NLP_Final_Project.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9gQauOUf0XV",
        "outputId": "8c1298fb-6d10-46d4-f775-2872781cd063"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to\n",
            "[nltk_data]     C:\\Users\\LapTop\\AppData\\Roaming\\nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to\n",
            "[nltk_data]     C:\\Users\\LapTop\\AppData\\Roaming\\nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "k-Ea7L_tf40w",
        "outputId": "4d1c6a62-9a16-471d-d0f3-b273a2ed0b30"
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      "outputs": [
        {
          "data": {
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              "      <th>text</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8476</td>\n",
              "      <td>You Can Smell Hillary’s Fear</td>\n",
              "      <td>Daniel Greenfield, a Shillman Journalism Fello...</td>\n",
              "      <td>FAKE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10294</td>\n",
              "      <td>Watch The Exact Moment Paul Ryan Committed Pol...</td>\n",
              "      <td>Google Pinterest Digg Linkedin Reddit Stumbleu...</td>\n",
              "      <td>FAKE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3608</td>\n",
              "      <td>Kerry to go to Paris in gesture of sympathy</td>\n",
              "      <td>U.S. Secretary of State John F. Kerry said Mon...</td>\n",
              "      <td>REAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10142</td>\n",
              "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
              "      <td>— Kaydee King (@KaydeeKing) November 9, 2016 T...</td>\n",
              "      <td>FAKE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>875</td>\n",
              "      <td>The Battle of New York: Why This Primary Matters</td>\n",
              "      <td>It's primary day in New York and front-runners...</td>\n",
              "      <td>REAL</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                                              title  \\\n",
              "0        8476                       You Can Smell Hillary’s Fear   \n",
              "1       10294  Watch The Exact Moment Paul Ryan Committed Pol...   \n",
              "2        3608        Kerry to go to Paris in gesture of sympathy   \n",
              "3       10142  Bernie supporters on Twitter erupt in anger ag...   \n",
              "4         875   The Battle of New York: Why This Primary Matters   \n",
              "\n",
              "                                                text label  \n",
              "0  Daniel Greenfield, a Shillman Journalism Fello...  FAKE  \n",
              "1  Google Pinterest Digg Linkedin Reddit Stumbleu...  FAKE  \n",
              "2  U.S. Secretary of State John F. Kerry said Mon...  REAL  \n",
              "3  — Kaydee King (@KaydeeKing) November 9, 2016 T...  FAKE  \n",
              "4  It's primary day in New York and front-runners...  REAL  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "#Read the data\n",
        "news = pd.read_csv(r\"E:/college/Year Four/Second Term/NLP/Project/news/news.csv\", sep=',', header=0)\n",
        "\n",
        "#Data undestanding\n",
        "news.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TbWrvqBBf7nv",
        "outputId": "fc41b7f2-ab11-48b9-a9f2-4b2aebae2f2b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6335 entries, 0 to 6334\n",
            "Data columns (total 4 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   Unnamed: 0  6335 non-null   int64 \n",
            " 1   title       6335 non-null   object\n",
            " 2   text        6335 non-null   object\n",
            " 3   label       6335 non-null   object\n",
            "dtypes: int64(1), object(3)\n",
            "memory usage: 198.1+ KB\n"
          ]
        }
      ],
      "source": [
        "news.isnull().sum()\n",
        "news.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "eZdSwhlDf9RN",
