One_Oath_AI_Internship.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/roggyy/Internship-quest/blob/main/One_Oath_AI_Internship.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One Oath Intern Assignment"
      ],
      "metadata": {
        "id": "GzaVQSG0GkkE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Datalink-\n",
        "[link text](https://)https://drive.google.com/drive/folders/1o5-CSBXzxC-ABjwP5gglO8x3Hv7pcCib?usp=sharing\n",
        "\n",
        "pls download the data from above link in your drive,and replace the file paths of all 4 files named Data .csv, negative-words.txt, paragraph.txt, positive-word.txt."
      ],
      "metadata": {
        "id": "KMruO8MZUPzC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preliminary"
      ],
      "metadata": {
        "id": "zV9sNle6F7gT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n"
      ],
      "metadata": {
        "id": "N0tPYiv0GUN8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.read_csv(r\"/content/drive/MyDrive/AI intern Assignment-20230705T154638Z-001/AI intern Assignment/Data .csv\")\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "yM-wyA0GHVLW",
        "outputId": "fc3cd926-16e3-499a-bdad-f5ea134bd657"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         NAME  ROLL SECTION     LOGIC  CONCENTRATION  \\\n",
              "0               Srishti Shahi    28       A  1.611111       2.666667   \n",
              "1             DEBMALYA LAHIRI     8       A  1.833333       1.833333   \n",
              "2               Ananya Naskar     4       A  3.388889       4.100000   \n",
              "3            Deepsikha Naskar     9       A  1.388889       1.750000   \n",
              "4                Aditi Kuilya     2       A  1.222222       1.833333   \n",
              "5                 Rohan Kundu    17       A  2.777778       2.500000   \n",
              "6          Soumyadeep Purkait    27       A  2.500000       3.400000   \n",
              "7         Shiven Singh Rajput    23       A  2.388889       2.500000   \n",
              "8   Harshavardhan Chakraborty    10       A  4.277778       3.166667   \n",
              "9                Simran Ghosh    25       A  3.166667       2.437500   \n",
              "10              Sunita Biswal    31       A  3.444444       2.700000   \n",
              "11              Bishwarup Das     6       A  1.277778       1.000000   \n",
              "12             Subham Bairagi    29       A  2.111111       2.000000   \n",
              "\n",
              "       FOCUS  COGNITIVE SKILL  RETENTION POWER  HARD WORKING  STUDY HABIT  \\\n",
              "0   1.611111         1.000000         1.500000      2.333333     1.611111   \n",
              "1   1.833333         0.000000         0.916667      1.833333     1.833333   \n",
              "2   3.388889         3.750000         3.642857      3.416667     3.388889   \n",
              "3   1.388889         0.625000         1.071429      1.250000     1.388889   \n",
              "4   1.222222         0.000000         0.916667      1.833333     1.222222   \n",
              "5   2.777778         1.750000         2.083333      3.428571     2.666667   \n",
              "6   2.500000         2.000000         2.428571      3.214286     2.500000   \n",
              "7   2.388889         1.250000         2.666667      1.500000     2.388889   \n",
              "8   4.277778         4.333333         5.083333      3.583333     4.277778   \n",
              "9   3.166667         2.000000         2.437500      3.166667     3.166667   \n",
              "10  3.444444         2.000000         3.714286      2.250000     3.444444   \n",
              "11  1.277778         1.200000         0.857143      1.437500     1.277778   \n",
              "12  2.111111         2.000000         1.428571      1.666667     2.111111   \n",
              "\n",
              "    CONSCIOUSNESS  SILLY MISTAKES  KNOWLEDGE GAP  IMPULSIVE  LEARNING GAP  \n",
              "0        1.214286        8.571429       8.000000   8.750000      6.400000  \n",
              "1        1.571429        7.333333       7.333333   8.000000      6.000000  \n",
              "2        3.388889        6.857143       5.555556   5.555556      7.200000  \n",
              "3        1.562500        7.428571       8.000000   8.000000      7.500000  \n",
              "4        0.785714        8.666667       8.222222   9.000000      6.000000  \n",
              "5        2.357143        6.333333       6.444444   7.000000      4.666667  \n",
              "6        2.125000        7.750000       7.111111   7.750000      6.285714  \n",
              "7        3.000000        6.500000       6.888889   6.375000      8.000000  \n",
              "8        4.083333        5.500000       4.555556   4.000000      7.400000  \n",
              "9        3.166667        6.444444       6.444444   6.444444      6.444444  \n",
