minor_EC3082_upd.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "28774910",
      "metadata": {
        "id": "28774910"
      },
      "source": [
        "# Importing required modules/frameworks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "23b2e2b9",
      "metadata": {
        "id": "23b2e2b9"
      },
      "outputs": [],
      "source": [
        "#importing libraries\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "25083733",
      "metadata": {
        "id": "25083733"
      },
      "source": [
        "# Reading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "28133c5a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695
        },
        "id": "28133c5a",
        "outputId": "eab1ac9e-5337-4646-a622-d4fa82ad8b26"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Unnamed: 0  roll_no           student_name branch/course  sdur_C1  \\\n",
              "0            NaN  1905858    RUDRA PRATAP BEHERA           CSE       12   \n",
              "1            NaN  1904043          SATYAM PANDEY           ETC        3   \n",
              "2            NaN  1904081       HARSHEETA KUMARI           ETC        5   \n",
              "3            NaN  9999999  Mai Kyu Batau ???????           ETC       69   \n",
              "4            NaN  1904287        SHIVANI  KUMARI           ETC        2   \n",
              "...          ...      ...                    ...           ...      ...   \n",
              "2404         NaN  2170214    Shakti Prasad Panda           MCA       39   \n",
              "2405         NaN  2170225        Mrunmayee Nayak           MCA       44   \n",
              "2406         NaN  2170228       Divya Tejaswinee           MCA       37   \n",
              "2407         NaN  2170240         Aurobindo Rout           MCA       67   \n",
              "2408         NaN  2170241        Madhusmita Dash           MCA       16   \n",
              "\n",
              "       pref_C1  sdur_C2   pref_C2  sdur_C3   pref_C3  sdur_DE1  pref_DE1  \\\n",
              "0     hardcopy        3  hardcopy        5  hardcopy        10  hardcopy   \n",
              "1     hardcopy        3  hardcopy        3  hardcopy         3  hardcopy   \n",
              "2        eBook        1     eBook        2     eBook         5  hardcopy   \n",
              "3        eBook       69     eBook       69  hardcopy        69  hardcopy   \n",
              "4        eBook        2     eBook        2     eBook         2     eBook   \n",
              "...        ...      ...       ...      ...       ...       ...       ...   \n",
              "2404  hardcopy       18     eBook       36  hardcopy        49  hardcopy   \n",
              "2405  hardcopy        2     eBook       20     eBook        36  hardcopy   \n",
              "2406  hardcopy       38  hardcopy       62  hardcopy        20     eBook   \n",
              "2407     eBook       42  hardcopy       17     eBook        62  hardcopy   \n",
              "2408     eBook       10     eBook       18     eBook        17     eBook   \n",
              "\n",
              "      sdur_DE2  pref_DE2  sdur_DE3  pref_DE3  sdur_OE1  pref_OE1  \n",
              "0            6  hardcopy         2  hardcopy         9     eBook  \n",
              "1            3  hardcopy         3  hardcopy         3  hardcopy  \n",
              "2            8     eBook         8     eBook         8     eBook  \n",
              "3           69  hardcopy        69     eBook        69  hardcopy  \n",
              "4            2     eBook         2     eBook         2     eBook  \n",
              "...        ...       ...       ...       ...       ...       ...  \n",
              "2404        18     eBook         7     eBook         7     eBook  \n",
              "2405        32  hardcopy        13     eBook        13     eBook  \n",
              "2406        38  hardcopy        45  hardcopy        45  hardcopy  \n",
              "2407        13     eBook        50  hardcopy        50  hardcopy  \n",
              "2408        49  hardcopy         9     eBook         9     eBook  \n",
              "\n",
              "[2409 rows x 18 columns]"
            ],
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              "      <th>Unnamed: 0</th>\n",
              "      <th>roll_no</th>\n",
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              "      <th>pref_OE1</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1905858</td>\n",
              "      <td>RUDRA PRATAP BEHERA</td>\n",
              "      <td>CSE</td>\n",
              "      <td>12</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>5</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>10</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>6</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>2</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>9</td>\n",
              "      <td>eBook</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1904043</td>\n",
              "      <td>SATYAM PANDEY</td>\n",
              "      <td>ETC</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1904081</td>\n",
              "      <td>HARSHEETA KUMARI</td>\n",
              "      <td>ETC</td>\n",
              "      <td>5</td>\n",
              "      <td>eBook</td>\n",
              "      <td>1</td>\n",
              "      <td>eBook</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>5</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>8</td>\n",
              "      <td>eBook</td>\n",
              "      <td>8</td>\n",
              "      <td>eBook</td>\n",
              "      <td>8</td>\n",
              "      <td>eBook</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9999999</td>\n",
              "      <td>Mai Kyu Batau ???????</td>\n",
              "      <td>ETC</td>\n",
              "      <td>69</td>\n",
              "      <td>eBook</td>\n",
              "      <td>69</td>\n",
              "      <td>eBook</td>\n",
              "      <td>69</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>69</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>69</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>69</td>\n",
              "      <td>eBook</td>\n",
              "      <td>69</td>\n",
              "      <td>hardcopy</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1904287</td>\n",
              "      <td>SHIVANI  KUMARI</td>\n",
              "      <td>ETC</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2404</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2170214</td>\n",
              "      <td>Shakti Prasad Panda</td>\n",
              "      <td>MCA</td>\n",
              "      <td>39</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>18</td>\n",
              "      <td>eBook</td>\n",
              "      <td>36</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>49</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>18</td>\n",
