news_classification.ipynb

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      "source": [
        "NEWS CLASSIFICATIONS   \n",
        "  SUBMITTED BY:-Sampuran Kishore Srivastava  EMAIL ID:-sampuran01122001@gmail.com"
      ],
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        "id": "yd3pX4Dgeiq9"
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      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk) (1.15.0)\n"
          ]
        }
      ],
      "source": [
        "pip install nltk"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk"
      ],
      "metadata": {
        "id": "82SJEu6iesKD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('punkt')"
      ],
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        },
        "id": "9ns_Xnu_exWG",
        "outputId": "83a4e2bc-7332-48a1-f59d-17723877d848"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
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              "True"
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      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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        "id": "mey1P4WUgyas"
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      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "fake = pd.read_csv(\"/content/drive/MyDrive/Fake.csv\")\n",
        "genuine = pd.read_csv(\"/content/drive/MyDrive/True.csv\")"
      ],
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        "id": "mGEuJySohBRG"
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      "outputs": []
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        "display(fake.info())"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 23481 entries, 0 to 23480\n",
            "Data columns (total 4 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   title    23481 non-null  object\n",
            " 1   text     23481 non-null  object\n",
            " 2   subject  23481 non-null  object\n",
            " 3   date     23481 non-null  object\n",
            "dtypes: object(4)\n",
            "memory usage: 733.9+ KB\n"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 21417 entries, 0 to 21416\n",
            "Data columns (total 4 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   title    21417 non-null  object\n",
            " 1   text     21417 non-null  object\n",
            " 2   subject  21417 non-null  object\n",
            " 3   date     21417 non-null  object\n",
            "dtypes: object(4)\n",
            "memory usage: 669.4+ KB\n"
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              "      <td>Factbox: Trump on Twitter (Dec 29) - Approval ...</td>\n",
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              "      <td>politicsNews</td>\n",
              "      <td>December 29, 2017</td>\n",
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              "      <td>Trump on Twitter (Dec 28) - Global Warming</td>\n",
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              "      <td>politicsNews</td>\n",
              "      <td>December 29, 2017</td>\n",
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              "      <td>politicsNews</td>\n",
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              "                                               title  ...                date\n",
              "0  As U.S. budget fight looms, Republicans flip t...  ...  December 31, 2017 \n",
              "1  U.S. military to accept transgender recruits o...  ...  December 29, 2017 \n",
              "2  Senior U.S. Republican senator: 'Let Mr. Muell...  ...  December 31, 2017 \n",
              "3  FBI Russia probe helped by Australian diplomat...  ...  December 30, 2017 \n",
              "4  Trump wants Postal Service to charge 'much mor...  ...  December 29, 2017 \n",
              "5  White House, Congress prepare for talks on spe...  ...  December 29, 2017 \n",
              "6  Trump says Russia probe will be fair, but time...  ...  December 29, 2017 \n",
              "7  Factbox: Trump on Twitter (Dec 29) - Approval ...  ...  December 29, 2017 \n",
              "8         Trump on Twitter (Dec 28) - Global Warming  ...  December 29, 2017 \n",
              "9  Alabama official to certify Senator-elect Jone...  ...  December 28, 2017 \n",
              "\n",
              "[10 rows x 4 columns]"
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          "data": {
            "text/plain": [
              "News               9050\n",
              "politics           6841\n",
              "left-news          4459\n",
              "Government News    1570\n",
              "US_News             783\n",
              "Middle-east         778\n",
              "Name: subject, dtype: int64"
            ]
          },
          "metadata": {}
        }
      ]
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        "genuine['target']=1"
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              "      <td>politicsNews</td>\n",
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              "      <td>Factbox: Trump on Twitter (Dec 29) - Approval ...</td>\n",
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              "            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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              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ],