              "10       3.444444        6.571429       5.333333   5.333333      7.333333  \n",
              "11       1.642857        7.666667       8.444444   8.250000      8.000000  \n",
              "12       2.111111        7.777778       7.777778   7.777778      8.333333  "
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NAME</th>\n",
              "      <th>ROLL</th>\n",
              "      <th>SECTION</th>\n",
              "      <th>LOGIC</th>\n",
              "      <th>CONCENTRATION</th>\n",
              "      <th>FOCUS</th>\n",
              "      <th>COGNITIVE SKILL</th>\n",
              "      <th>RETENTION POWER</th>\n",
              "      <th>HARD WORKING</th>\n",
              "      <th>STUDY HABIT</th>\n",
              "      <th>CONSCIOUSNESS</th>\n",
              "      <th>SILLY MISTAKES</th>\n",
              "      <th>KNOWLEDGE GAP</th>\n",
              "      <th>IMPULSIVE</th>\n",
              "      <th>LEARNING GAP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Srishti Shahi</td>\n",
              "      <td>28</td>\n",
              "      <td>A</td>\n",
              "      <td>1.611111</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>1.611111</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>1.611111</td>\n",
              "      <td>1.214286</td>\n",
              "      <td>8.571429</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.750000</td>\n",
              "      <td>6.400000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DEBMALYA LAHIRI</td>\n",
              "      <td>8</td>\n",
              "      <td>A</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>1.571429</td>\n",
              "      <td>7.333333</td>\n",
              "      <td>7.333333</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ananya Naskar</td>\n",
              "      <td>4</td>\n",
              "      <td>A</td>\n",
              "      <td>3.388889</td>\n",
              "      <td>4.100000</td>\n",
              "      <td>3.388889</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>3.642857</td>\n",
              "      <td>3.416667</td>\n",
              "      <td>3.388889</td>\n",
              "      <td>3.388889</td>\n",
              "      <td>6.857143</td>\n",
              "      <td>5.555556</td>\n",
              "      <td>5.555556</td>\n",
              "      <td>7.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Deepsikha Naskar</td>\n",
              "      <td>9</td>\n",
              "      <td>A</td>\n",
              "      <td>1.388889</td>\n",
              "      <td>1.750000</td>\n",
              "      <td>1.388889</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>1.071429</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>1.388889</td>\n",
              "      <td>1.562500</td>\n",
              "      <td>7.428571</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>7.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Aditi Kuilya</td>\n",
              "      <td>2</td>\n",
              "      <td>A</td>\n",
              "      <td>1.222222</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>1.222222</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>1.222222</td>\n",
              "      <td>0.785714</td>\n",
              "      <td>8.666667</td>\n",
              "      <td>8.222222</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.000000</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rohan Kundu</td>\n",
              "      <td>17</td>\n",
              "      <td>A</td>\n",
              "      <td>2.777778</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.777778</td>\n",
              "      <td>1.750000</td>\n",
              "      <td>2.083333</td>\n",
              "      <td>3.428571</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>2.357143</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>6.444444</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.666667</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Soumyadeep Purkait</td>\n",
              "      <td>27</td>\n",
              "      <td>A</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.428571</td>\n",
              "      <td>3.214286</td>\n",
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              "      <td>2.125000</td>\n",
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              "      <td>7.111111</td>\n",
              "      <td>7.750000</td>\n",
              "      <td>6.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Shiven Singh Rajput</td>\n",
              "      <td>23</td>\n",
              "      <td>A</td>\n",
              "      <td>2.388889</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.388889</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>2.388889</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>6.888889</td>\n",
              "      <td>6.375000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Harshavardhan Chakraborty</td>\n",