              "      <td>eBook</td>\n",
              "      <td>7</td>\n",
              "      <td>eBook</td>\n",
              "      <td>7</td>\n",
              "      <td>eBook</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2405</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2170225</td>\n",
              "      <td>Mrunmayee Nayak</td>\n",
              "      <td>MCA</td>\n",
              "      <td>44</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>20</td>\n",
              "      <td>eBook</td>\n",
              "      <td>36</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>32</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>13</td>\n",
              "      <td>eBook</td>\n",
              "      <td>13</td>\n",
              "      <td>eBook</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2406</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2170228</td>\n",
              "      <td>Divya Tejaswinee</td>\n",
              "      <td>MCA</td>\n",
              "      <td>37</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>38</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>62</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>20</td>\n",
              "      <td>eBook</td>\n",
              "      <td>38</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>45</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>45</td>\n",
              "      <td>hardcopy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2407</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2170240</td>\n",
              "      <td>Aurobindo Rout</td>\n",
              "      <td>MCA</td>\n",
              "      <td>67</td>\n",
              "      <td>eBook</td>\n",
              "      <td>42</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>17</td>\n",
              "      <td>eBook</td>\n",
              "      <td>62</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>13</td>\n",
              "      <td>eBook</td>\n",
              "      <td>50</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>50</td>\n",
              "      <td>hardcopy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2408</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2170241</td>\n",
              "      <td>Madhusmita Dash</td>\n",
              "      <td>MCA</td>\n",
              "      <td>16</td>\n",
              "      <td>eBook</td>\n",
              "      <td>10</td>\n",
              "      <td>eBook</td>\n",
              "      <td>18</td>\n",
              "      <td>eBook</td>\n",
              "      <td>17</td>\n",
              "      <td>eBook</td>\n",
              "      <td>49</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>9</td>\n",
              "      <td>eBook</td>\n",
              "      <td>9</td>\n",
              "      <td>eBook</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2409 rows × 18 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57a9b52e-6c8e-4782-a10e-eebf1f854aa1')\"\n",
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              "                                                     [key], {});\n",
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              "\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",
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            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "#reading the excel sheet\n",
        "df = pd.read_excel('student.xlsx')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "d86bf956",
      "metadata": {
        "id": "d86bf956",
        "outputId": "34bb4212-b26a-4d60-e27d-ec32f2de1e28",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2409, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "#computing the shape\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "964963c5",
      "metadata": {
        "id": "964963c5"
      },
      "source": [
        "# Data preprocessing\n",
        "- Dropping empty rows/columns\n",
        "- Checking for duplicates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "a0247e2b",
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        "id": "a0247e2b",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      roll_no           student_name branch/course  sdur_C1   pref_C1  \\\n",
              "0     1905858    RUDRA PRATAP BEHERA           CSE       12  hardcopy   \n",
              "1     1904043          SATYAM PANDEY           ETC        3  hardcopy   \n",
              "2     1904081       HARSHEETA KUMARI           ETC        5     eBook   \n",
              "3     9999999  Mai Kyu Batau ???????           ETC       69     eBook   \n",
              "4     1904287        SHIVANI  KUMARI           ETC        2     eBook   \n",
              "...       ...                    ...           ...      ...       ...   \n",
              "2404  2170214    Shakti Prasad Panda           MCA       39  hardcopy   \n",
              "2405  2170225        Mrunmayee Nayak           MCA       44  hardcopy   \n",
              "2406  2170228       Divya Tejaswinee           MCA       37  hardcopy   \n",
              "2407  2170240         Aurobindo Rout           MCA       67     eBook   \n",
              "2408  2170241        Madhusmita Dash           MCA       16     eBook   \n",
              "\n",
              "      sdur_C2   pref_C2  sdur_C3   pref_C3  sdur_DE1  pref_DE1  sdur_DE2  \\\n",
              "0           3  hardcopy        5  hardcopy        10  hardcopy         6   \n",
              "1           3  hardcopy        3  hardcopy         3  hardcopy         3   \n",
              "2           1     eBook        2     eBook         5  hardcopy         8   \n",
              "3          69     eBook       69  hardcopy        69  hardcopy        69   \n",
              "4           2     eBook        2     eBook         2     eBook         2   \n",
              "...       ...       ...      ...       ...       ...       ...       ...   \n",
              "2404       18     eBook       36  hardcopy        49  hardcopy        18   \n",
              "2405        2     eBook       20     eBook        36  hardcopy        32   \n",
              "2406       38  hardcopy       62  hardcopy        20     eBook        38   \n",
              "2407       42  hardcopy       17     eBook        62  hardcopy        13   \n",
              "2408       10     eBook       18     eBook        17     eBook        49   \n",
              "\n",
              "      pref_DE2  sdur_DE3  pref_DE3  sdur_OE1  pref_OE1  \n",
              "0     hardcopy         2  hardcopy         9     eBook  \n",
              "1     hardcopy         3  hardcopy         3  hardcopy  \n",
              "2        eBook         8     eBook         8     eBook  \n",
              "3     hardcopy        69     eBook        69  hardcopy  \n",
              "4        eBook         2     eBook         2     eBook  \n",
              "...        ...       ...       ...       ...       ...  \n",