        "outputId": "94ef24da-07b8-4dad-ebf7-f8c46ef64558"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[13], line 17\u001b[0m\n\u001b[0;32m     14\u001b[0m fig \u001b[38;5;241m=\u001b[39m ex\u001b[38;5;241m.\u001b[39mpie(news, names\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m, values\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalues\u001b[39m\u001b[38;5;124m'\u001b[39m, title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNews Distribution\u001b[39m\u001b[38;5;124m\"\u001b[39m, hole\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.20\u001b[39m)\n\u001b[0;32m     16\u001b[0m \u001b[38;5;66;03m# Show the pie chart\u001b[39;00m\n\u001b[1;32m---> 17\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\plotly\\basedatatypes.py:3410\u001b[0m, in \u001b[0;36mBaseFigure.show\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3377\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3378\u001b[0m \u001b[38;5;124;03mShow a figure using either the default renderer(s) or the renderer(s)\u001b[39;00m\n\u001b[0;32m   3379\u001b[0m \u001b[38;5;124;03mspecified by the renderer argument\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3406\u001b[0m \u001b[38;5;124;03mNone\u001b[39;00m\n\u001b[0;32m   3407\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3408\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpio\u001b[39;00m\n\u001b[1;32m-> 3410\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\plotly\\io\\_renderers.py:394\u001b[0m, in \u001b[0;36mshow\u001b[1;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[0;32m    389\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    390\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    391\u001b[0m         )\n\u001b[0;32m    393\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4.2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 394\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    395\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    396\u001b[0m         )\n\u001b[0;32m    398\u001b[0m     ipython_display\u001b[38;5;241m.\u001b[39mdisplay(bundle, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    400\u001b[0m \u001b[38;5;66;03m# external renderers\u001b[39;00m\n",
            "\u001b[1;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
          ]
        }
      ],
      "source": [
        "# visualization\n",
        "import plotly.express as ex\n",
        "\n",
        "ex.pie(news, names='label', title=\"label\", hole=0.20).show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "nhnk4uNvgAUe",
        "outputId": "05c9ebd4-1df9-43b5-c135-2fe68e5f35ca"
      },
      "outputs": [
        {
          "data": {
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              "summary": "{\n  \"name\": \"news\",\n  \"rows\": 6335,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3038,\n        \"min\": 2,\n        \"max\": 10557,\n        \"num_unique_values\": 6335,\n        \"samples\": [\n          9957,\n          7596,\n          8905\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6256,\n        \"samples\": [\n          \"Elizabeth Warren's speech attacking Donald Trump made a bigger argument about Republicans\",\n          \"Assange: Clinton And ISIS Are Funded By Same People, \\u201cTrump Not Permitted To Win\\u201d\",\n          \"Reporters Stunned to Learn Trump Fans Lining Up 12 Hours Before Rally Starts\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6059,\n        \"samples\": [\n          \"donald trump way ahead poll republican nomination. bernie sander win new hampshire primary tuesday, he\\u2019s close hillary clinton national polls. neither trump sander likely win! new fox news tv special, \\u201ctech revolution\\u201d 8 11 pm et sunday night, we\\u2019ll explain better way predict winner look betting odds. give marco rubio 50 percent chance winning nomination, hillary clinton 80 percent chance. betting odds better track record poll pundits. come people put money line, rather people mouth off. george mason university economist robin hanson put way: imagine you\\u2019re bar\\u2026 \\u201cyou're pontificating -- somebody challenge says, \\u2018want bet?\\u2019 us, soon somebody say \\u2018want bet?\\u2019 -- pause. go, \\u2018do really believe that?