHsUIAAAAgPEoRgAAAACMRzECAAAAYDyKEQAAAADjUYwAAAAAGI9iBAAAAMB4FCMAAAAAxqMYAQAAADAexQgAAACA8ShGAAAAAIxHMQIAAABgPIoRAAAAAONRjAAAAAAYr0BudpowYUKuD9i/f/8bDgMAAAAAdnBYlmX93U4VK1bM3cEcDh0+fPimQ92sS+l2JwAA5wpq2M/uCDDEuc0T7Y4AAE7lk6uloFyuGB05cuRmsgAAAADALe2GrzFKS0vT/v37lZ7O8gwAAACA21uei1FycrJ69eolPz8/1axZU7GxsZKkl156SaNHj3Z6QAAAAADIb3kuRhEREdqxY4dWr14tHx+frPE2bdooKirKqeEAAAAAwBVyeSnSfyxatEhRUVFq3LixHA5H1njNmjV16NAhp4YDAAAAAFfI84pRXFycihcvnmM8KSkpW1ECAAAAgNtFnovRnXfeqe+//z7r+V9laPr06WrSpInzkgEAAACAi+T5VLpRo0apXbt22rt3r9LT0zV+/Hjt3btX69at05o1a/IjIwAAAADkqzyvGDVv3lzR0dFKT09X7dq19dNPP6l48eJav369GjRokB8ZAQAAACBfOSzLsuwO4WyX+GolAG4mqGE/uyPAEOc2T7Q7AgA4lU8uz5HL86l0kpSRkaGFCxcqJiZGklSjRg116NBBBQrc0OEAAAAAwFZ5bjJ79uzRww8/rJMnTyosLEyS9O6776pYsWL67rvvVKtWLaeHBAAAAID8lOdrjJ599lnVrFlTx44d07Zt27Rt2zYdPXpU4eHheu655/IjIwAAAADkqzyvGEVHR2vLli0KCgrKGgsKCtLIkSPVsGFDp4YDAAAAAFfI84pR1apVderUqRzjp0+fVuXKlZ0SCgAAAABcKVfF6MKFC1mPyMhI9e/fXwsWLNCxY8d07NgxLViwQAMGDNC7776b33kBAAAAwOlydbtuDw8PORyOrOd//chfY//9PCMjIz9y5gm36wbgbrhdN1yF23UDcDdOvV33qlWrbiYLAAAAANzSclWMWrZsmd85AAAAAMA2N/yNrMnJyYqNjVVaWlq28fDw8JsOBQAAAACulOdiFBcXp6efflpLly696vZb4RojAAAAAMiLPN+ue8CAATp//rw2btwoX19fLVu2TLNmzVKVKlW0ePHi/MgIAAAAAPkqzytGK1eu1Lfffqs777xTHh4eKl++vO677z4VLlxYkZGReuihh/IjJwAAAADkmzyvGCUlJal48eKSpKCgIMXFxUmSateurW3btjk3HQAAAAC4QJ6LUVhYmPbv3y9JqlOnjqZNm6bjx49r6tSpCg0NdXpAAAAAAMhveT6V7uWXX9aJEyckScOHD1fbtm01Z84ceXl5aebMmc7OBwAAAAD5zmFZlnUzB0hOTta+fftUrlw5hYSEOCvXTbmUbncCAHCuoIb97I4AQ5zbPNHuCADgVD65XAq64e8x+oufn5/q169/s4cBAAAAANvkqhgNGjQo1wf84IMPbjgMAAAAANghV8Vo+/btuTqYw+G4qTAAAAAAYIdcFaNVq1bldw4AAAAAsE2eb9cN9zN/7hy1u6+1Gtarra5PPKZdO3faHQluirmGm9WsfiUtGPe8Dv80UinbJ6p9q/Bs2z8e0U0p2ydme3w7sc9Vj+VVsIA2zB+ilO0TFV61tCviww3xuQZXYa7lP4qR4ZYt/UHvj4nU8336av5XCxUWVk0vPt9LZ86csTsa3AxzDc7g7+utXQeOa0Bk1DX3+fHfe1ShTUTWo0fEjKvuN2pAB52IS8ivqDAAn2twFeaaa1CMDDd71gx1frSLOnZ6RJUqV9abw0fIx8dHi7752u5ocDPMNTjDT//eqxGTl2jxqmv/S2laWrpOnbmY9Th/MSXHPvc3q6F7G1dXxIcL8zMu3Byfa3AV5pprUIwMdjktTTF796hxk6ZZYx4eHmrcuKl27sjdDTeA3GCuwZXuvrOK/lgRqR0Lh2r864+raKB/tu3FiwZo8tAn1Wvo50pOSbMpJW53fK7BVZhrrkMxMti58+eUkZGh4ODgbOPBwcGKj4+3KRXcEXMNrrJ8XYyeHTpbDz7/kd4c/63ublBZ3058UR4e/7lr6sdvd9MnC37Vtr2xNibF7Y7PNbgKc811cnVXusWLF+f6gA8//HCeAqSkpGjr1q0qWrSoatSokW3bpUuX9OWXX6p79+7X/PnU1FSlpqZmG7M8veXt7Z2nHACA299XP27N+t97Dv6pXb8dV8ySEWpxZxWt3nRAfZ5sqQA/H7332U82pgQA3IpyVYw6duyYq4M5HA5lZGTk+sUPHDig+++/X7GxsXI4HGrevLnmz5+v0NBQSVJCQoKefvrp6xajyMhIjRgxItvYG0OH681hb+U6h6mCigTJ09Mzx4V7Z86cUUhIiE2p4I6Ya7DL78fPKO7cRVUqW0yrNx1Qq4ZV1Si8ohI2jsu237/n/FPzl25R72Gz7QmK2w6fa3AV5prr5OpUuszMzFw98lKKJGnw4MGqVauWTp8+rf379ysgIEDNmjVTbGzuT2+IiIhQQkJCtsdrgyPylMNUBb28VL1GTW3csD5rLDMzUxs3rld4nXo2JoO7Ya7BLqWLF1FwoL9Oxl+QJL0yZoHuejxSjZ4YrUZPjFbHl6ZIkp4aMkNvTfzOzqi4zfC5BldhrrlOrlaM8su6dev0888/KyQkRCEhIfruu+/Up08f3X333Vq1apX8/f3/9hje3jlPm7uUnl+J3c9TPZ7W0NcHq2bNWqpVO1xfzJ6llJQUdezU2e5ocDPMNTiDv6+XKpUtlvW8QulghVctrXMXknU2IUlvPP+gFq2I1sn4C7qjbIhGvtxRh47Ga/m6GEnS0ZPnsh0vMfnKqdiHj8bp+OnzLnsfcA98rsFVmGuucUPFKCkpSWvWrFFsbKzS0rLf0ad///65Pk5KSooKFPhPBIfDoSlTpqhfv35q2bKl5s6deyPxkAdt2z2oc2fPavLECYqPj1NYteqaPG26glmahZMx1+AM9WuU10/TX856PubVRyRJsxdvUP9RUapVpbS6tm+kIgG+OhGXoJ/X79Pbk5co7TL/Ygbn43MNrsJccw2HZVlWXn5g+/btevDBB5WcnKykpCQVLVpU8fHx8vPzU/HixXX48OFcH+uuu+7SSy+9pKeeeirHtn79+mnOnDm6cOFCnk/RY8UIgLsJatjP7ggwxLnNE+2OAABO5ZPLpaA836574MCBat++vc6dOydfX19t2LBBf/zxhxo0aKD3338/T8fq1KmT5s2bd9VtEydO1JNPPqk89jYAAAAAyLM8rxgVKVJEGzduVFhYmIoUKaL169erevXq2rhxo3r06KF9+/blV9ZcY8UIgLthxQiuwooRAHeTbytGBQsWlIfHlR8rXrx41h3kAgMDdfTo0bweDgAAAABsl+ebL9SrV0+bN29WlSpV1LJlSw0bNkzx8fGaPXu2atWqlR8ZAQAAACBf5XnFaNSoUVlfwDpy5EgFBQXpxRdfVFxcnD7++GOnBwQAAACA/Jbna4xuB1xjBMDdcI0RXIVrjAC4m3y7xggAAAAA3E2erzGqWLGiHA7HNbfn5XuMAAAAAOBWkOdiNGDAgGzPL1++rO3bt2vZsmV67bXXnJULAAAAAFwmz8Xo5Zdfvur4pEmTtGXLlpsOBAAAAACu5rRrjNq1a6evv/7aWYcDAAAAAJdxWjFasGCBihYt6qzDAQAAAIDL3NAXvP73zRcsy9LJkycVFxenyZMnOzUcAAAAALhCnotRhw4dshUjDw8PFStWTK1atVK1atWcGg4AAAAAXIEveAWA2wBf8ApX4QteAbibfPuCV09PT50+fTrH+JkzZ+Tp6ZnXwwEAAACA7fJcjK61wJSamiovL6+bDgQAAAAArpbra4wmTJggSXI4HJo+fboKFSqUtS0jI0Nr167lGiMAAAAAt6VcF6MPP/xQ0pUVo6lTp2Y7bc7Ly0sVKlTQ1KlTnZ8QAAAAAPJZrovRkSNHJEn33HOPvvnmGwUFBeVbKAAAAABwpTzfrnvVqlX5kQMAAAAAbJPnmy888sgjevfdd3OMjxkzRo899phTQgEAAACAK+W5GK1du1YPPvhgjvF27dpp7dq1TgkFAAAAAK6U52KUmJh41dtyFyxYUBcuXHBKKAAAAABwpTwXo9q1aysqKirH+Pz581WjRg2nhAIAAAAAV8rzzReGDh2qzp0769ChQ2rdurUkacWKFZo3b56++uorpwcEAAAAgPyW52LUvn17LVq0SKNGjdKCBQvk6+ur8PBw/fzzz2rZsmV+ZAQAAACAfOWwLMty1sF2796tWrVqOetwN+xSut0JAMC5ghr2szsCDHFu80S7IwCAU/nkcikoz9cY/a+LFy/q448/1l133aU6derc7OEAAAAAwOVuuBitXbtW3bt3V2hoqN5//321bt1aGzZscGY2AAAAAHCJPF1jdPLkSc2cOVOffvqpLly4oC5duig1NVWLFi3ijnQAAAAAblu5XjFq3769wsLCtHPnTo0bN05//vmnPvroo/zMBgAAAAAukesVo6VLl6p///568cUXVaVKlfzMBAAAAAAulesVo19//VUXL15UgwYN1KhRI02cOFHx8fH5mQ0AAAAAXCLPt+tOSkpSVFSUPvvsM23atEkZGRn64IMP9MwzzyggICC/cuZJUprT7kAOXJeHw2F3BABwqmaRK+2OAEOs/mcruyPAEIV9crcWlOe70vn7++uZZ57Rr7/+ql27dumVV17R6NGjVbx4cT388MN5DgoAAAAAdrup7zEKCwvTmDFjdOzYMc2bN89ZmQAAAADApW76C14lydPTUx07dtTixYudcTgAAAAAcCmnFCMAAAAAuJ1RjAAAAAAYj2IEAAAAwHgUIwAAAADGoxgBAAAAMB7FCAAAAIDxKEYAAAAAjEcxAgAAAGA8ihEAAAAA41GMAAAAABiPYgQAAADAeBQjAAAAAMajGAEAAAAwHsUIAAAAgPEoRgAAAACMRzECAAAAYDyKEQAAAADjUYwAAAAAGI9iBAAAAMB4FCMAAAAAxqMYAQAAADAexQgAAACA8ShGAAAAAIxHMQIAAABgPIoRAAAAAONRjAAAAAAYj2IEAAAAwHgUIwAAAADGoxgBAAAAMF4BuwPAPl9FzdNXUfN04s/jkqQ7KlXWcy/0VbO7W9icDO5o65bNmjXjU8Xs3a24uDh9MH6SWt/bxu5YcEPMNThD/XJF1L1pOVUPDVCxAG8Nitqp1fvjs+3zQquK6lSvlAJ8CmjH0QSN+mG/jp5NybZP8yrB6t2igqoUL6S09Ext/eO8XvlylyvfCm5zH0+ZqE+mTso2Vr5CRS349gebErkvipHBipcoof4DXlG58uVlWZa+W7xIA/v31byvvlGlylXsjgc3k5KSrKphYerY6RENGtDP7jhwY8w1OIOPl4cOnErUt9v/1NjHw3Ns79G0nJ68q4yGLYrRn+dT9OI9d2hS17p6dPJGpWVkSpJaVyumoe2raeLKQ9p85Jw8PRyqXLyQq98K3MAdlSpr0sefZT0v4Mmf8PmB/6oGa9mqdbbn/foP1IKo+dq1cwfFCE7X/O6Wan53S7tjwADMNTjDuoNnte7g2Wtu/0ejspr+y+9ac+DKKtKwRXu1/JXmalUtRD/tOS1Ph0Ovta2iccsP6tvoE1k/dyQ+Od+zw/14FiigkJBidsdwexQjSJIyMjL080/LlJKSrPA6de2OAwDALat0ER8VC/DWxsPnssYSUzO0+/gFhZcJ1E97TqtaaCGVKOwjy5Lm9m6o4EJeOnAqUeOWH9ShuCQb0+N2dPSPP9SuTQt5eXmrdp266td/oEqGlrI7ltuxvRjFxMRow4YNatKkiapVq6Z9+/Zp/PjxSk1NVbdu3dS6devr/nxqaqpSU1OzjaU7vOTt7Z2fsd3Gbwf2q2e3J5WWlipfPz+NHTdRd1SqbHcsAABuWcGFvCRJZ5PSso2fSUxTyP9tKx3kK0l6vmVFjf3pN51IuKRujcvq4x711GniBl24lO7a0Lht1awdruHvjFL5ChUVHxenT6ZNUu+nu2n+19/J39/f7nhuxda70i1btkx169bVq6++qnr16mnZsmVq0aKFDh48qD/++EP333+/Vq5ced1jREZGKjAwMNvj/TGRLnoHt78KFStq3oKFmjUnSo91eULD3hyiw4cO2h0LAIDbmofDIUn69NfftXJfnGJOXNRbi2MkS7qvRnGb0+F20qx5C7W5v62qVA1Tk2bNNX7iNF28eFE//7jU7mhux9Zi9Pbbb+u1117TmTNnNGPGDP3jH/9Q7969tXz5cq1YsUKvvfaaRo8efd1jREREKCEhIdvj1X9GuOgd3P4KFvRSuXLlVaNmLb004BVVrVpNc7/43O5YAADcss4kXlkpKurvlW08uJCX4v9vW3zilbNZDv/XaXOXMywdO5+ikoE+LkoKdxRQuLDKla+go0dj7Y7idmwtRnv27FHPnj0lSV26dNHFixf16KOPZm3v2rWrdu7ced1jeHt7q3DhwtkenEZ34zKtTF1OS/v7HQEAMNTx85cUdzFVd1UMyhrz9/JUrdKFtfNYgiQp5s+LSk3PUPlgv6x9Cng4VCrQVycSLrk8M9xHcnKSjh89ys0Y8oHt1xg5/m+p2cPDQz4+PgoMDMzaFhAQoISEBLuiub2Pxo1V0+YtFBoaqqSkJC37YYm2bt6kSVOn2x0Nbig5OUmxsf/5163jx49p374YBQYGKpQLSOFEzDU4g29BT5Ut6pv1vHQRX1UtUUgXUi7r5IVUzd14VM/eXUGxZ1Ou3K671R2Ku5im1fuu3KUuKS1DX2/5Uy+0qqhTF1J1IuGSujcpJ0lavve0Le8Jt6dxY8fo7patFBpaWnFxp/XxlI/k4emhB9o9ZHc0t2NrMapQoYJ+++03VapUSZK0fv16lStXLmt7bGysQkND7Yrn9s6ePathbwxWfFycCgUEqEqVME2aOl2NmzazOxrc0J7du9X7me5Zz8f+37WA7Tt00jsjr3/KLJAXzDU4Q41SAfqkR/2s5688cOVrLBZHn9Bbi2M0a12sfL089eb/C1OATwFFxyao35zorO8wkqRxPx9UumXpnY415F3QQ7uPX9Dzs7frIjdeQB6cPnVSbw55VQnnzysoqKjq1KuvGbPnK6hoUbujuR2HZVmWXS8+depUlS1bVg89dPXG+/rrr+v06dOaPj1vKxhJaba9JRjmr4trAcBdNIu8/k2PAGdZ/c9WdkeAIQr75O7qIVuLUX6hGMFVKEYA3A3FCK5CMYKr5LYY2XrzBQAAAAC4FVCMAAAAABiPYgQAAADAeBQjAAAAAMajGAEAAAAwHsUIAAAAgPEoRgAAAACMRzECAAAAYDyKEQAAAADjUYwAAAAAGI9iBAAAAMB4FCMAAAAAxqMYAQAAADAexQgAAACA8ShGAAAAAIxHMQIAAABgPIoRAAAAAONRjAAAAAAYj2IEAAAAwHgUIwAAAADGoxgBAAAAMB7FCAAAAIDxKEYAAAAAjEcxAgAAAGA8ihEAAAAA41GMAAAAABiPYgQAAADAeBQjAAAAAMajGAEAAAAwHsUIAAAAgPEoRgAAAACMRzECAAAAYDyHZVmW3SGc7VK63QlgioxMt/u/D25RHg6H3RFgCKYaXOWOft/YHQGG+HNq51ztx4oRAAAAAONRjAAAAAAYj2IEAAAAwHgUIwAAAADGoxgBAAAAMB7FCAAAAIDxKEYAAAAAjEcxAgAAAGA8ihEAAAAA41GMAAAAABiPYgQAAADAeBQjAAAAAMajGAEAAAAwHsUIAAAAgPEoRgAAAACMRzECAAAAYDyKEQAAAADjUYwAAAAAGI9iBAAAAMB4FCMAAAAAxqMYAQAAADAexQgAAACA8ShGAAAAAIxHMQIAAABgPIoRAAAAAONRjAAAAAAYj2IEAAAAwHgUIwAAAADGoxgBAAAAMB7FCAAAAIDxKEYAAAAAjFfA7gCw3/y5czRrxqeKj49T1bBqGvL6UNUOD7c7FtzIV1Hz9FXUPJ3487gk6Y5KlfXcC33V7O4WNieDO9q6ZbNmzfhUMXt3Ky4uTh+Mn6TW97axOxbcFL9DcbMaVQ5Wn/urqna5IipZxFfPTFmvZTtOSJIKeDg0uEMNta5VUuVD/HUh5bJ+2Xdaoxbu0amES1nH6N8uTG1qlVTNsoFKS89U9UFL7Ho7tzVWjAy3bOkPen9MpJ7v01fzv1qosLBqevH5Xjpz5ozd0eBGipcoof4DXtGcqK/1xfwFatiosQb276tDB3+zOxrcUEpKsqqGhSnijeF2R4Gb43conMHPu4D2HEvQ6/N35Njm6+Wp2uWKaNwP+/TAqJV6dtoGVSoRoJl9mmTbz8vTQ99tO65Za464KrZbYsXIcLNnzVDnR7uoY6dHJElvDh+htWtXa9E3X6tX7+dsTgd30bJV62zP+/UfqAVR87Vr5w5VqlzFplRwV83vbqnmd7e0OwYMwO9QOMOqPae0as+pq267eCldT4z/d7axN+bv0NKIe1Q6yFfHz6VIkt5fEiNJ6tKkXP6GdXO33IqRZVl2RzDG5bQ0xezdo8ZNmmaNeXh4qHHjptq5Y7uNyeDOMjIy9OPS75WSkqzwOnXtjgMAN4TfobBLYd8Cysy0lJBy2e4obueWWzHy9vbWjh07VL16dbujuL1z588pIyNDwcHB2caDg4N15Mhhm1LBXf12YL96dntSaWmp8vXz09hxE3VHpcp2xwKAG8LvUNjBu4CH3uhUS4u2HFXipXS747gd24rRoEGDrjqekZGh0aNHZ33QfPDBB9c9TmpqqlJTU7ONWZ7e8vb2dk5QAE5RoWJFzVuwUIkXL2rF8h817M0hmj5jNuUIAIBcKODh0LTejeRwODRkbrTdcdySbcVo3LhxqlOnjooUKZJt3LIsxcTEyN/fXw6H42+PExkZqREjRmQbe2PocL057C0npnVPQUWC5OnpmeMi0TNnzigkJMSmVHBXBQt6qVy58pKkGjVrac/u3Zr7xed6c/jbNicDgLzjdyhcqYCHQ9Oea6TSwb7q8uGvrBblE9uuMRo1apQSEhI0dOhQrVq1Kuvh6empmTNnatWqVVq5cuXfHiciIkIJCQnZHq8NjnDBO7j9FfTyUvUaNbVxw/qssczMTG3cuF7hderZmAwmyLQydTktze4YAHBD+B0KV/mrFFUs5q/Hx/2qc0n87swvtq0YDRkyRPfee6+6deum9u3bKzIyUgULFszzcby9c542R4nOvad6PK2hrw9WzZq1VKt2uL6YPUspKSnq2Kmz3dHgRj4aN1ZNm7dQaGiokpKStOyHJdq6eZMmTZ1udzS4oeTkJMXGxmY9P378mPbti1FgYKBCQ0vZmAzuht+hcAY/b09VLFYo63nZEH/VLBOo80lpOpVwSZ8830i1yxZR90nr5enhULHCV/7uPZ+UpssZV25aVjrIV0X8vVQ6yE+eHg7VLBMoSToSl6jk1AzXv6nblMOy+TZwiYmJ6tu3r6KjozVnzhzVr19f0dHRqlGjxg0fk2KUN/PmfJH15XRh1apr8OtvKjy8jt2xbgsZmdxFMTdGDHtDmzauV3xcnAoFBKhKlTD1fOZZNW7azO5otw2PXJxajCs2b9qo3s90zzHevkMnvTNytA2Jbi9Mtbzhd+iNu6PfN3ZHuCU0qRqirwfl/MLzqPV/aOySGG0a2faqP/fIB2u1/kC8JOnDHg30eJPy193HZH9Ozd0/VthejP4yf/58DRgwQHFxcdq1axfFCLcFihFchWIEV2GqwVUoRnCV3BajW+Z23U888YSaN2+urVu3qnz5nI0XAAAAAPLLLVOMJKlMmTIqU6aM3TEAAAAAGMa2u9IBAAAAwK2CYgQAAADAeBQjAAAAAMajGAEAAAAwHsUIAAAAgPEoRgAAAACMRzECAAAAYDyKEQAAAADjUYwAAAAAGI9iBAAAAMB4FCMAAAAAxqMYAQAAADAexQgAAACA8ShGAAAAAIxHMQIAAABgPIoRAAAAAONRjAAAAAAYj2IEAAAAwHgUIwAAAADGoxgBAAAAMB7FCAAAAIDxKEYAAAAAjEcxAgAAAGA8ihEAAAAA41GMAAAAABiPYgQAAADAeBQjAAAAAMajGAEAAAAwHsUIAAAAgPEoRgAAAACMRzECAAAAYDyKEQAAAADjOSzLsuwOAfulpqYqMjJSERER8vb2tjsO3BhzDa7CXIOrMNfgKsy1/EUxgiTpwoULCgwMVEJCggoXLmx3HLgx5hpchbkGV2GuwVWYa/mLU+kAAAAAGI9iBAAAAMB4FCMAAAAAxqMYQZLk7e2t4cOHcyEf8h1zDa7CXIOrMNfgKsy1/MXNFwAAAAAYjxUjAAAAAMajGAEAAAAwHsUIAAAAgPEoRgAAAACMRzGCJk2apAoVKsjHx0eNGjXSpk2b7I4EN7R27Vq1b99epUqVksPh0KJFi+yOBDcUGRmphg0bKiAgQMWLF1fHjh21f/9+u2PBDU2ZMkXh4eEqXLiwChcurCZNmmjp0qV2x4IBRo8eLYfDoQEDBtgdxe1QjAwXFRWlQYMGafjw4dq2bZvq1KmjBx54QKdPn7Y7GtxMUlKS6tSpo0mTJtkdBW5szZo16tu3rzZs2KDly5fr8uXLuv/++5WUlGR3NLiZMmXKaPTo0dq6dau2bNmi1q1bq0OHDtqzZ4/d0eDGNm/erGnTpik8PNzuKG6J23UbrlGjRmrYsKEmTpwoScrMzFTZsmX10ksvaciQITang7tyOBxauHChOnbsaHcUuLm4uDgVL15ca9asUYsWLeyOAzdXtGhRvffee+rVq5fdUeCGEhMTVb9+fU2ePFn/+te/VLduXY0bN87uWG6FFSODpaWlaevWrWrTpk3WmIeHh9q0aaP169fbmAwAnCMhIUHSlT9YgfySkZGh+fPnKykpSU2aNLE7DtxU37599dBDD2X7uw3OVcDuALBPfHy8MjIyVKJEiWzjJUqU0L59+2xKBQDOkZmZqQEDBqhZs2aqVauW3XHghnbt2qUmTZro0qVLKlSokBYuXKgaNWrYHQtuaP78+dq2bZs2b95sdxS3RjECALilvn37avfu3fr111/tjgI3FRYWpujoaCUkJGjBggXq0aOH1qxZQzmCUx09elQvv/yyli9fLh8fH7vjuDWKkcFCQkLk6empU6dOZRs/deqUSpYsaVMqALh5/fr105IlS7R27VqVKVPG7jhwU15eXqpcubIkqUGDBtq8ebPGjx+vadOm2ZwM7mTr1q06ffq06tevnzWWkZGhtWvXauLEiUpNTZWnp6eNCd0H1xgZzMvLSw0aNNCKFSuyxjIzM7VixQrOkQZwW7IsS/369dPChQu1cuVKVaxY0e5IMEhmZqZSU1PtjgE3c++992rXrl2Kjo7Oetx5553q2rWroqOjKUVOxIqR4QYNGqQePXrozjvv1F133aVx48YpKSlJTz/9tN3R4GYSExN18ODBrOdHjhxRdHS0ihYtqnLlytmYDO6kb9++mjt3rr799lsFBATo5MmTkqTAwED5+vranA7uJCIiQu3atVO5cuV08eJFzZ07V6tXr9aPP/5odzS4mYCAgBzXSfr7+ys4OJjrJ52MYmS4xx9/XHFxcRo2bJhOnjypunXratmyZTluyADcrC1btuiee+7Jej5o0CBJUo8ePTRz5kybUsHdTJkyRZLUqlWrbOMzZsxQz549XR8Ibuv06dPq3r27Tpw4ocDAQIWHh+vHH3/UfffdZ3c0ADeI7zECAAAAYDyuMQIAAABgPIoRAAAAAONRjAAAAAAYj2IEAAAAwHgUIwAAAADGoxgBAAAAMB7FCAAAAIDxKEYAANv07NlTHTt2zHreqlUrDRgwwOU5Vq9eLYfDofPnz19zH4fDoUWLFuX6mG+99Zbq1q17U7l+//13ORwORUdH39RxAAB/j2IEAMimZ8+ecjgccjgc8vLyUuXKlfX2228rPT0931/7m2++0TvvvJOrfXNTZgAAyK0CdgcAANx62rZtqxkzZig1NVU//PCD+vbtq4IFCyoiIiLHvmlpafLy8nLK6xYtWtQpxwEAIK9YMQIA5ODt7a2SJUuqfPnyevHFF9WmTRstXrxY0n9Ofxs5cqRKlSqlsLAwSdLRo0fVpUsXFSlSREWLFlWHDh30+++/Zx0zIyNDgwYNUpEiRRQcHKx//vOfsiwr2+v+76l0qampGjx4sMqWLStvb29VrlxZn376qX7//Xfdc889kqSgoCA5HA717NlTkpSZmanIyEhVrFhRvr6+qlOnjhYsWJDtdX744QdVrVpVvr6+uueee7LlzK3BgweratWq8vPz0x133KGhQ4fq8uXLOfabNm2aypYtKz8/P3Xp0kUJCQnZtk+fPl3Vq1eXj4+PqlWrpsmTJ+c5CwDg5lGMAAB/y9fXV2lpaVnPV6xYof3792v58uVasmSJLl++rAceeEABAQH65Zdf9O9//1uFChVS27Zts35u7Nixmjlzpj777DP9+uuvOnv2rBYuXHjd1+3evbvmzZunCRMmKCYmRtOmTVOhQoVUtmxZff3115Kk/fv368SJExo/frwkKTIyUp9//rmmTp2qPXv2aODAgerWrZvWrFkj6UqB69y5s9q3b6/o6Gg9++yzGjJkSJ7/mwQEBGjmzJnau3evxo8fr08++UQffvhhtn0OHjyoL7/8Ut99952WLVum7du3q0+fPlnb58yZo2HDhmnkyJGKiYnRqFGjNHToUM2aNSvPeQAAN8kCAOC/9OjRw+rQoYNlWZaVmZlpLV++3PL29rZeffXVrO0lSpSwUlNTs35m9uzZVlhYmJWZmZk1lpqaavn6+lo//vijZVmWFRoaao0ZMyZr++XLl60yZcpkvZZlWVbLli2tl19+2bIsy9q/f78lyVq+fPlVc65atcqSZJ07dy5r7NKlS5