              "      <td>10</td>\n",
              "      <td>A</td>\n",
              "      <td>4.277778</td>\n",
              "      <td>3.166667</td>\n",
              "      <td>4.277778</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>5.083333</td>\n",
              "      <td>3.583333</td>\n",
              "      <td>4.277778</td>\n",
              "      <td>4.083333</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>4.555556</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>7.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Simran Ghosh</td>\n",
              "      <td>25</td>\n",
              "      <td>A</td>\n",
              "      <td>3.166667</td>\n",
              "      <td>2.437500</td>\n",
              "      <td>3.166667</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.437500</td>\n",
              "      <td>3.166667</td>\n",
              "      <td>3.166667</td>\n",
              "      <td>3.166667</td>\n",
              "      <td>6.444444</td>\n",
              "      <td>6.444444</td>\n",
              "      <td>6.444444</td>\n",
              "      <td>6.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Sunita Biswal</td>\n",
              "      <td>31</td>\n",
              "      <td>A</td>\n",
              "      <td>3.444444</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>3.444444</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.714286</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>3.444444</td>\n",
              "      <td>3.444444</td>\n",
              "      <td>6.571429</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>7.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Bishwarup Das</td>\n",
              "      <td>6</td>\n",
              "      <td>A</td>\n",
              "      <td>1.277778</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.277778</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>1.437500</td>\n",
              "      <td>1.277778</td>\n",
              "      <td>1.642857</td>\n",
              "      <td>7.666667</td>\n",
              "      <td>8.444444</td>\n",
              "      <td>8.250000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Subham Bairagi</td>\n",
              "      <td>29</td>\n",
              "      <td>A</td>\n",
              "      <td>2.111111</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.111111</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.428571</td>\n",
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              "      <td>2.111111</td>\n",
              "      <td>7.777778</td>\n",
              "      <td>7.777778</td>\n",
              "      <td>7.777778</td>\n",
              "      <td>8.333333</td>\n",
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              "          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": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Q.1** Group the given data into four categories for Bright and Dark traits. Where categories are A(1-0),B(3-2), C(6-4) and D(7-10).\n",
        "\n",
        "For each student calculate the average of bright traits and dark traits and assign them into a category.\n",
        "\n",
        "**Bright trait**s:  LOGIC, CONCENTRATION, FOCUS, COGNITIVE SKILL, RETENTION POWER, HARD WORKING, STUDY HABIT, CONSCIOUSNESS\n",
        "**Dark Trait**: SILLY MISTAKES,KNOWLEDGE GAP, IMPULSIVE, LEARNING GAP\n"
      ],
      "metadata": {
        "id": "scv82FSOIK9x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorize = lambda x: 'A' if x <= 1 else 'B' if x <= 3 else 'C' if x <= 6 else 'D'\n",
        "\n",
        "# Calculate average of dark traits and catogorize\n",
        "df['Average Dark Traits'] = df[['SILLY MISTAKES', 'KNOWLEDGE GAP', 'IMPULSIVE', 'LEARNING GAP']].mean(axis=1).round()\n",
        "\n",
        "df['Dark Category'] = df['Average Dark Traits'].apply(categorize)\n",
        "\n",
        "# Calculate average of bright traits and catogorize\n",
        "df['Average Bright Traits'] = df[['LOGIC', 'CONCENTRATION', 'FOCUS', 'COGNITIVE SKILL', 'RETENTION POWER',\n",
        "                                  'HARD WORKING', 'STUDY HABIT', 'CONSCIOUSNESS']].mean(axis=1).round()\n",
        "\n",
        "# Apply the categorization function to create the 'Bright Category' column\n",
        "df['Bright Category'] = df['Average Bright Traits'].apply(categorize)\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "id": "UTN7be9AJVaM",
        "outputId": "8b9930ae-7178-4678-9199-bbb6902cbe70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         NAME  ROLL SECTION     LOGIC  CONCENTRATION  \\\n",
              "0               Srishti Shahi    28       A  1.611111       2.666667   \n",
              "1             DEBMALYA LAHIRI     8       A  1.833333       1.833333   \n",
              "2               Ananya Naskar     4       A  3.388889       4.100000   \n",