\\u2019\\u201d careful bet. aren\\u2019t, lose money. think odds wrong? put money mouth is. american politician banned political prediction markets, they\\u2019ve allowed few, like predictit.org. preditctit\\u2019s odds little bettor may trade $850 per candidate. odds bigger unrestricted markets, like england\\u2019s betfair.com, informative. betfair post odds confusing gambling formulas, two u simplify american here: electionbettingodds.com. odds update every five minutes. prediction market like betfair run sketchy bookies. business operate way stock market \\u2013 people buy sell \\u201cshares\\u201d pay based whether candidate successful. today, 10 cents, buy share trump. becomes president, win dollar. odds good track record. november, ben carson surged first place polls, bettor knew would fade--betfair 9 percent. odds 1 percent. betting odds sometimes fail: evening iowa caucus, bettor thought donald trump would win. still beat poll pundits. part reason they\\u2019re good \\u201cwisdom crowds.\\u201d people betting may fool making bad bet -- enough good information whole group bet likely accurate. see tv show \\u201cwho want millionaire.\\u201d contestant ask audience, expert. expert pretty well. get answer right 65 percent time, audience get right 91 percent time. bet prediction market called intrade accurately predicted \\u201camerican idol\\u201d winners, oscar winners, election result almost every u.s. state. even predicted saddam hussein would captured. sadly, government said betting \\u201ccontrary public interest.\\u201d sued intrade put business. longer access intrade\\u2019s interesting useful predictions. fortunately, site still allow political betting, best odds easily readable electionbettingodds.com. we\\u2019ll explain better tv sunday night! john stossel\\u2019s special \\u201ctech revolution\\u201d air sunday night fox news channel 8 pm et. maxim lott fox news supervising producer twitter @maximlott. john stossel author \\\"no can't! government fails -- individual succeed\\\" host \\\"stossel\\\" (fridays 9 pm/et), weekly program highlighting current consumer issue libertarian viewpoint. stossel also appears regularly fox news channel (fnc) providing signature analysis. click information john stossel.\",\n          \"scientist pour $100 million looking alien around weird star 10/26/2016 daily caller astronomer pour $100 million investigating star may surrounded large structure built alien civilization. researcher university california, berkeley\\u2019s breakthrough listen project search extraterrestrial intelligence (seti) turning program\\u2019s $100 million budget investigating star\\u2019s unique behavior. \\u201ceveryone, every seti program telescope, mean every astronomer kind telescope wavelength see tabby\\u2019s star looked it,\\u201d dr. andrew siemion, director berkeley seti research center, said press statement . \\u201cit\\u2019s looked hubble, it\\u2019s looked keck, it\\u2019s looked infrared radio high energy, every possible thing imagine, including whole range seti experiments. nothing found.\\u201d researcher repeatedly scan star eight hour per night next two month examine extremely unusually dimming behavior. star randomly dims much 22 percent output extremely irregular intervals. consistent large orbiting masses, much larger planets, blocking star\\u2019s light pas front it. natural form large mass could cause kic 8462852\\u2019s dimming aren\\u2019t consistent star\\u2019s age . scientist found first possible evidence extraterrestrial civilization around kic 8462852 last october , astronomer yale university top school published study used nasa\\u2019s kepler space telescope. far, astronomer haven\\u2019t found single naturalistic explanation star\\u2019s exceedingly unusual dimming, explains extremely unusual behavior star. astronomer examined 500 star vicinity kic 8462852, seen nothing else like it. dense formation near kic 8462852 similar \\u201cdyson spheres,\\u201d hypothetical, energy-harvesting \\u201cmegastuctures\\u201d theoretical alien could hypothetically build rearranging solar system. scientist pondered existence dyson sphere since 1960s, thinking could potential solution energy problem faced extremely old civilization. seti scientist long argued human could detect distant alien civilization looking technological artifact like dyson sphere orbiting stars. \\u201cwe spent long time trying convince wasn\\u2019t real. weren\\u2019t able to,\\u201d ben montet, caltech astronomer co-authored research star, told gizmodo . \\u201cnone considered phenomenon alone explain observations.