            "text/plain": [
              "                                               title  ... target\n",
              "0  As U.S. budget fight looms, Republicans flip t...  ...      1\n",
              "1  U.S. military to accept transgender recruits o...  ...      1\n",
              "2  Senior U.S. Republican senator: 'Let Mr. Muell...  ...      1\n",
              "3  FBI Russia probe helped by Australian diplomat...  ...      1\n",
              "4  Trump wants Postal Service to charge 'much mor...  ...      1\n",
              "5  White House, Congress prepare for talks on spe...  ...      1\n",
              "6  Trump says Russia probe will be fair, but time...  ...      1\n",
              "7  Factbox: Trump on Twitter (Dec 29) - Approval ...  ...      1\n",
              "8         Trump on Twitter (Dec 28) - Global Warming  ...      1\n",
              "9  Alabama official to certify Senator-elect Jone...  ...      1\n",
              "\n",
              "[10 rows x 5 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(fake.head(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "ND2OKwtXnneo",
        "outputId": "b530e087-68a4-4af8-a2e7-4a5a5a146726"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "      <td>Donald Trump Sends Out Embarrassing New Year’...</td>\n",
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              "      <td>December 31, 2017</td>\n",
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              "      <td>December 31, 2017</td>\n",
              "      <td>0</td>\n",
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              "      <th>2</th>\n",
              "      <td>Sheriff David Clarke Becomes An Internet Joke...</td>\n",
              "      <td>On Friday, it was revealed that former Milwauk...</td>\n",
              "      <td>News</td>\n",
              "      <td>December 30, 2017</td>\n",
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              "      <th>3</th>\n",
              "      <td>Trump Is So Obsessed He Even Has Obama’s Name...</td>\n",
              "      <td>On Christmas day, Donald Trump announced that ...</td>\n",
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              "      <td>December 29, 2017</td>\n",
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              "      <td>News</td>\n",
              "      <td>December 25, 2017</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Fresh Off The Golf Course, Trump Lashes Out A...</td>\n",
              "      <td>Donald Trump spent a good portion of his day a...</td>\n",
              "      <td>News</td>\n",
              "      <td>December 23, 2017</td>\n",
              "      <td>0</td>\n",
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              "      <th>7</th>\n",
              "      <td>Trump Said Some INSANELY Racist Stuff Inside ...</td>\n",
              "      <td>In the wake of yet another court decision that...</td>\n",
              "      <td>News</td>\n",
              "      <td>December 23, 2017</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Former CIA Director Slams Trump Over UN Bully...</td>\n",
              "      <td>Many people have raised the alarm regarding th...</td>\n",
              "      <td>News</td>\n",
              "      <td>December 22, 2017</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>WATCH: Brand-New Pro-Trump Ad Features So Muc...</td>\n",
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              "      <td>News</td>\n",
              "      <td>December 21, 2017</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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              "          const element = document.querySelector('#df-13e19cf4-43d5-41e8-b5f2-0d3f7a7d084c');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                               title  ... target\n",
              "0   Donald Trump Sends Out Embarrassing New Year’...  ...      0\n",
              "1   Drunk Bragging Trump Staffer Started Russian ...  ...      0\n",
              "2   Sheriff David Clarke Becomes An Internet Joke...  ...      0\n",
              "3   Trump Is So Obsessed He Even Has Obama’s Name...  ...      0\n",
              "4   Pope Francis Just Called Out Donald Trump Dur...  ...      0\n",
              "5   Racist Alabama Cops Brutalize Black Boy While...  ...      0\n",
              "6   Fresh Off The Golf Course, Trump Lashes Out A...  ...      0\n",
              "7   Trump Said Some INSANELY Racist Stuff Inside ...  ...      0\n",
              "8   Former CIA Director Slams Trump Over UN Bully...  ...      0\n",
              "9   WATCH: Brand-New Pro-Trump Ad Features So Muc...  ...      0\n",
              "\n",
              "[10 rows x 5 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.concat([fake,genuine],axis=0)"
      ],
      "metadata": {
        "id": "R9U3paMqnv1j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=data.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "VsztS4_Sojj9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=data.drop(['subject','date','title'],axis=1)"
      ],
      "metadata": {
        "id": "MPrDfi_qo0jr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_AqOWqZIr4Et",
        "outputId": "72ed4f25-cf99-4982-90b3-50254ef18397"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['text', 'target'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TOKENIZATION\n"