              "3            Deepsikha Naskar     9       A  1.388889       1.750000   \n",
              "4                Aditi Kuilya     2       A  1.222222       1.833333   \n",
              "5                 Rohan Kundu    17       A  2.777778       2.500000   \n",
              "6          Soumyadeep Purkait    27       A  2.500000       3.400000   \n",
              "7         Shiven Singh Rajput    23       A  2.388889       2.500000   \n",
              "8   Harshavardhan Chakraborty    10       A  4.277778       3.166667   \n",
              "9                Simran Ghosh    25       A  3.166667       2.437500   \n",
              "10              Sunita Biswal    31       A  3.444444       2.700000   \n",
              "11              Bishwarup Das     6       A  1.277778       1.000000   \n",
              "12             Subham Bairagi    29       A  2.111111       2.000000   \n",
              "\n",
              "       FOCUS  COGNITIVE SKILL  RETENTION POWER  HARD WORKING  STUDY HABIT  \\\n",
              "0   1.611111         1.000000         1.500000      2.333333     1.611111   \n",
              "1   1.833333         0.000000         0.916667      1.833333     1.833333   \n",
              "2   3.388889         3.750000         3.642857      3.416667     3.388889   \n",
              "3   1.388889         0.625000         1.071429      1.250000     1.388889   \n",
              "4   1.222222         0.000000         0.916667      1.833333     1.222222   \n",
              "5   2.777778         1.750000         2.083333      3.428571     2.666667   \n",
              "6   2.500000         2.000000         2.428571      3.214286     2.500000   \n",
              "7   2.388889         1.250000         2.666667      1.500000     2.388889   \n",
              "8   4.277778         4.333333         5.083333      3.583333     4.277778   \n",
              "9   3.166667         2.000000         2.437500      3.166667     3.166667   \n",
              "10  3.444444         2.000000         3.714286      2.250000     3.444444   \n",
              "11  1.277778         1.200000         0.857143      1.437500     1.277778   \n",
              "12  2.111111         2.000000         1.428571      1.666667     2.111111   \n",
              "\n",
              "    CONSCIOUSNESS  SILLY MISTAKES  KNOWLEDGE GAP  IMPULSIVE  LEARNING GAP  \\\n",
              "0        1.214286        8.571429       8.000000   8.750000      6.400000   \n",
              "1        1.571429        7.333333       7.333333   8.000000      6.000000   \n",
              "2        3.388889        6.857143       5.555556   5.555556      7.200000   \n",
              "3        1.562500        7.428571       8.000000   8.000000      7.500000   \n",
              "4        0.785714        8.666667       8.222222   9.000000      6.000000   \n",
              "5        2.357143        6.333333       6.444444   7.000000      4.666667   \n",
              "6        2.125000        7.750000       7.111111   7.750000      6.285714   \n",
              "7        3.000000        6.500000       6.888889   6.375000      8.000000   \n",
              "8        4.083333        5.500000       4.555556   4.000000      7.400000   \n",
              "9        3.166667        6.444444       6.444444   6.444444      6.444444   \n",
              "10       3.444444        6.571429       5.333333   5.333333      7.333333   \n",
              "11       1.642857        7.666667       8.444444   8.250000      8.000000   \n",
              "12       2.111111        7.777778       7.777778   7.777778      8.333333   \n",
              "\n",
              "    Average Dark Traits Dark Category  Average Bright Traits Bright Category  \n",
              "0                   8.0             D                    2.0               B  \n",
              "1                   7.0             D                    1.0               A  \n",
              "2                   6.0             C                    4.0               C  \n",
              "3                   8.0             D                    1.0               A  \n",
              "4                   8.0             D                    1.0               A  \n",
              "5                   6.0             C                    3.0               B  \n",
              "6                   7.0             D                    3.0               B  \n",
              "7                   7.0             D                    2.0               B  \n",
              "8                   5.0             C                    4.0               C  \n",
              "9                   6.0             C                    3.0               B  \n",
              "10                  6.0             C                    3.0               B  \n",
              "11                  8.0             D                    1.0               A  \n",