\\u201d best naturalistic explanation favored astronomers, involves huge mass comet erratically orbiting star creating enough dust dim light, january analysis star\\u2019s history render hypothesis implausible, since unprecedented dimming continued century. order dim long time period, star would need million time dust comet orbiting case. astronomer estimate dimming would require roughly 648,000 giant comet 200 kilometer diameter, aligned pas front star. chance formation render essentially impossible, currently remotely plausible scientific explanation going kic 8462852. astronomer previously frequently misjudge abnormal stellar occurrence and, usually, abnormality simply new phenomenon. graduate student astronomy, found usual pulsing radio signal predictable seemed sign intelligent life 1967. astronomer even nicknamed signal lgm-1, \\u201clittle green men,\\u201d believed detected signal extraterrestrial civilization , turned first pulsar.\",\n          \"tuesday, 33 u senator elected november sworn vice president joe biden \\u2014 including 12 new chamber. class includes 22 republican 11 democrats, big reason gop 54-46 majority senate overall. here's crazy fact: 46 democrat got vote 54 republican across 2010, 2012, 2014 elections. according nathan nicholson, researcher voting reform advocacy group fairvote, \\\"the 46 democratic caucus member 114th congress received total 67.8 million vote winning seats, 54 republican caucus member received 47.1 million votes.\\\" here's look like chart form: mean republican majority illegitimate anything like that. indeed, 2008 2012, table turned: democrat got senate seat vote share suggested should. problem deck stacked favor republicans. problem deck stacked favor small states, receive equal representation senate despite dramatic variance population. senate profoundly anti-democratic body abolished.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"REAL\",\n          \"FAKE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
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              "      <th>text</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8476</td>\n",
              "      <td>You Can Smell Hillary’s Fear</td>\n",
              "      <td>daniel greenfield, shillman journalism fellow ...</td>\n",
              "      <td>FAKE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10294</td>\n",
              "      <td>Watch The Exact Moment Paul Ryan Committed Pol...</td>\n",
              "      <td>google pinterest digg linkedin reddit stumbleu...</td>\n",
              "      <td>FAKE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3608</td>\n",
              "      <td>Kerry to go to Paris in gesture of sympathy</td>\n",
              "      <td>u.s. secretary state john f. kerry said monday...</td>\n",
              "      <td>REAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10142</td>\n",
              "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
              "      <td>— kaydee king (@kaydeeking) november 9, 2016 l...</td>\n",
              "      <td>FAKE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>875</td>\n",
              "      <td>The Battle of New York: Why This Primary Matters</td>\n",
              "      <td>primary day new york front-runner hillary clin...</td>\n",
              "      <td>REAL</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3b7c9b8-8c9c-4dcd-87ff-7079835c3ae8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c3b7c9b8-8c9c-4dcd-87ff-7079835c3ae8 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-c3b7c9b8-8c9c-4dcd-87ff-7079835c3ae8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-10bca403-349b-4fb8-851f-4233c5dbfd1c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-10bca403-349b-4fb8-851f-4233c5dbfd1c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-10bca403-349b-4fb8-851f-4233c5dbfd1c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Unnamed: 0                                              title  \\\n",
              "0        8476                       You Can Smell Hillary’s Fear   \n",
              "1       10294  Watch The Exact Moment Paul Ryan Committed Pol...   \n",
              "2        3608        Kerry to go to Paris in gesture of sympathy   \n",
              "3       10142  Bernie supporters on Twitter erupt in anger ag...   \n",
              "4         875   The Battle of New York: Why This Primary Matters   \n",
              "\n",
              "                                                text label  \n",