      ],
      "metadata": {
        "id": "9Usw4LIfwBlU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.tokenize import word_tokenize"
      ],
      "metadata": {
        "id": "QLJx_nsNsEzI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['text']=data['text'].apply(word_tokenize)"
      ],
      "metadata": {
        "id": "6urvFWLGsTko"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.head(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4Xox0iisn3h",
        "outputId": "7598bc51-ce95-4a40-f40d-b57e4c998aee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                text  target\n",
            "0  [Donald, Trump, just, couldn, t, wish, all, Am...       0\n",
            "1  [House, Intelligence, Committee, Chairman, Dev...       0\n",
            "2  [On, Friday, ,, it, was, revealed, that, forme...       0\n",
            "3  [On, Christmas, day, ,, Donald, Trump, announc...       0\n",
            "4  [Pope, Francis, used, his, annual, Christmas, ...       0\n",
            "5  [The, number, of, cases, of, cops, brutalizing...       0\n",
            "6  [Donald, Trump, spent, a, good, portion, of, h...       0\n",
            "7  [In, the, wake, of, yet, another, court, decis...       0\n",
            "8  [Many, people, have, raised, the, alarm, regar...       0\n",
            "9  [Just, when, you, might, have, thought, we, d,...       0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "STEMMING\n"
      ],
      "metadata": {
        "id": "HPlRux-vvAnq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.stem.snowball import SnowballStemmer\n",
        "porter = SnowballStemmer(\"english\")"
      ],
      "metadata": {
        "id": "XkDLzclYs8Rp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def stem_it(text):\n",
        "  return [porter.stem(word) for word in text]"
      ],
      "metadata": {
        "id": "6pteahR6tieJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['text']=data['text'].apply(stem_it)"
      ],
      "metadata": {
        "id": "TWbbuofLt6OX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.head(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k-6PPO2RuGkU",
        "outputId": "fe326832-c0e3-42f9-fb90-556be1128d67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                text  target\n",
            "0  [donald, trump, just, couldn, t, wish, all, am...       0\n",
            "1  [hous, intellig, committe, chairman, devin, nu...       0\n",
            "2  [on, friday, ,, it, was, reveal, that, former,...       0\n",
            "3  [on, christma, day, ,, donald, trump, announc,...       0\n",
            "4  [pope, franci, use, his, annual, christma, day...       0\n",
            "5  [the, number, of, case, of, cop, brutal, and, ...       0\n",
            "6  [donald, trump, spent, a, good, portion, of, h...       0\n",
            "7  [in, the, wake, of, yet, anoth, court, decis, ...       0\n",
            "8  [mani, peopl, have, rais, the, alarm, regard, ...       0\n",
            "9  [just, when, you, might, have, thought, we, d,...       0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "STOPWORD REMOVAL"
      ],
      "metadata": {
        "id": "UE40x3VgxKlU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def stop_it(t):\n",
        "  dt=[word for word in t if len(word)>2]\n",
        "  return dt"
      ],
      "metadata": {
        "id": "cldtC66Wwd2C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['text']=data['text'].apply(stop_it)"
      ],
      "metadata": {
        "id": "ZzQ5IbASyI_Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.head(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TI8dTiZfykr_",
        "outputId": "33614ff5-3bf7-4874-ca28-b401aa87fa60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                text  target\n",
            "0  [donald, trump, just, couldn, wish, all, ameri...       0\n",
            "1  [hous, intellig, committe, chairman, devin, nu...       0\n",
            "2  [friday, was, reveal, that, former, milwauke, ...       0\n",
            "3  [christma, day, donald, trump, announc, that, ...       0\n",
            "4  [pope, franci, use, his, annual, christma, day...       0\n",
            "5  [the, number, case, cop, brutal, and, kill, pe...       0\n",
            "6  [donald, trump, spent, good, portion, his, day...       0\n",
            "7  [the, wake, yet, anoth, court, decis, that, de...       0\n",
            "8  [mani, peopl, have, rais, the, alarm, regard, ...       0\n",
            "9  [just, when, you, might, have, thought, get, b...       0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['text']=data['text'].apply(' '.join)"
      ],
      "metadata": {
        "id": "L4DbGKD-yuHW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " Splitting up of data"
      ],
      "metadata": {
        "id": "CGwirPEDzSWx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(data['text'], data['target'], test_size=0.25)\n",
        "display(X_train.head())\n",
        "print('\\n')\n",
        "display(y_train.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "id": "4lE2oxtGzF2X",
        "outputId": "8014586f-0d6b-40da-fece-a70c71a8e04a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "31481    melbourn fla. reuter republican presidenti nom...\n",