afn5+1bt26bPv26tXLevLJJy3LsqyIiAirRo0a2bYPHjw4x7H+lyRr4cKF19z+3nvvWQ0aNMh6Pnz4cMvT09M6duxY1tjSpUstDw8P68SJE5ZlWValSpWsuXPnZjvOO++8YzVp0sSyLMs6cuSIJcnavn37NV8XAOAcXGMEAMhhyZIlKlSokC5fvqzMzEz94x//0FtvvZW1vXbt2tmuK9qxY4cOHjyogICAbMe5dOmSDh06pISEBJ04cUKNGjXK2lagQAHdeeedOU6n+0t0dLQ8PT3VsmXLXOc+ePCgkpOTdd9992UbT0tLU7169SRJMTEx2XJIUpMmTXL9Gn+JiorShAkTdOjQISUmJio9PV2FCxfOtk+5cuVUunTpbK+TmZmp/fv3KyAgQIcOHVKvXr3Uu3fvrH3S09MVGBiY5zwAgJtDMQIA5HDPPfdoypQp8vLyUqlSpVSgQPZfF/7+/tmeJyYmqkGDBpozZ06OYxUrVuyGMvj6+ub5ZxITEyVJ33//fbZCIl25bspZ1q9fr65du2rEiBF64IEHFBgYqPnz52vs2LF5zvrJJ5/kKGqenp5OywoAyB2KEQAgB39/f1WuXDnX+9evX19RUVEqXrx4jlWTv4SGhmrjxo1q0aKFpCsrI1u3blX9+vWvun/t2rWVmZmpNWvWqE2bNjm2/7VilZGRkTVWo0YNeXt7KzY29porTdWrV8+6kcRfNmzY8Pdv8r+sW7dO5cuX1xtvvJE19scff+TYLzY2Vn/++adKlSqV9ToeHh4KCwtTiRIlVKpUKR0+fFhdu3bN0+sDAJyPmy8AAG5a165dFRISog4dOuiXX37RkSNHtHr1avXv31/Hjh2TJL388ssaPXq0Fi1apH379qlPnz7X/Q6iChUqqEePHnrmmWe0aNGirGN++eWXkqTy5cvL4XBoyZIliouLU2JiogICAvTqq69q4MCBmjVrlg4dOqRt27bpo48+yrqhwQsvvKDffvtNr732mvbv36+5c+dq5syZeXq/VapUUWxsrObPn69Dhw5pwoQJV72RhI+Pj3r06KEdO3bol19+Uf/+/dWlSxeVLFlSkjRixAhFRkZqwoQJOnDggHbt2qUZM2bogw8+yFMeAMDNoxgBAG6an5+f1q5dq3Llyqlz586qXr26evXqpUuXLmWtIL3yyit66qmn1KNHDzVp0kQBAQHq1KnTdY87ZcoUPfroo+rTp4+qVaum3r17KykpSZJUunRpjRgxQkOGDFGJEiXUr18/SdI777yjoUOHKjIyUtWrV1fbtm31/fffq2LFipKuXPfz9ddfa9GiRapTp46mTp2qUaNG5en9Pvzwwxo4cKD69eununXrat26dRo6dGiO/SpXrqzOnTvrwQcf1P3336/w8PBst+N+9tlnNX36dM2YMUO1a9dWy5YtNXPmzKysAADXcVjXuuoVAAAAAAzBihEAAAAA41GMAAAAABiPYgQAAADAeBQjAAAAAMajGAEAAAAwHsUIAAAAgPEoRgAAAACMRzECAAAAYDyKEQAAAADjUYwAAAAAGI9iBAAAAMB4FCMAAAAAxvv/kReXMUaCXewAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred_NB))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0T2FnArNmvBI",
        "outputId": "23c94f9d-d477-4d31-dfd8-35cbc3c528d9"
      },
      "id": "0T2FnArNmvBI",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.96      0.99      0.98       120\n",
            "           2       0.97      0.97      0.97       153\n",
            "           3       0.99      1.00      0.99       154\n",
            "           4       1.00      0.91      0.95       116\n",
            "           5       0.94      0.97      0.95       125\n",
            "\n",
            "    accuracy                           0.97       668\n",
            "   macro avg       0.97      0.97      0.97       668\n",
            "weighted avg       0.97      0.97      0.97       668\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Generalised function for model**"
      ],
      "metadata": {
        "id": "UbkeNNSmsTXO"
      },