              "0  daniel greenfield, shillman journalism fellow ...  FAKE  \n",
              "1  google pinterest digg linkedin reddit stumbleu...  FAKE  \n",
              "2  u.s. secretary state john f. kerry said monday...  REAL  \n",
              "3  — kaydee king (@kaydeeking) november 9, 2016 l...  FAKE  \n",
              "4  primary day new york front-runner hillary clin...  REAL  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#import\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import string\n",
        "from nltk.stem import SnowballStemmer\n",
        "from nltk.corpus import stopwords\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "import os\n",
        "from textblob import TextBlob\n",
        "from nltk.stem import PorterStemmer\n",
        "from textblob import Word\n",
        "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
        "import sklearn.feature_extraction.text as text\n",
        "from sklearn import model_selection, preprocessing, linear_model, naive_bayes, metrics, svm\n",
        "\n",
        "#pre processing steps like lower case, stemming and lemmatization\n",
        "\n",
        "news['text'] = news['text'].apply(lambda x: \" \".join(x.lower() for x in x.split()))\n",
        "stop = stopwords.words('english')\n",
        "news['text'] = news['text'].apply(lambda x: \" \".join(x for x in x.split() if x not in stop))\n",
        "#st = PorterStemmer()\n",
        "#news['text'] = news['text'].apply(lambda x: \" \".join([st.stem(word) for word in x.split()]))\n",
        "news['text'] =news['text'].apply(lambda x: \" \".join([Word(word).lemmatize() for word in x.split()]))\n",
        "\n",
        "news.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XEq-6orQgCyb",
        "outputId": "da447f11-417c-45bb-958e-177d77714080"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.02360706, 0.01848622, 0.03343499, ..., 0.0566598 , 0.02224422,\n",
              "       0.02275503])"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Splitting data into train and validation\n",
        "train_x, valid_x, train_y, valid_y = model_selection.train_test_split(news['text'], news['label'],test_size=0.20, random_state=0)\n",
        "\n",
        "\n",
        "\n",
        "encoder = preprocessing.LabelEncoder()\n",
        "train_y = encoder.fit_transform(train_y)\n",
        "valid_y = encoder.fit_transform(valid_y)\n",
        "# TFIDF\n",
        "tfidf_vect = TfidfVectorizer()\n",
        "tfidf_vect.fit(news['text'])\n",
        "xtrain_tfidf =  tfidf_vect.transform(train_x)\n",
        "xvalid_tfidf =  tfidf_vect.transform(valid_x)\n",
        "\n",
        "xtrain_tfidf.data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "08szF10_gEmx"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import accuracy_score, mean_squared_error, confusion_matrix\n",
        "\n",
        "def train_model(classifier, feature_vector_train, label, feature_vector_valid, is_neural_net=False):\n",
        "    # fit the training dataset on the classifier\n",
        "    classifier.fit(feature_vector_train, label)\n",
        "    # predict the labels on validation dataset\n",
        "    predictions = classifier.predict(feature_vector_valid)\n",
        "\n",
        "    # calculate accuracy\n",
        "    accuracy = accuracy_score(predictions, valid_y)\n",
        "\n",
        "    # calculate mean squared error\n",
        "    mse = mean_squared_error(valid_y, predictions)\n",
        "\n",
        "    # calculate confusion matrix\n",
        "    cm = confusion_matrix(valid_y, predictions)\n",
        "\n",
        "    # visualize confusion matrix using seaborn and matplotlib\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "    plt.title(\"Confusion Matrix\")\n",
        "    plt.xlabel(\"Predicted Labels\")\n",
        "    plt.ylabel(\"True Labels\")\n",
        "    plt.show()\n",
        "\n",
        "    return accuracy, mse, cm\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "H-wCnuS_gG9a",
        "outputId": "ba224f11-5b74-496f-81fb-4b7db82d2702"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Naive Bayes - Accuracy:  0.8784530386740331\n",
            "Naive Bayes - Mean Squared Error:  0.12154696132596685\n",
            "Naive Bayes - Confusion Matrix:\n",
            " [[495 120]\n",
            " [ 34 618]]\n"
          ]
        }
      ],