              "2404     eBook         7     eBook         7     eBook  \n",
              "2405  hardcopy        13     eBook        13     eBook  \n",
              "2406  hardcopy        45  hardcopy        45  hardcopy  \n",
              "2407     eBook        50  hardcopy        50  hardcopy  \n",
              "2408  hardcopy         9     eBook         9     eBook  \n",
              "\n",
              "[2409 rows x 17 columns]"
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              "      <th></th>\n",
              "      <th>roll_no</th>\n",
              "      <th>student_name</th>\n",
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              "      <th>sdur_C1</th>\n",
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              "      <th>pref_OE1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1905858</td>\n",
              "      <td>RUDRA PRATAP BEHERA</td>\n",
              "      <td>CSE</td>\n",
              "      <td>12</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>5</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>10</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>6</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>2</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>9</td>\n",
              "      <td>eBook</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1904043</td>\n",
              "      <td>SATYAM PANDEY</td>\n",
              "      <td>ETC</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>3</td>\n",
              "      <td>hardcopy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1904081</td>\n",
              "      <td>HARSHEETA KUMARI</td>\n",
              "      <td>ETC</td>\n",
              "      <td>5</td>\n",
              "      <td>eBook</td>\n",
              "      <td>1</td>\n",
              "      <td>eBook</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>5</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>8</td>\n",
              "      <td>eBook</td>\n",
              "      <td>8</td>\n",
              "      <td>eBook</td>\n",
              "      <td>8</td>\n",
              "      <td>eBook</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9999999</td>\n",
              "      <td>Mai Kyu Batau ???????</td>\n",
              "      <td>ETC</td>\n",
              "      <td>69</td>\n",
              "      <td>eBook</td>\n",
              "      <td>69</td>\n",
              "      <td>eBook</td>\n",
              "      <td>69</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>69</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>69</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>69</td>\n",
              "      <td>eBook</td>\n",
              "      <td>69</td>\n",
              "      <td>hardcopy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1904287</td>\n",
              "      <td>SHIVANI  KUMARI</td>\n",
              "      <td>ETC</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2404</th>\n",
              "      <td>2170214</td>\n",
              "      <td>Shakti Prasad Panda</td>\n",
              "      <td>MCA</td>\n",
              "      <td>39</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>18</td>\n",
              "      <td>eBook</td>\n",
              "      <td>36</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>49</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>18</td>\n",
              "      <td>eBook</td>\n",
              "      <td>7</td>\n",
              "      <td>eBook</td>\n",
              "      <td>7</td>\n",
              "      <td>eBook</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2405</th>\n",
              "      <td>2170225</td>\n",
              "      <td>Mrunmayee Nayak</td>\n",
              "      <td>MCA</td>\n",
              "      <td>44</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>2</td>\n",
              "      <td>eBook</td>\n",
              "      <td>20</td>\n",
              "      <td>eBook</td>\n",
              "      <td>36</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>32</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>13</td>\n",
              "      <td>eBook</td>\n",
              "      <td>13</td>\n",
              "      <td>eBook</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2406</th>\n",
              "      <td>2170228</td>\n",
              "      <td>Divya Tejaswinee</td>\n",
              "      <td>MCA</td>\n",
              "      <td>37</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>38</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>62</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>20</td>\n",
              "      <td>eBook</td>\n",
              "      <td>38</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>45</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>45</td>\n",
              "      <td>hardcopy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2407</th>\n",
              "      <td>2170240</td>\n",
              "      <td>Aurobindo Rout</td>\n",
              "      <td>MCA</td>\n",
              "      <td>67</td>\n",
              "      <td>eBook</td>\n",
              "      <td>42</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>17</td>\n",
              "      <td>eBook</td>\n",
              "      <td>62</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>13</td>\n",
              "      <td>eBook</td>\n",
              "      <td>50</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>50</td>\n",
              "      <td>hardcopy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2408</th>\n",
              "      <td>2170241</td>\n",
              "      <td>Madhusmita Dash</td>\n",
              "      <td>MCA</td>\n",
              "      <td>16</td>\n",
              "      <td>eBook</td>\n",
              "      <td>10</td>\n",
              "      <td>eBook</td>\n",
              "      <td>18</td>\n",
              "      <td>eBook</td>\n",
              "      <td>17</td>\n",
              "      <td>eBook</td>\n",
              "      <td>49</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>9</td>\n",
              "      <td>eBook</td>\n",
              "      <td>9</td>\n",
              "      <td>eBook</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2409 rows × 17 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d8dc0cd-4e13-4260-9608-fbadc90b6480')\"\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "#dropping first column of dataframe (NaN)\n",
        "df = df.iloc[: , 1:]\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "fd9059ff",
      "metadata": {
        "id": "fd9059ff",
        "outputId": "682acab1-4975-49a1-c021-42b194cc67c7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2409, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "#shape updates\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "bd4b5599",