              "12                  8.0             D                    2.0               B  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cd03823d-b696-43ff-bf68-ce8dc9ff9982\">\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>NAME</th>\n",
              "      <th>ROLL</th>\n",
              "      <th>SECTION</th>\n",
              "      <th>LOGIC</th>\n",
              "      <th>CONCENTRATION</th>\n",
              "      <th>FOCUS</th>\n",
              "      <th>COGNITIVE SKILL</th>\n",
              "      <th>RETENTION POWER</th>\n",
              "      <th>HARD WORKING</th>\n",
              "      <th>STUDY HABIT</th>\n",
              "      <th>CONSCIOUSNESS</th>\n",
              "      <th>SILLY MISTAKES</th>\n",
              "      <th>KNOWLEDGE GAP</th>\n",
              "      <th>IMPULSIVE</th>\n",
              "      <th>LEARNING GAP</th>\n",
              "      <th>Average Dark Traits</th>\n",
              "      <th>Dark Category</th>\n",
              "      <th>Average Bright Traits</th>\n",
              "      <th>Bright Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Srishti Shahi</td>\n",
              "      <td>28</td>\n",
              "      <td>A</td>\n",
              "      <td>1.611111</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>1.611111</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>1.611111</td>\n",
              "      <td>1.214286</td>\n",
              "      <td>8.571429</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.750000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>D</td>\n",
              "      <td>2.0</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DEBMALYA LAHIRI</td>\n",
              "      <td>8</td>\n",
              "      <td>A</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>1.571429</td>\n",
              "      <td>7.333333</td>\n",
              "      <td>7.333333</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ananya Naskar</td>\n",
              "      <td>4</td>\n",
              "      <td>A</td>\n",
              "      <td>3.388889</td>\n",
              "      <td>4.100000</td>\n",
              "      <td>3.388889</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>3.642857</td>\n",
              "      <td>3.416667</td>\n",
              "      <td>3.388889</td>\n",
              "      <td>3.388889</td>\n",
              "      <td>6.857143</td>\n",
              "      <td>5.555556</td>\n",
              "      <td>5.555556</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>C</td>\n",
              "      <td>4.0</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Deepsikha Naskar</td>\n",
              "      <td>9</td>\n",
              "      <td>A</td>\n",
              "      <td>1.388889</td>\n",
              "      <td>1.750000</td>\n",
              "      <td>1.388889</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>1.071429</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>1.388889</td>\n",
              "      <td>1.562500</td>\n",
              "      <td>7.428571</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Aditi Kuilya</td>\n",
              "      <td>2</td>\n",
              "      <td>A</td>\n",
              "      <td>1.222222</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>1.222222</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>1.222222</td>\n",
              "      <td>0.785714</td>\n",
              "      <td>8.666667</td>\n",
              "      <td>8.222222</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rohan Kundu</td>\n",
              "      <td>17</td>\n",
              "      <td>A</td>\n",
              "      <td>2.777778</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.777778</td>\n",
              "      <td>1.750000</td>\n",
              "      <td>2.083333</td>\n",
              "      <td>3.428571</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>2.357143</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>6.444444</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>6.0</td>\n",
              "      <td>C</td>\n",
              "      <td>3.0</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Soumyadeep Purkait</td>\n",
              "      <td>27</td>\n",
              "      <td>A</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.428571</td>\n",
              "      <td>3.214286</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.125000</td>\n",
              "      <td>7.750000</td>\n",
              "      <td>7.111111</td>\n",
              "      <td>7.750000</td>\n",
              "      <td>6.285714</td>\n",
              "      <td>7.0</td>\n",
              "      <td>D</td>\n",
              "      <td>3.0</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Shiven Singh Rajput</td>\n",
              "      <td>23</td>\n",
              "      <td>A</td>\n",