      "source": [
        "# Naive Bayes training\n",
        "accuracy_nb, mse_nb, cm_nb = train_model(naive_bayes.MultinomialNB(alpha=0.2), xtrain_tfidf, train_y, xvalid_tfidf)\n",
        "print(\"Naive Bayes - Accuracy: \", accuracy_nb)\n",
        "print(\"Naive Bayes - Mean Squared Error: \", mse_nb)\n",
        "print(\"Naive Bayes - Confusion Matrix:\\n\", cm_nb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "8NZagFhYgKWk",
        "outputId": "05944882-7a5a-4008-de51-cd6b0aa68c6b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Logistic Regression - Accuracy:  0.9163378058405682\n",
            "Logistic Regression - Mean Squared Error:  0.08366219415943173\n",
            "Logistic Regression - Confusion Matrix:\n",
            " [[574  41]\n",
            " [ 65 587]]\n"
          ]
        }
      ],
      "source": [
        "# Linear Classifier on Word Level TF IDF Vectors\n",
        "accuracy_lr, mse_lr, cm_lr = train_model(linear_model.LogisticRegression(), xtrain_tfidf, train_y, xvalid_tfidf)\n",
        "print(\"Logistic Regression - Accuracy: \", accuracy_lr)\n",
        "print(\"Logistic Regression - Mean Squared Error: \", mse_lr)\n",
        "print(\"Logistic Regression - Confusion Matrix:\\n\", cm_lr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pHfs2TtFgM_3"
      },
      "outputs": [],
      "source": [
        "# Importing\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "mm3I4wKjgN8E",
        "outputId": "b0136216-65ee-48fe-f10f-bccf08ff8f99"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SVM - Accuracy:  0.9337016574585635\n",
            "SVM - Mean Squared Error:  0.06629834254143646\n",
            "SVM - Confusion Matrix:\n",
            " [[572  43]\n",
            " [ 41 611]]\n"
          ]
        }
      ],
      "source": [
        "# Support Vector Machine (SVM) training (take time)\n",
        "accuracy_svm, mse_svm, cm_svm = train_model(SVC(kernel='linear'), xtrain_tfidf, train_y, xvalid_tfidf)\n",
        "print(\"SVM - Accuracy: \", accuracy_svm)\n",
        "print(\"SVM - Mean Squared Error: \", mse_svm)\n",
        "print(\"SVM - Confusion Matrix:\\n\", cm_svm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "RBEUoh-FgQg5",
        "outputId": "392e02bc-8864-4c4e-c9d4-7e0398656d61"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Random Forest - Accuracy:  0.89344909234412\n",
            "Random Forest - Mean Squared Error:  0.10655090765588003\n",
            "Random Forest - Confusion Matrix:\n",
            " [[552  63]\n",
            " [ 72 580]]\n"
          ]
        }
      ],
      "source": [
        "# Random Forest Classifier training\n",
        "accuracy_rf, mse_rf, cm_rf = train_model(RandomForestClassifier(n_estimators=100), xtrain_tfidf, train_y, xvalid_tfidf)\n",
        "print(\"Random Forest - Accuracy: \", accuracy_rf)\n",
        "print(\"Random Forest - Mean Squared Error: \", mse_rf)\n",
        "print(\"Random Forest - Confusion Matrix:\\n\", cm_rf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "W3wfUn5ogRI-",
        "outputId": "3e35e451-1b7b-4e48-9fda-46d8c16c5737"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Decision Tree - Accuracy:  0.8208366219415943\n",
            "Decision Tree - Mean Squared Error:  0.17916337805840568\n",
            "Decision Tree - Confusion Matrix:\n",
            " [[494 121]\n",
            " [106 546]]\n"
          ]
        }
      ],
      "source": [
        "# Decision Tree Classifier training\n",
        "accuracy_dt, mse_dt, cm_dt = train_model(DecisionTreeClassifier(), xtrain_tfidf, train_y, xvalid_tfidf)\n",
        "print(\"Decision Tree - Accuracy: \", accuracy_dt)\n",
        "print(\"Decision Tree - Mean Squared Error: \", mse_dt)\n",
        "print(\"Decision Tree - Confusion Matrix:\\n\", cm_dt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "yBHYqXpvgS81",
        "outputId": "11f34842-c711-4f80-c638-73f685ca7fc3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Gradient Boosting - Accuracy:  0.9021310181531176\n",
            "Gradient Boosting - Mean Squared Error:  0.0978689818468824\n",
            "Gradient Boosting - Confusion Matrix:\n",
            " [[561  54]\n",
            " [ 70 582]]\n"
          ]
        }
      ],
      "source": [
        "# Gradient Boosting Classifier training\n",