      "id": "UbkeNNSmsTXO"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model training"
      ],
      "metadata": {
        "id": "HLuZoRcMs_n0"
      },
      "id": "HLuZoRcMs_n0"
    },
    {
      "cell_type": "code",
      "source": [
        "def model_training(model,X_train,X_test,y_train):\n",
        "  model.fit(X_train,y_train)\n",
        "  y_pred=model.predict(X_test)\n",
        "  y_pred_proba=model.predict_proba(X_test)\n",
        "  return y_pred,y_pred_proba\n"
      ],
      "metadata": {
        "id": "npCzjCM1odjT"
      },
      "id": "npCzjCM1odjT",
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Evaluation"
      ],
      "metadata": {
        "id": "jcQkjBNytC5r"
      },
      "id": "jcQkjBNytC5r"
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_model(model,y_pred,y_pred_proba,X_train,X_test,y_train,y_test):\n",
        "\n",
        "  #Calculating train and test accuracy\n",
        "  train_acc=accuracy_score(y_train,model.predict(X_train))\n",
        "  test_acc=accuracy_score(y_test,model.predict(X_test))\n",
        "  print(\"Training accuracy: \",train_acc.round(3))\n",
        "  print(\"Testing accuracy: \",test_acc.round(3))\n",
        "\n",
        "  #computing ROC AUC score\n",
        "  rocauc_score= roc_auc_score(y_test,y_pred_proba,multi_class=\"ovr\")\n",
        "  print(\"ROC AUC score: \",rocauc_score.round(3))\n",
        "\n",
        "  #calculating precision,recall,f1score\n",
        "  precision=precision_score(y_test,y_pred,average='weighted')\n",
        "  recall=recall_score(y_test,y_pred,average='weighted')\n",
        "  f1=f1_score(y_test,y_pred,average='weighted')\n",
        "  print(\"Precision score: \",precision.round(3))\n",
        "  print(\"Recall score: \",recall.round(3))\n",
        "  print(\"f1 score: \",f1.round(3))\n",
        "  print()"
      ],
      "metadata": {
        "id": "0upQZkfUtEDd"
      },
      "id": "0upQZkfUtEDd",
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Decision Tree Classifier"
      ],
      "metadata": {
        "id": "T__Xts_ht9UK"
      },
      "id": "T__Xts_ht9UK"
    },
    {
      "cell_type": "code",
      "source": [
        "dt=DecisionTreeClassifier()\n",
        "\n",
        "#model training\n",
        "y_pred_dt,y_pred_proba_dt=model_training(dt)\n",
        "\n",
        "#model evaluation\n",
        "evaluate_model(dt,y_pred_dt,y_pred_proba_dt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FVAxN1FwuBp6",
        "outputId": "ec48ab37-a01e-4473-a15d-e38ad0de7930"
      },
      "id": "FVAxN1FwuBp6",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy:  1.0\n",