              "      <td>2.388889</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.388889</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>2.388889</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>6.888889</td>\n",
              "      <td>6.375000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>7.0</td>\n",
              "      <td>D</td>\n",
              "      <td>2.0</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Harshavardhan Chakraborty</td>\n",
              "      <td>10</td>\n",
              "      <td>A</td>\n",
              "      <td>4.277778</td>\n",
              "      <td>3.166667</td>\n",
              "      <td>4.277778</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>5.083333</td>\n",
              "      <td>3.583333</td>\n",
              "      <td>4.277778</td>\n",
              "      <td>4.083333</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>4.555556</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>7.400000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>C</td>\n",
              "      <td>4.0</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Simran Ghosh</td>\n",
              "      <td>25</td>\n",
              "      <td>A</td>\n",
              "      <td>3.166667</td>\n",
              "      <td>2.437500</td>\n",
              "      <td>3.166667</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.437500</td>\n",
              "      <td>3.166667</td>\n",
              "      <td>3.166667</td>\n",
              "      <td>3.166667</td>\n",
              "      <td>6.444444</td>\n",
              "      <td>6.444444</td>\n",
              "      <td>6.444444</td>\n",
              "      <td>6.444444</td>\n",
              "      <td>6.0</td>\n",
              "      <td>C</td>\n",
              "      <td>3.0</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Sunita Biswal</td>\n",
              "      <td>31</td>\n",
              "      <td>A</td>\n",
              "      <td>3.444444</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>3.444444</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.714286</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>3.444444</td>\n",
              "      <td>3.444444</td>\n",
              "      <td>6.571429</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>7.333333</td>\n",
              "      <td>6.0</td>\n",
              "      <td>C</td>\n",
              "      <td>3.0</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Bishwarup Das</td>\n",
              "      <td>6</td>\n",
              "      <td>A</td>\n",
              "      <td>1.277778</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.277778</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>1.437500</td>\n",
              "      <td>1.277778</td>\n",
              "      <td>1.642857</td>\n",
              "      <td>7.666667</td>\n",
              "      <td>8.444444</td>\n",
              "      <td>8.250000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Subham Bairagi</td>\n",
              "      <td>29</td>\n",
              "      <td>A</td>\n",
              "      <td>2.111111</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.111111</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.428571</td>\n",
              "      <td>1.666667</td>\n",
              "      <td>2.111111</td>\n",
              "      <td>2.111111</td>\n",
              "      <td>7.777778</td>\n",
              "      <td>7.777778</td>\n",
              "      <td>7.777778</td>\n",
              "      <td>8.333333</td>\n",
              "      <td>8.0</td>\n",
              "      <td>D</td>\n",
              "      <td>2.0</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd03823d-b696-43ff-bf68-ce8dc9ff9982')\"\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",
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          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Q.2** Write a Python program that counts the frequency of each word in a given text document. Remove any punctuation marks and convert all words to lowercase before counting. Display the **top 5 most frequent words along with their frequencies**. Also calculate the below parameters.\n",
        "\n",
        "The formulas for calculations are :\n",
        "**Positive Score:** This score is calculated by assigning the value of +1 for each word if found in the Positive Dictionary and then adding up all the values.\n",
        "**Negative Score:** This score is calculated by assigning the value of -1 for each word if found in the Negative Dictionary and then adding up all the values. We multiply the score with -1 so that the score is a positive number.\n",
        "\n",
        "**Polarity Score:** This is the score that determines if a given text is positive or negative in nature. It is calculated by using the formula:\n",
        "Polarity Score = (Positive Score – Negative Score)/ ((Positive Score + Negative Score) + 0.000001)\n",