        "accuracy_gb, mse_gb, cm_gb = train_model(GradientBoostingClassifier(), xtrain_tfidf, train_y, xvalid_tfidf)\n",
        "print(\"Gradient Boosting - Accuracy: \", accuracy_gb)\n",
        "print(\"Gradient Boosting - Mean Squared Error: \", mse_gb)\n",
        "print(\"Gradient Boosting - Confusion Matrix:\\n\", cm_gb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "N9TOFeRKgX80",
        "outputId": "169e621b-14c0-4e5f-b3c4-de3f3a2fe555"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "KNN - Accuracy:  0.5572217837411207\n",
            "KNN - Mean Squared Error:  0.44277821625887925\n",
            "KNN - Confusion Matrix:\n",
            " [[615   0]\n",
            " [561  91]]\n"
          ]
        }
      ],
      "source": [
        "# K-Nearest Neighbors (KNN) Classifier training\n",
        "accuracy_knn, mse_knn, cm_knn = train_model(KNeighborsClassifier(), xtrain_tfidf, train_y, xvalid_tfidf)\n",
        "print(\"KNN - Accuracy: \", accuracy_knn)\n",
        "print(\"KNN - Mean Squared Error: \", mse_knn)\n",
        "print(\"KNN - Confusion Matrix:\\n\", cm_knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xs6FpCRQHInH"
      },
      "source": [
        "## Save the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "5fs-FItbhtoC"
      },
      "outputs": [],
      "source": [
        "import joblib\n",
        "def save_model(model, filename):\n",
        "    # Save the model to a file using joblib\n",
        "    joblib.dump(model, filename)\n",
        "    print(f\"Model saved as {filename}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "obmI7eftHRWd",
        "outputId": "dd4d2a0a-b42e-40ed-8339-f8705695b766"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model saved as naive_bayes_model.joblib\n"
          ]
        }
      ],
      "source": [
        "# Save the Naive Bayes model\n",
        "save_model(naive_bayes.MultinomialNB(alpha=0.2), \"naive_bayes_model.joblib\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9lccP3zTHanp",
        "outputId": "20e776ec-0bb3-4c2e-c55b-c78601f4e8db"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model saved as logistic_regression_model.joblib\n"
          ]
        }
      ],
      "source": [
        "# Save the Logistic Regression model\n",
        "save_model(linear_model.LogisticRegression(), \"logistic_regression_model.joblib\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A1zjgZDxHc9Y",
        "outputId": "b1d9f25c-012b-45db-83d3-1055e9526d06"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model saved as svm_model.joblib\n"
          ]
        }
      ],
      "source": [
        "# SVM\n",
        "save_model(SVC(kernel='linear'), \"svm_model.joblib\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vxadla4tHnsB",
        "outputId": "64c35603-fbc4-489e-d258-bfff634d7ab2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model saved as random_forest_model.joblib\n"
          ]
        }
      ],
      "source": [
        "# Random Forest\n",
        "save_model(RandomForestClassifier(n_estimators=100), \"random_forest_model.joblib\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zb3C0X_aHuh-",
        "outputId": "50449c12-392b-4f36-ebaa-956fdf4d2a50"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model saved as decision_tree_model.joblib\n"
          ]
        }
      ],
      "source": [
        "# Decision Tree\n",
        "save_model(DecisionTreeClassifier(), \"decision_tree_model.joblib\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1vAK3AUpHyel",
        "outputId": "185b2f88-e9c2-4237-99ce-cd27c49878cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model saved as gradient_boosting_model.joblib\n"
          ]
        }
      ],
      "source": [
        "# Gradient Boosting\n",
        "save_model(GradientBoostingClassifier(), \"gradient_boosting_model.joblib\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U4USC_SaH2EG",
        "outputId": "29698031-31b3-41f4-e64b-5dbc0a6d0d23"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model saved as knn_model.joblib\n"
          ]
        }
      ],
      "source": [
        "# K-Nearest Neighbors (KNN)\n",
        "save_model(KNeighborsClassifier(), \"knn_model.joblib\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xzbIp7XqIYCc"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.12.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}