      "metadata": {
        "id": "bd4b5599"
      },
      "outputs": [],
      "source": [
        "#dropping empty 'rows'\n",
        "#df = df.dropna(axis = 0)\n",
        "#df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "4902fb7d",
      "metadata": {
        "id": "4902fb7d"
      },
      "outputs": [],
      "source": [
        "#shape updates\n",
        "#df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "8d4a902f",
      "metadata": {
        "id": "8d4a902f",
        "outputId": "cb1054f7-8cb8-47e6-8bf9-c947824368ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2409 entries, 0 to 2408\n",
            "Data columns (total 17 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   roll_no        2409 non-null   int64 \n",
            " 1   student_name   2409 non-null   object\n",
            " 2   branch/course  2409 non-null   object\n",
            " 3   sdur_C1        2409 non-null   int64 \n",
            " 4   pref_C1        2409 non-null   object\n",
            " 5   sdur_C2        2409 non-null   int64 \n",
            " 6   pref_C2        2409 non-null   object\n",
            " 7   sdur_C3        2409 non-null   int64 \n",
            " 8   pref_C3        2409 non-null   object\n",
            " 9   sdur_DE1       2409 non-null   int64 \n",
            " 10  pref_DE1       2409 non-null   object\n",
            " 11  sdur_DE2       2409 non-null   int64 \n",
            " 12  pref_DE2       2409 non-null   object\n",
            " 13  sdur_DE3       2409 non-null   int64 \n",
            " 14  pref_DE3       2409 non-null   object\n",
            " 15  sdur_OE1       2409 non-null   int64 \n",
            " 16  pref_OE1       2409 non-null   object\n",
            "dtypes: int64(8), object(9)\n",
            "memory usage: 320.1+ KB\n"
          ]
        }
      ],
      "source": [
        "#information in dataframe\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "36df4df7",
      "metadata": {
        "id": "36df4df7",
        "outputId": "dd6231e5-fadf-4fb2-e30b-f6fab122dec2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      roll_no           student_name branch/course  sdur_C1   pref_C1  \\\n",
              "0     1905858    RUDRA PRATAP BEHERA           CSE       12  hardcopy   \n",
              "1     1904043          SATYAM PANDEY           ETC        3  hardcopy   \n",
              "2     1904081       HARSHEETA KUMARI           ETC        5     eBook   \n",
              "3     9999999  Mai Kyu Batau ???????           ETC       69     eBook   \n",
              "4     1904287        SHIVANI  KUMARI           ETC        2     eBook   \n",
              "...       ...                    ...           ...      ...       ...   \n",
              "2404  2170214    Shakti Prasad Panda           MCA       39  hardcopy   \n",
              "2405  2170225        Mrunmayee Nayak           MCA       44  hardcopy   \n",
              "2406  2170228       Divya Tejaswinee           MCA       37  hardcopy   \n",
              "2407  2170240         Aurobindo Rout           MCA       67     eBook   \n",
              "2408  2170241        Madhusmita Dash           MCA       16     eBook   \n",
              "\n",
              "      sdur_C2   pref_C2  sdur_C3   pref_C3  sdur_DE1  pref_DE1  sdur_DE2  \\\n",
              "0           3  hardcopy        5  hardcopy        10  hardcopy         6   \n",
              "1           3  hardcopy        3  hardcopy         3  hardcopy         3   \n",
              "2           1     eBook        2     eBook         5  hardcopy         8   \n",
              "3          69     eBook       69  hardcopy        69  hardcopy        69   \n",
              "4           2     eBook        2     eBook         2     eBook         2   \n",
              "...       ...       ...      ...       ...       ...       ...       ...   \n",
              "2404       18     eBook       36  hardcopy        49  hardcopy        18   \n",
              "2405        2     eBook       20     eBook        36  hardcopy        32   \n",
              "2406       38  hardcopy       62  hardcopy        20     eBook        38   \n",
              "2407       42  hardcopy       17     eBook        62  hardcopy        13   \n",
              "2408       10     eBook       18     eBook        17     eBook        49   \n",
              "\n",
              "      pref_DE2  sdur_DE3  pref_DE3  sdur_OE1  pref_OE1  \n",
              "0     hardcopy         2  hardcopy         9     eBook  \n",
              "1     hardcopy         3  hardcopy         3  hardcopy  \n",
              "2        eBook         8     eBook         8     eBook  \n",
              "3     hardcopy        69     eBook        69  hardcopy  \n",
              "4        eBook         2     eBook         2     eBook  \n",
              "...        ...       ...       ...       ...       ...  \n",
              "2404     eBook         7     eBook         7     eBook  \n",
              "2405  hardcopy        13     eBook        13     eBook  \n",
              "2406  hardcopy        45  hardcopy        45  hardcopy  \n",
              "2407     eBook        50  hardcopy        50  hardcopy  \n",
              "2408  hardcopy         9     eBook         9     eBook  \n",
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              "      <th>0</th>\n",
              "      <td>1905858</td>\n",
              "      <td>RUDRA PRATAP BEHERA</td>\n",
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              "      <th>2</th>\n",
              "      <td>1904081</td>\n",
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              "      <td>ETC</td>\n",
              "      <td>5</td>\n",
              "      <td>eBook</td>\n",
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              "      <td>9999999</td>\n",
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              "      <td>ETC</td>\n",
              "      <td>69</td>\n",
              "      <td>eBook</td>\n",
              "      <td>69</td>\n",
              "      <td>eBook</td>\n",
              "      <td>69</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>69</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>69</td>\n",
              "      <td>hardcopy</td>\n",
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              "      <td>eBook</td>\n",
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              "      <td>hardcopy</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1904287</td>\n",
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              "      <td>eBook</td>\n",
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              "      <td>eBook</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>39</td>\n",
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              "      <td>eBook</td>\n",
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              "      <td>eBook</td>\n",