            "Testing accuracy:  0.847\n",
            "ROC AUC score:  0.901\n",
            "Precision score:  0.851\n",
            "Recall score:  0.847\n",
            "f1 score:  0.847\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "K Nearest Neighbours Classifier"
      ],
      "metadata": {
        "id": "Pp1bu7wMua2L"
      },
      "id": "Pp1bu7wMua2L"
    },
    {
      "cell_type": "code",
      "source": [
        "knn=KNeighborsClassifier(n_neighbors=5)\n",
        "\n",
        "#model training\n",
        "y_pred_knn,y_pred_proba_knn=model_training(knn)\n",
        "\n",
        "#model evaluation\n",
        "evaluate_model(knn,y_pred_knn,y_pred_proba_knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jfycZU6NvIU0",
        "outputId": "d95be71e-c025-4511-bbb3-48cc9594433e"
      },
      "id": "jfycZU6NvIU0",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy:  0.963\n",
            "Testing accuracy:  0.931\n",
            "ROC AUC score:  0.989\n",
            "Precision score:  0.933\n",
            "Recall score:  0.931\n",
            "f1 score:  0.931\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest Classifier"
      ],
      "metadata": {
        "id": "6ritWecxvr3U"
      },
      "id": "6ritWecxvr3U"
    },
    {
      "cell_type": "code",
      "source": [
        "rf=RandomForestClassifier()\n",
        "\n",
        "#model training\n",
        "y_pred_rf,y_pred_proba_rf=model_training(rf)\n",
        "\n",
        "#model evaluation\n",
        "evaluate_model(rf,y_pred_rf,y_pred_proba_rf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSJPWhdGwHqC",
        "outputId": "9d251029-e796-486e-98b8-32203cae74aa"
      },
      "id": "QSJPWhdGwHqC",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy:  1.0\n",
            "Testing accuracy:  0.972\n",
            "ROC AUC score:  0.997\n",
            "Precision score:  0.972\n",
            "Recall score:  0.972\n",
            "f1 score:  0.972\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***From the above 4 models tested, Naive Bayes Classifier seems to be the best performing model. It provides better training and testing accuracy as well as excellent scores for precision, recall and f1 score.***"
      ],
      "metadata": {
        "id": "qIOvmPTnwWQG"
      },
      "id": "qIOvmPTnwWQG"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nfanfvbIRP1H"
      },
      "id": "nfanfvbIRP1H",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
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