data-cleaning.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**below code is to download the nltk module**"
      ],
      "metadata": {
        "id": "Z9nNVV6NiToj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install nltk"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "boPjrbz3ggWO",
        "outputId": "36d1723f-f277-498d-bdea-8ccb16590b8c"
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      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.8/dist-packages (3.7)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.8/dist-packages (from nltk) (7.1.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from nltk) (4.64.1)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.8/dist-packages (from nltk) (2022.6.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from nltk) (1.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import nltk\n",
        "\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "ps=PorterStemmer()\n",
        "all_sw=stopwords.words('english')\n",
        "all_sw.remove('not')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bCSOJ_GGfOj5",
        "outputId": "662600c0-9809-4432-d5e0-c950da7ae3e9"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**below code is to mount the drive into colab**"
      ],
      "metadata": {
        "id": "ps6FMi4efEhR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xSYYaLyFFIEo",
        "outputId": "5d5694b6-8248-415c-e493-e9edcab91408"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**below code is to read a csv file and convert into dataframe**"
      ],
      "metadata": {
        "id": "yzRmOVUmfty-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv('/content/drive/MyDrive/#agnipath.csv')\n",
        "data=data['Unnamed: 2']"
      ],
      "metadata": {
        "id": "Zm4cMvhscMak"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "****"
      ],
      "metadata": {
        "id": "eItde4CJjiBj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**below code is to delete the emojis, other languages,< br > statements from the data**\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "z7hTSy8VeeSK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corpus=[]\n",
        "for i in range(len(data)):\n",
        "  review=re.sub('[^a-zA-Z]',' ',data[i])\n",
        "  review =review.lower()\n",
        "  review = review.split()\n",
        "  review=[ps.stem(word) for word in review if not word in set(all_sw)]\n",
        "  review =' '.join(review)\n",
        "  corpus.append(review)\n"
      ],
      "metadata": {
        "id": "y42mdHTAgwCg"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv=CountVectorizer(max_features=len(data))\n"
      ],
      "metadata": {
        "id": "9xMEI9nFksWn"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data =cv.fit_transform(corpus).toarray()\n"
      ],
      "metadata": {
        "id": "KId1WiBulgB1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
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          "height": 206
        },
        "id": "50bQpEdSjeo7",
        "outputId": "6bc1d8b1-fad8-4eb7-ce0d-69f18bf4ec12"
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      "execution_count": 135,
      "outputs": [
        {
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              "   Unnamed: 0                                              Tweet  \\\n",
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              "3  @Write2divya Himachal Walo Ne Kya Kiya .. Agni...  \n",
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      "source": [
        "data=data['Unnamed: 2']\n",
        "data.head()"
      ],
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              "0    Na ruke na kabhi na jhuke na kabhi na dabe na ...\n",
              "1    @summer_shower8 @codenamelineman matlab ye, ki...\n",
              "2                                              #VALUE!\n",
              "3    @Write2divya Himachal Walo Ne Kya Kiya .. Agni...\n",
              "4                                              #VALUE!\n",
              "Name: Unnamed: 2, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**to delete the null values**"
      ],
      "metadata": {
        "id": "Y3BllLO7j84b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data=data.dropna()"
      ],
      "metadata": {
        "id": "c9tr26j7ciNh"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape[0]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pcKO_898cq5a",
        "outputId": "c23b84ab-c0da-4247-af52-aae59a48b852"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20000"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**below code is to save the updated dataframe to a csv and save it in the drive**"
      ],
      "metadata": {
        "id": "2YO4BnukgwHN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.to_csv('/content/drive/MyDrive/#JusticeForDefenceStudents2.0.csv')"
      ],
      "metadata": {
        "id": "TUp25MyyfcnJ"
      },
      "execution_count": 133,
      "outputs": []
    }
  ]
}