              "40550    washington reuter u.s. secretari state rex til...\n",
              "21991    tune the altern current radio network acr for ...\n",
              "3354     sarah palin realli care about children with di...\n",
              "25571    washington reuter adam schiff the top democrat...\n",
              "Name: text, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "31481    1\n",
              "40550    1\n",
              "21991    0\n",
              "3354     0\n",
              "25571    1\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " Vectorization"
      ],
      "metadata": {
        "id": "kKSSvAEz2jo1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "my_tfidf = TfidfVectorizer( max_df=0.7)\n",
        "\n",
        "tfidf_train = my_tfidf.fit_transform(X_train)\n",
        "tfidf_test = my_tfidf.transform(X_test)\n"
      ],
      "metadata": {
        "id": "uzARnI461kAz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(tfidf_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-X7oZqbv1kPe",
        "outputId": "f86760a7-43c7-42a9-fb58-5c51bc4cfbfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 78058)\t0.04014790434598124\n",
            "  (0, 29900)\t0.06853322518420842\n",
            "  (0, 63041)\t0.08486958326350102\n",
            "  (0, 10552)\t0.016855958918614196\n",
            "  (0, 7573)\t0.03988720839514482\n",
            "  (0, 81187)\t0.028310421114374748\n",
            "  (0, 54594)\t0.05333411755923839\n",
            "  (0, 48426)\t0.0459077388287403\n",
            "  (0, 50061)\t0.06754956346223275\n",
            "  (0, 77137)\t0.05552121005973836\n",
            "  (0, 59771)\t0.050602590677677625\n",
            "  (0, 57812)\t0.032090906981446794\n",
            "  (0, 40888)\t0.04753551488813618\n",
            "  (0, 57273)\t0.08241003434292632\n",
            "  (0, 68943)\t0.023062176510872053\n",
            "  (0, 82014)\t0.023273482213809208\n",
            "  (0, 61345)\t0.051643885775058546\n",
            "  (0, 69208)\t0.06145780595063846\n",
            "  (0, 86648)\t0.11955703818713127\n",
            "  (0, 42231)\t0.1073174760019092\n",
            "  (0, 29866)\t0.03896962047529929\n",
            "  (0, 64256)\t0.03146254802710105\n",
            "  (0, 40720)\t0.037867628932682175\n",
            "  (0, 37374)\t0.06395897997581496\n",
            "  (0, 89432)\t0.045302223427674135\n",
            "  :\t:\n",
            "  (33672, 24053)\t0.02277737722665097\n",
            "  (33672, 29977)\t0.03907105624782276\n",
            "  (33672, 37525)\t0.08211709312276977\n",
            "  (33672, 88396)\t0.01892017732512078\n",
            "  (33672, 90674)\t0.02036743495116809\n",
            "  (33672, 37666)\t0.013652579134023827\n",
            "  (33672, 7156)\t0.032789995411547826\n",
            "  (33672, 39544)\t0.022792572630642466\n",
            "  (33672, 58890)\t0.05468608251180898\n",
            "  (33672, 43830)\t0.03076425567963707\n",
            "  (33672, 73358)\t0.0267578168488615\n",
            "  (33672, 17567)\t0.023895893396623884\n",
            "  (33672, 27475)\t0.023545066515432082\n",
            "  (33672, 90645)\t0.06093793129885781\n",
            "  (33672, 57594)\t0.060439537888261036\n",
            "  (33672, 36802)\t0.0173482530303453\n",
            "  (33672, 78803)\t0.021795419332193203\n",
            "  (33672, 31782)\t0.02243628633074803\n",
            "  (33672, 7976)\t0.01742423275981251\n",
            "  (33672, 83412)\t0.0821640049601673\n",
            "  (33672, 83055)\t0.20181525914202253\n",
            "  (33672, 26561)\t0.01862461856266686\n",
            "  (33672, 65193)\t0.025908919026430933\n",
            "  (33672, 69018)\t0.08684927119334528\n",
            "  (33672, 69347)\t0.017051268048817365\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "LogisticRegression"
      ],
      "metadata": {
        "id": "3q9vSGf_4S2P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "DIcGoL571keQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_1 = LogisticRegression(max_iter=900)\n",
        "model_1.fit(tfidf_train, y_train)\n",
        "pred_1 = model_1.predict(tfidf_test)\n",
        "crl = accuracy_score(y_test,pred_1)\n",
        "print(crl*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t4WV5LQV5Cvy",
        "outputId": "8662777d-88f5-4a55-bc82-53e4a2e7b21e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "98.85077951002226\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " PassiveAggressiveClassifier"
      ],
      "metadata": {
        "id": "J4RUkdlb54MW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import PassiveAggressiveClassifier\n",
        "\n",
        "model = PassiveAggressiveClassifier(max_iter=50)\n",
        "model.fit(tfidf_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zpRVQqiL5yPz",
        "outputId": "ce8bc7d7-64b4-4b03-d54d-3bd94e8c25e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassiveAggressiveClassifier(max_iter=50)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(tfidf_test)\n",
        "accscore = accuracy_score(y_test, y_pred)\n",
        "print('The accuracy of prediction is ',accscore*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HCORWg4F6dKA",
        "outputId": "a179d208-b538-46ad-9c47-dd6bf889fc8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of prediction is  99.60801781737194\n"
          ]
        }
      ]
    }
  ]
}