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              "      <td>eBook</td>\n",
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              "      <th>2406</th>\n",
              "      <td>2170228</td>\n",
              "      <td>Divya Tejaswinee</td>\n",
              "      <td>MCA</td>\n",
              "      <td>37</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>38</td>\n",
              "      <td>hardcopy</td>\n",
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              "      <td>hardcopy</td>\n",
              "      <td>45</td>\n",
              "      <td>hardcopy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2407</th>\n",
              "      <td>2170240</td>\n",
              "      <td>Aurobindo Rout</td>\n",
              "      <td>MCA</td>\n",
              "      <td>67</td>\n",
              "      <td>eBook</td>\n",
              "      <td>42</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>17</td>\n",
              "      <td>eBook</td>\n",
              "      <td>62</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>13</td>\n",
              "      <td>eBook</td>\n",
              "      <td>50</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>50</td>\n",
              "      <td>hardcopy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2408</th>\n",
              "      <td>2170241</td>\n",
              "      <td>Madhusmita Dash</td>\n",
              "      <td>MCA</td>\n",
              "      <td>16</td>\n",
              "      <td>eBook</td>\n",
              "      <td>10</td>\n",
              "      <td>eBook</td>\n",
              "      <td>18</td>\n",
              "      <td>eBook</td>\n",
              "      <td>17</td>\n",
              "      <td>eBook</td>\n",
              "      <td>49</td>\n",
              "      <td>hardcopy</td>\n",
              "      <td>9</td>\n",
              "      <td>eBook</td>\n",
              "      <td>9</td>\n",
              "      <td>eBook</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1717 rows × 17 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c031767-3dd9-437a-a2e0-b0c5846a5452')\"\n",
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              "\n",
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              "                                                     [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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "#deleting multiple entries (and considering just first entry) of same student w.r.t his/her roll number\n",
        "df = df.drop_duplicates(subset='roll_no', keep=\"first\")\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1a193036",
      "metadata": {
        "id": "1a193036",
        "outputId": "bc65947c-ae6a-493c-e08a-03a878a88952"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1717, 17)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#shape updates\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9a32e613",
      "metadata": {
        "id": "9a32e613"
      },
      "source": [
        "# Binary encoding the preferences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "9580b752",
      "metadata": {
        "id": "9580b752",
        "outputId": "20b41c74-87c0-4731-8310-97d611cfe1d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      roll_no           student_name branch/course  sdur_C1  pref_C1  sdur_C2  \\\n",
              "0     1905858    RUDRA PRATAP BEHERA           CSE       12        1        3   \n",
              "1     1904043          SATYAM PANDEY           ETC        3        1        3   \n",
              "2     1904081       HARSHEETA KUMARI           ETC        5        0        1   \n",
              "3     9999999  Mai Kyu Batau ???????           ETC       69        0       69   \n",
              "4     1904287        SHIVANI  KUMARI           ETC        2        0        2   \n",
              "...       ...                    ...           ...      ...      ...      ...   \n",
              "2404  2170214    Shakti Prasad Panda           MCA       39        1       18   \n",
              "2405  2170225        Mrunmayee Nayak           MCA       44        1        2   \n",
              "2406  2170228       Divya Tejaswinee           MCA       37        1       38   \n",
              "2407  2170240         Aurobindo Rout           MCA       67        0       42   \n",
              "2408  2170241        Madhusmita Dash           MCA       16        0       10   \n",
              "\n",
              "      pref_C2  sdur_C3  pref_C3  sdur_DE1  pref_DE1  sdur_DE2  pref_DE2  \\\n",
              "0           1        5        1        10         1         6         1   \n",
              "1           1        3        1         3         1         3         1   \n",
              "2           0        2        0         5         1         8         0   \n",
              "3           0       69        1        69         1        69         1   \n",
              "4           0        2        0         2         0         2         0   \n",
              "...       ...      ...      ...       ...       ...       ...       ...   \n",
              "2404        0       36        1        49         1        18         0   \n",
              "2405        0       20        0        36         1        32         1   \n",
              "2406        1       62        1        20         0        38         1   \n",
              "2407        1       17        0        62         1        13         0   \n",
              "2408        0       18        0        17         0        49         1   \n",
              "\n",
              "      sdur_DE3  pref_DE3  sdur_OE1  pref_OE1  \n",
              "0            2         1         9         0  \n",
              "1            3         1         3         1  \n",
              "2            8         0         8         0  \n",
              "3           69         0        69         1  \n",
              "4            2         0         2         0  \n",
              "...        ...       ...       ...       ...  \n",
              "2404         7         0         7         0  \n",
              "2405        13         0        13         0  \n",
              "2406        45         1        45         1  \n",
              "2407        50         1        50         1  \n",
              "2408         9         0         9         0  \n",
              "\n",
              "[1717 rows x 17 columns]"
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              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1904081</td>\n",
              "      <td>HARSHEETA KUMARI</td>\n",
              "      <td>ETC</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9999999</td>\n",
              "      <td>Mai Kyu Batau ???????</td>\n",
              "      <td>ETC</td>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "      <td>69</td>\n",