        "Range is from -1 to +1\n",
        "\n",
        "**Average Sentence Length** = the number of words / the number of sentences\n",
        "\n",
        "Note :- Use the given paragraph.txt for calculations\n",
        "\n"
      ],
      "metadata": {
        "id": "IdAqYI-PQeYX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from collections import Counter\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.tokenize import word_tokenize ,sent_tokenize\n",
        "\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')"
      ],
      "metadata": {
        "id": "GEugzvDPPLFS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8074e2d1-4f7b-411a-d870-22529cb322c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the text document\n",
        "with open('/content/drive/MyDrive/AI intern Assignment-20230705T154638Z-001/AI intern Assignment/paragraph.txt', 'r', encoding='utf-8') as file:\n",
        "    text = file.read()\n",
        "\n",
        "##<<---------------------Part 1-------------------->>\n",
        "\n",
        "# Remove punctuation marks and convert to lowercase\n",
        "text = re.sub(r'[^\\w\\s.]', '', text.lower())\n",
        "\n",
        "# Set up NLTK resources\n",
        "stop_words = set(stopwords.words('english'))\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "lemmatized_tokens = []\n",
        "\n",
        "for token in (text.split()):\n",
        "    if token not in stop_words and len(token) > 2:\n",
        "        # Lemmatize the token\n",
        "        lemmatized_token = lemmatizer.lemmatize(token)\n",
        "        lemmatized_tokens.append(lemmatized_token)\n",
        "\n",
        "# Count the frequency of each word\n",
        "word_counts = Counter(lemmatized_tokens)\n",
        "\n",
        "# Display the top 5 most frequent words\n",
        "top_words = word_counts.most_common(5)\n",
        "print(\"Top 5 most frequent words:\")\n",
        "for word, count in top_words:\n",
        "    print(f\"{word}: {count}\")\n",
        "\n",
        "##<<---------------------Part 1-------------------->>\n",
        "\n",
        "##<<---------------------Part 2-------------------->>\n",
        "\n",
        "with open('/content/drive/MyDrive/AI intern Assignment-20230705T154638Z-001/AI intern Assignment/positive-words.txt', 'r', encoding='utf-8') as file:\n",
        "    positWord = file.read()\n",
        "positWord= positWord.lower()\n",
        "positWord= set(positWord.split())\n",
        "# posit_present={}\n",
        "\n",
        "dict(word_counts).keys()\n",
        "\n",
        "pr = {key: value for key, value in word_counts.items() if key in positWord}\n",
        "print()\n",
        "print(\"Positive Dict-\",pr)\n",
        "ps= sum(pr.values())\n",
        "\n",
        "\n",
        "with open('/content/drive/MyDrive/AI intern Assignment-20230705T154638Z-001/AI intern Assignment/negative-words.txt', 'r', encoding='latin-1') as file:\n",
        "    negWord = file.read()\n",
        "negtWord= negWord.lower()\n",
        "negWord= set(negWord.split())\n",
        "\n",
        "dict(word_counts).keys()\n",
        "\n",
        "nr = {key: value for key, value in word_counts.items() if key in negWord}\n",
        "print(\"Negative Dict-\",nr)\n",
        "ns= sum(nr.values())\n",
        "\n",
        "# Calculate polarity score\n",
        "polarity_score = ( ps- ns) / (ps + ns + 0.000001)\n",
        "#print(polarity_score)\n",
        "\n",
        "\n",
        "sl= len(sent_tokenize(text))\n",
        "\n",
        "wl= len(text.split())\n",
        "avgWordPerSen= wl/sl\n",
        "\n",
        "# Display the calculated parameters\n",
        "print(f\"\\nPositive Score: {ps}\")\n",
        "print(f\"Negative Score: {ns}\")\n",
        "print(f\"Polarity Score: {polarity_score:.4f}\")\n",
        "print(f\"Average Sentence Length: {avgWordPerSen:.2f} words/sentence\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdPGYhNsZNWN",
        "outputId": "5399ebb5-16db-4695-a92e-ee3903c43096"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 5 most frequent words:\n",
            "nlp: 12\n",
            "machine: 11\n",
            "model: 10\n",
            "word: 9\n",
            "language: 7\n",
            "\n",
            "Positive Dict- {'intelligence': 1, 'correct': 1, 'sophisticated': 1, 'positive': 1, 'reputation': 1, 'prominent': 1, 'accurate': 1, 'leverage': 1, 'concise': 1, 'gained': 1, 'prominence': 1, 'remarkable': 1, 'breakthrough': 2, 'revolutionized': 1, 'fascinating': 1, 'immense': 1, 'seamless': 1}\n",
            "Negative Dict- {'object': 1, 'complex': 1, 'dense': 1, 'negative': 1}\n",
            "\n",
            "Positive Score: 18\n",
            "Negative Score: 4\n",
            "Polarity Score: 0.6364\n",
            "Average Sentence Length: 18.61 words/sentence\n"
          ]
        }
      ]
    }
  ]
}