              "      <td>1</td>\n",
              "      <td>69</td>\n",
              "      <td>1</td>\n",
              "      <td>69</td>\n",
              "      <td>1</td>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "      <td>69</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1904287</td>\n",
              "      <td>SHIVANI  KUMARI</td>\n",
              "      <td>ETC</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2404</th>\n",
              "      <td>2170214</td>\n",
              "      <td>Shakti Prasad Panda</td>\n",
              "      <td>MCA</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>49</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2405</th>\n",
              "      <td>2170225</td>\n",
              "      <td>Mrunmayee Nayak</td>\n",
              "      <td>MCA</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2406</th>\n",
              "      <td>2170228</td>\n",
              "      <td>Divya Tejaswinee</td>\n",
              "      <td>MCA</td>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>62</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2407</th>\n",
              "      <td>2170240</td>\n",
              "      <td>Aurobindo Rout</td>\n",
              "      <td>MCA</td>\n",
              "      <td>67</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>62</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2408</th>\n",
              "      <td>2170241</td>\n",
              "      <td>Madhusmita Dash</td>\n",
              "      <td>MCA</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>49</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1717 rows × 17 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b88764d5-3330-43af-a3b6-805ac58b4e32')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "#importing library to ignore warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "#converting preferences in binary (1 for Hardcopy, 0 for eBook)\n",
        "df['pref_C1'] = df['pref_C1'].map(\n",
        "                   {'hardcopy':1 ,'eBook':0})\n",
        "\n",
        "df['pref_C2'] = df['pref_C2'].map(\n",
        "                   {'hardcopy':1 ,'eBook':0})\n",
        "\n",
        "df['pref_C3'] = df['pref_C3'].map(\n",
        "                   {'hardcopy':1 ,'eBook':0})\n",
        "\n",
        "df['pref_DE1'] = df['pref_DE1'].map(\n",
        "                   {'hardcopy':1 ,'eBook':0})\n",
        "\n",
        "df['pref_DE2'] = df['pref_DE2'].map(\n",
        "                   {'hardcopy':1 ,'eBook':0})\n",
        "\n",
        "df['pref_DE3'] = df['pref_DE3'].map(\n",
        "                   {'hardcopy':1 ,'eBook':0})\n",
        "\n",
        "df['pref_OE1'] = df['pref_OE1'].map(\n",
        "                   {'hardcopy':1 ,'eBook':0})\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "fe733d87",
      "metadata": {
        "id": "fe733d87",
        "outputId": "85d5c91c-e922-42f6-c128-fee5c467c2c4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1717 entries, 0 to 2408\n",
            "Data columns (total 17 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   roll_no        1717 non-null   int64 \n",
            " 1   student_name   1717 non-null   object\n",
            " 2   branch/course  1717 non-null   object\n",
            " 3   sdur_C1        1717 non-null   int64 \n",
            " 4   pref_C1        1717 non-null   int64 \n",
            " 5   sdur_C2        1717 non-null   int64 \n",
            " 6   pref_C2        1717 non-null   int64 \n",
            " 7   sdur_C3        1717 non-null   int64 \n",
            " 8   pref_C3        1717 non-null   int64 \n",
            " 9   sdur_DE1       1717 non-null   int64 \n",
            " 10  pref_DE1       1717 non-null   int64 \n",
            " 11  sdur_DE2       1717 non-null   int64 \n",
            " 12  pref_DE2       1717 non-null   int64 \n",
            " 13  sdur_DE3       1717 non-null   int64 \n",
            " 14  pref_DE3       1717 non-null   int64 \n",
            " 15  sdur_OE1       1717 non-null   int64 \n",
            " 16  pref_OE1       1717 non-null   int64 \n",
            "dtypes: int64(15), object(2)\n",
            "memory usage: 241.5+ KB\n"
          ]
        }
      ],
      "source": [
        "#visializing information in dataframe\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bbb1d344",
      "metadata": {
        "id": "bbb1d344"
      },
      "source": [
        "# Sorting the dataset in terms of roll number"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "78bca205",
      "metadata": {
        "id": "78bca205",
        "outputId": "104f6a5d-b4a3-48a0-f290-7712515592e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       roll_no          student_name branch/course  sdur_C1  pref_C1  sdur_C2  \\\n",
              "1130   1901032         Navasree  Das         CIVIL       62        1       46   \n",
              "1241   1901036         Pratik  Sinha         CIVIL       36        1       36   \n",
              "1289   1901052  Shubham Kumar Mishra         CIVIL       44        1       44   \n",
              "1294   1901068   Shashi Ranjan Singh         CIVIL       47        1       27   \n",
              "1264   1902003   Aditya  Priyadarshi    Mechanical        9        0       17   \n",
              "...        ...                   ...           ...      ...      ...      ...   \n",
              "1932  20053008      Ratrishree Nandi        B.Tech       52        1       41   \n",
              "555   20053011     SUBHASINI  MONDAL           CSE       36        1       19   \n",
              "1934  20053012        Sukanya Kumari        B.Tech       17        0       66   \n",
              "1935  20053015  Ankit Kumar Banerjee        B.Tech        8        0       12   \n",
              "1936  20053016          Rajesh Kalia        B.Tech       60        1       10   \n",
              "\n",
              "      pref_C2  sdur_C3  pref_C3  sdur_DE1  pref_DE1  sdur_DE2  pref_DE2  \\\n",
              "1130        1       10        0        47         1        43         1   \n",
              "1241        1       36        1         2         0        58         1   \n",
              "1289        1       12        0         8         0        39         1   \n",
              "1294        1        8        0         2         0        35         1   \n",
              "1264        0       20        0        39         1        17         0   \n",
              "...       ...      ...      ...       ...       ...       ...       ...   \n",
              "1932        1       32        1        18         0        15         0   \n",
              "555         0       36        1        15         0        47         1   \n",
              "1934        1       13        0        38         1        52         1   \n",
              "1935        0       49        1        13         0        45         1   \n",
              "1936        0        5        0        49         1        47         1   \n",
              "\n",
              "      sdur_DE3  pref_DE3  sdur_OE1  pref_OE1  \n",
              "1130        43         1        44         1  \n",
              "1241        58         1        36         1  \n",
              "1289        39         1        60         1  \n",
              "1294        35         1        38         1  \n",
              "1264        17         0        31         1  \n",
              "...        ...       ...       ...       ...  \n",
              "1932        39         1         6         0  \n",
              "555         47         1         6         0  \n",
              "1934        15         0        46         1  \n",
              "1935        39         1        17         0  \n",
              "1936        36         1        15         0  \n",
              "\n",
              "[1717 rows x 17 columns]"
            ],
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              "\n",
              "  <div id=\"df-0bcd9beb-2a92-4e0e-b0b2-47b611479a47\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>roll_no</th>\n",
              "      <th>student_name</th>\n",
              "      <th>branch/course</th>\n",
              "      <th>sdur_C1</th>\n",
              "      <th>pref_C1</th>\n",
              "      <th>sdur_C2</th>\n",
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              "      <th>pref_OE1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1130</th>\n",
              "      <td>1901032</td>\n",
              "      <td>Navasree  Das</td>\n",
              "      <td>CIVIL</td>\n",
              "      <td>62</td>\n",
              "      <td>1</td>\n",
              "      <td>46</td>\n",
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              "      <td>1</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1241</th>\n",
              "      <td>1901036</td>\n",
              "      <td>Pratik  Sinha</td>\n",
              "      <td>CIVIL</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>58</td>\n",
              "      <td>1</td>\n",
              "      <td>58</td>\n",
              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1289</th>\n",
              "      <td>1901052</td>\n",
              "      <td>Shubham Kumar Mishra</td>\n",
              "      <td>CIVIL</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1294</th>\n",
              "      <td>1901068</td>\n",
              "      <td>Shashi Ranjan Singh</td>\n",
              "      <td>CIVIL</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1264</th>\n",
              "      <td>1902003</td>\n",
              "      <td>Aditya  Priyadarshi</td>\n",
              "      <td>Mechanical</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1932</th>\n",
              "      <td>20053008</td>\n",
              "      <td>Ratrishree Nandi</td>\n",
              "      <td>B.Tech</td>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>555</th>\n",
              "      <td>20053011</td>\n",
              "      <td>SUBHASINI  MONDAL</td>\n",
              "      <td>CSE</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1934</th>\n",
              "      <td>20053012</td>\n",
              "      <td>Sukanya Kumari</td>\n",
              "      <td>B.Tech</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>66</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1935</th>\n",
              "      <td>20053015</td>\n",
              "      <td>Ankit Kumar Banerjee</td>\n",
              "      <td>B.Tech</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>49</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1936</th>\n",
              "      <td>20053016</td>\n",
              "      <td>Rajesh Kalia</td>\n",
              "      <td>B.Tech</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>49</td>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1717 rows × 17 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0bcd9beb-2a92-4e0e-b0b2-47b611479a47')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0bcd9beb-2a92-4e0e-b0b2-47b611479a47 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-0bcd9beb-2a92-4e0e-b0b2-47b611479a47');\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",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "#sorting the dataframe w.r.t student roll numbers\n",
        "df = df.sort_values(by ='roll_no')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "e3f08627",
      "metadata": {
        "id": "e3f08627",
        "outputId": "4e855049-34b8-4b41-db5b-c149f580cdbf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1717, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "#shape updates\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f2886bfb",
      "metadata": {
        "id": "f2886bfb"
      },
      "source": [
        "# Typecasting the entries into appropriate datatypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "3820297a",
      "metadata": {
        "id": "3820297a"
      },
      "outputs": [],
      "source": [
        "#converting roll numbers to int from float\n",
        "#df[\"roll_no\"] = df[\"roll_no\"].astype(float).astype(int)\n",
        "#df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e708aa6c",
      "metadata": {
        "id": "e708aa6c"
      },
      "source": [
        "# Performing price and mass related computations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "04e9f0d3",
      "metadata": {
        "id": "04e9f0d3"
      },
      "outputs": [],
      "source": [
        "#material (hardcopy) prices in Rupees\n",
        "\n",
        "#Wireless communications - Theodore S Rapport\n",
        "hc_price_CC = 829.00\n",
        "\n",
        "#Probability and Statistics for Engineering and the Sciences - Jay L Devore\n",
        "hc_price_IFS = 725.00\n",
        "\n",
        "#CMOS VLSI design - Neil H. E. Weste and David Money Harris\n",
        "hc_price_VLSI = 929.00\n",
        "\n",
        "#Principles of Soft Computing - S. N. Sivanandan and S. N. Deepa\n",
        "hc_price_DE1 = 710.00\n",
        "\n",
        "#Information Theory, Coding and Cryptography - Ranjan Bose\n",
        "hc_price_DE2 = 495.00\n",
        "\n",
        "#Digital Image Processing - S Jayaraman, S Esakkirajan, T Veerakumar\n",
        "hc_price_DE3 = 738.00\n",
        "\n",
        "#Quantum Computing Explained - David McMohan\n",
        "hc_price_OE1 = 6500.00"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "e2077b40",
      "metadata": {
        "id": "e2077b40",
        "outputId": "d1134dc6-2a2e-409d-c1d4-af34bb598abd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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oqmVJ3gmc17Z7R1UtG2HckiRJvTCyRK2qrgJ2nqD858BeE5QXcPgkxzoOOG5NxyhJktRnvplAkiSpp0zUJEmSemroRK0NDpg3ymAkSZJ0r0kTtSTrJPnzJF9OchPwI+CGJJcmeV+Sh05fmJIkSXPPVC1qZwPbA28F/qiqFlbVZsCfAOcC703ykmmIUZIkaU6aatTn06rqt+ML26Mxvgh8Mcm6I4tMkiRpjpu0RW2iJG1VtpEkSdKqWaVRn0nOWNOBSJIkaXmr+niOV67RKCRJkvQHVipRa+/b3Km9g1OSJEkjtMJELck5SR6YZBPgAuCTSY4afWiSJElz2zAtahtW1e3Ac4ATq2p34GmjDUuSJEnDJGrzk2wBvABwEIEkSdI0GSZRewdwFrC0qs5L8hDgitGGJUmSpKkeeAtAVf0r8K8Dy1cledlIo5IkSdLULWpJtkyyKMl92vJmSf4BW9QkSZJGbqqXsr8euBD4MHBukr8ALgPWBx43PeFJkiTNXVN1fR4GPLyqliXZGvhv4IlVdf70hCZJkjS3TdX1+ev2Anaq6ifA5SZpkiRJ02eqFrWtknxoYHmLweWqeu3owpIkSdJUidrfjFu2NU2SJGkaTZqoVdUJ0xmIJEmSlrdSL2WXJEnS9DFRkyRJ6ikTNUmSpJ5a4SukkiwAXglsO7h9Vb1idGFJkiRphYkacBrwn8DXgXtGG44kSZLGDJOo3a+q3jzySCRJkrScYe5ROyPJ/iOPRJIkScsZJlF7HV2y9qsktye5I8ntow5MkiRprlth12dVbTAdgUiSJGl5k7aoJXlE+7nrRNOwJ0gyL8n3k5zRlrdL8t0kS5N8Psl9Wvl92/LStn7bgWO8tZVfnmSfVb1YSZKk2WSqFrX/DRwGvH+CdQU8dchzvA64DHhgW34vcHRVnZzk48ChwMfaz1uq6qFJXtS2e2GSHYEXAY8CHgx8PcnDqsoRqJIkaa02aYtaVR3Wfj5lgmmoJC3JVsAzgE+15dAleF9om5wAHNjmD2jLtPV7te0PAE6uqruq6n+ApcBuK3ORkiRJs9FUXZ9/MtWOSR6Y5NErOP4HgDcBv2vLmwK3VtXdbflaYMs2vyVwDUBbf1vb/vflE+wjSZK01pqq6/O5Sf4J+CpwPnAzsB7wUOApwDbAGyfbOckzgZuq6vwke66xiCc/32F0XbVsvfXWoz6dJEnSyE2aqFXVG5JsAjwXeD6wBfAruvvNPlFV31rBsZ8IPLs9g209unvUPghslGR+azXbCriubX8dsBC4Nsl8YEPg5wPlYwb3GYz3GOAYgEWLFtUKYpMkSeq9KZ+jVlXLquqTVXVIVe1TVQdW1VuHSNJo221VVdvSDQb4ZlUdBJwNPK9tdjDdK6oATm/LtPXfrKpq5S9qo0K3A3YAvreS1ylJkjTrDPMKqTXtzcDJSd4FfB84tpUfC3wmyVJgGV1yR1VdkuQU4FLgbuBwR3xKkqS5YFoStao6BzinzV/FBKM2q+rXdF2sE+3/buDdo4tQkiSpf4Z5hZQkSZJmwAoTtST3S/J/knyyLe/QRnRKkiRphIZpUTseuAt4Qlu+DnjXyCKSJEkSMFyitn1V/RPwW4CquhPISKOSJEnSUInab5KsT/d+T5JsT9fCJkmSpBEaZtTnEXRvJ1iY5CS6B9keMsqgJEmSNESiVlWLk1wA7EHX5fm6qvrZyCOTJEma44YZ9flnwN1V9eWqOgO4O8mBow9NkiRpbhvmHrUjquq2sYWqupWuO1SSJEkjNEyiNtE2M/HqKUmSpDllmERtSZKjkmzfpqOA80cdmCRJ0lw3TKL2GuA3wOfbdBdw+CiDkiRJ0nCjPn8JvGUaYpEkSdKASRO1JB+oqtcn+Tfaw24HVdWzRxqZJEnSHDdVi9pn2s//Ox2BSJIkaXmTJmpVdX6SecBhVXXQNMYkSZIkVjCYoKruAbZJcp9pikeSJEnNMM9Duwr4dpLTgV+OFVbVUSOLSpIkSUMlale2aR1gg9GGI0mSpDFTJmpJdgEuAS6pqsumJyRJkiTBFPeoJfl74BTgucCXk7xy2qKSJEnSlC1qLwR2qao7k2wKfBX45PSEJUmSpKlGfd5VVXcCVNXPV7CtJEmS1rCpWtQe0kZ6AgTYfmDZNxNIkiSN2FSJ2gHjln1DgSRJ0jSa6s0E/z6dgUiSJGl53ncmSZLUUyZqkiRJPbXCRC3JY6YjEEmSJC1vmBa1jyb5XpK/SrLhyCOSJEkSMESiVlVPAg4CFgLnJ/lskqePPDJJkqQ5bqh71KrqCuBtwJuBJwMfSvKjJM+ZbJ8k67WWuB8kuSTJka18uyTfTbI0yeeT3KeV37ctL23rtx041ltb+eVJ9ln1y5UkSZo9hrlHbackRwOXAU8FnlVVj2zzR0+x613AU6tqZ2AXYN8kewDvBY6uqocCtwCHtu0PBW5p5Ue37UiyI/Ai4FHAvnRdsfNW+kolSZJmmWFa1D4MfB/YuaoOr6oLAKrqerpWtglV5xdtcd02FV2C94VWfgJwYJs/oC3T1u+VJK385Kq6q6r+B1gK7Dbk9UmSJM1aw9yj9mTgZGCHJI8Z66ps6z4z1b5J5iW5ELgJWAxcCdxaVXe3Ta4FtmzzWwLXtOPeDdwGbDpYPsE+g+c6LMmSJEtuvvnmFV2WJElS7w3T9bk/XYL1IeCfgaVJ9hvm4FV1T1XtAmxF1wr2iNWIdUXnOqaqFlXVogULFozqNJIkSdNmqnd9jjkKeEpVLQVIsj3wZeArw56kqm5NcjbwBGCjJPNbq9lWwHVts+voRpZem2Q+sCHw84HyMYP7SJIkrbWGuUftjrEkrbkKuGNFOyVZkGSjNr8+8HS6AQlnA89rmx0MnNbmT2/LtPXfrKpq5S9qo0K3A3YAvjdE3JIkSbPaMC1qS5KcCZxCNxjg+cB5Y4/mqKovTbLfFsAJbYTmOsApVXVGkkuBk5O8i26QwrFt+2OBzyRZCiyjG+lJVV2S5BTgUuBu4PCqumcVrlWSJGlWGSZRWw/4Kd3z0wBuBtYHnkWXuE2YqFXVRcBjJyi/iglGbVbVr+mSwImO9W7g3UPEKkmStNZYYaJWVS+fjkAkSZK0vGFGfW6V5NQkN7Xpi0m2mo7gJEmS5rJhBhMcT3dD/4Pb9G+tTJIkSSM0TKK2oKqOr6q72/RpwAeVSZIkjdgwidrPk7ykvWVgXpKX0D3fTJIkSSM0TKL2CuAFwI3ADXTPOHOAgSRJ0ogNM+rzauDZ0xCLJEmSBgwz6vOEsTcMtOWNkxw32rAkSZI0TNfnTlV169hCVd3CBA+ylSRJ0po1TKK2TpKNxxaSbMJwbzSQJEnSahgm4Xo/8J0k/wqEbjCBr3OSJEkasWEGE5yYZAnw1Fb0nKq6dLRhSZIkadJErXVxjrkR+OzguqpaNsrAJEmS5rqpWtTOB4quu3Nr4JY2vxHwE2C7kUcnSZI0h006mKCqtquqhwBfB55VVQ+qqk2BZwJfm64AJUmS5qphRn3uUVVnji1U1VeAPx5dSJIkSYLhRn1en+RtwL+05YOA60cXkiRJkmC4FrUXAwuAU4EvtfkXjzIoSZIkraBFLck84MNVddA0xSNJkqRmyha1qroH2CbJfaYpHkmSJDXD3KN2FfDtJKcDvxwrrKqjRhaVJEmShkrUrmzTOsAGow1HkiRJY4Z5hdSR0xGIJEmSlrfCRC3JAuBNwKOA9cbKq+qpk+4kSZKk1TbM4zlOAn5E98qoI4EfA+eNMCZJkiQxXKK2aVUdC/y2qv69ql4B2JomSZI0YsMMJvht+3lDkmfQvZVgk9GFJEmSJBguUXtXkg2BNwIfBh4IvGGkUUmSJGmoUZ9ntNnbgKeMNhxJkiSNmTRRS/JhoCZbX1WvHUlEkiRJAqYeTLAEOL9Nzx6YH5umlGRhkrOTXJrkkiSva+WbJFmc5Ir2c+NWniQfSrI0yUVJdh041sFt+yuSHLzqlytJkjR7TNqiVlUnjM0nef3g8pDuBt5YVRck2QA4P8li4BDgG1X1niRvAd4CvBnYD9ihTbsDHwN2T7IJcASwiK6F7/wkp1fVLSsZjyRJ0qwyzOM5YIou0El3qLqhqi5o83cAlwFbAgcAY0nfCcCBbf4A4MTqnAtslGQLYB9gcVUta8nZYmDflY1HkiRpthk2UVstSbYFHgt8F9i8qm5oq24ENm/zWwLXDOx2bSubrFySJGmtNtVggju4tyXtfkluH1sFVFU9cJgTJHkA8EXg9VV1e5Lfr6uqSrLSrXWTnOcw4DCArbfeek0cUpIkaUZN2qJWVRtU1QPbNH9gfoOVSNLWpUvSTqqqL7Xin7YuTdrPm1r5dcDCgd23amWTlY+P95iqWlRVixYsWDBMeJIkSb02sq7PdE1nxwKXVdVRA6tOB8ZGbh4MnDZQ/rI2+nMP4LbWRXoWsHeSjdsI0b1bmSRJ0lptmDcTrKonAi8FLk5yYSv7W+A9wClJDgWuBl7Q1p0J7A8sBe4EXg5QVcuSvJN7XwT/jqpaNsK4JUmSemFkiVpVfYvufraJ7DXB9gUcPsmxjgOOW3PRSZIk9d+0jPqUJEnSyjNRkyRJ6ikTNUmSpJ4yUZMkSeopEzVJkqSeMlGTJEnqKRM1SZKknjJRkyRJ6ikTNUmSpJ4yUZMkSeopEzVJkqSeMlGTJEnqKRM1SZKknjJRkyRJ6ikTNUmSpJ4yUZMkSeopEzVJkqSeMlGTJEnqKRM1SZKknjJRkyRJ6ikTNUmSpJ4yUZMkSeopEzVJkqSeMlGTJEnqKRM1SZKknjJRkyRJ6ikTNUmSpJ4yUZMkSeopEzVJkqSeMlGTJEnqKRM1SZKknhpZopbkuCQ3JfnhQNkmSRYnuaL93LiVJ8mHkixNclGSXQf2Obhtf0WSg0cVryRJUt+MskXt08C+48reAnyjqnYAvtGWAfYDdmjTYcDHoEvsgCOA3YHdgCPGkjtJkqS13cgStar6D2DZuOIDgBPa/AnAgQPlJ1bnXGCjJFsA+wCLq2pZVd0CLOYPkz9JkqS10nTfo7Z5Vd3Q5m8ENm/zWwLXDGx3bSubrFySJGmtN2ODCaqqgFpTx0tyWJIlSZbcfPPNa+qwkiRJM2a6E7Wfti5N2s+bWvl1wMKB7bZqZZOV/4GqOqaqFlXVogULFqzxwCVJkqbbdCdqpwNjIzcPBk4bKH9ZG/25B3Bb6yI9C9g7ycZtEMHerUySJGmtN39UB07yOWBP4EFJrqUbvfke4JQkhwJXAy9om58J7A8sBe4EXg5QVcuSvBM4r233jqoaP0BBmlNyZGb0/HXEGrtjQZK0AiNL1KrqxZOs2muCbQs4fJLjHAcctwZDkyRJmhV8M4EkSVJPmahJkiT11Mi6PueCmbxXyPuEJEla+9miJkmS1FMmapIkST1l16ckSSPmY3W0qkzUNCP8T0uSpBUzUZM0Z/gHgqTZxnvUJEmSespETZIkqadM1CRJknrKRE2SJKmnTNQkSZJ6ykRNkiSpp0zUJEmSesrnqEmShjKTz6HzGXSaq2xRkyRJ6ikTNUmSpJ4yUZMkSeopEzVJkqSeMlGTJEnqKRM1SZKknjJRkyRJ6ikTNUmSpJ4yUZMkSeopEzVJkqSeMlGTJEnqKRM1SZKknjJRkyRJ6ikTNUmSpJ4yUZMkSeqpWZOoJdk3yeVJliZ5y0zHI0mSNGqzIlFLMg/4CLAfsCPw4iQ7zmxUkiRJozUrEjVgN2BpVV1VVb8BTgYOmOGYJEmSRmq2JGpbAtcMLF/byiRJktZaqaqZjmGFkjwP2Leq/qItvxTYvapePbDNYcBhbfHhwOXTHujKexDws5kOYpay7laddbfqrLvVY/2tOutu1c2GutumqhZMtGL+dEeyiq4DFg4sb9XKfq+qjgGOmc6gVleSJVW1aKbjmI2su1Vn3a066271WH+rzrpbdbO97mZL1+d5wA5JtktyH+BFwOkzHJMkSdJIzYoWtaq6O8mrgbOAecBxVXXJDIclSZI0UrMiUQOoqjOBM2c6jjVsVnXV9ox1t+qsu1Vn3a0e62/VWXerblbX3awYTCBJkjQXzZZ71CRJkuYcE7VpkOSPkpyc5Mok5yc5M8nD2nRmkiuSXJDklCSbz3S8fZHkF/jwN+IAAAmWSURBVO3ntkl+leTCgek+SR6e5Jy2fFmSWd28vTqSnJ1kn3Flr0/ylSQ/nGD7PZJ8d6Du3t7KD0nyz9MUdq8kuafVxyVJfpDkjUnWaev2THLbuM/g09q645LcNFE9zxWrUndJFrbP7aVtv9fN9HXMlFWsv/WSfK9tf0mSI2f6OmZCkq2SnNa+R69M8sH2/bD2/JutKqcRTkCA7wB/OVC2M/Ak4ArgWQPlewKPnumY+zIBv2g/twV+OMH6s4ADBpYfM9Mxz2BdHQYcP67sXOBPJ6m7y4Gd2/w8YMc2fwjwzzN9PTNUh78YmN8M+DpwZFveEzhjkv3+FNh1onqeK9Oq1B2wBbBrm98A+O+xz+Fcm1ax/gI8oM2vC3wX2GOmr2Wa6y3A94CXt+V5wLHA+9amf7O2qI3eU4DfVtXHxwqq6gfADsB3qurfBsrPqarZkeH3wxZ0b6kAoKounsFYZtoXgGe0x9eQZFvgwSz/Ro9BmwE3AFTVPVV16TTEOGtU1U10ye+rk2QF2/4HsGxaApsFhq27qrqhqi5o83cAl+EbZ1am/qqqftEW123TXLvp/KnAr6vqeOj+LwPeALwCuN9kO822f7MmaqP3aOD8lSjXxLYfaL7+SCs7Gvhm6957Q5KNZjLAmVRVy+j+styvFb0IOIXJ/+M+Grg8yalJXpVkvWkIc1apqqvo/kLfrBU9aVw3yvYzGF6vrWzdtT8sHkvXKjTnDVt/SeYluRC4CVhcVXOt/h7FuO/Rqrod+AnwUNaSf7Oz5vEcmvOurKpdBguq6vgkZwH7AgcAr0qyc1XdNSMRzrzP0SVop7Wfh062YVW9I8lJwN7AnwMvpusq0OT+s6qeOdNBzFKT1l2SBwBfBF7fvmT1hyasv9aCtEv7I/XUJI+2V2Y5a8W/WVvURu8S4HErUa6VUFXXV9VxVXUAcDddS+VcdRqwV5JdgftV1ZQttlV1ZVV9DNgL2DnJptMR5GyR5CHAPXStFVoJw9ZdknXpkrSTqupL0xHbbLCyn72quhU4m+6P1rnkUsZ9jyZ5ILA1sHRGIhoBE7XR+yZw33QvjQcgyU50N87+cZJnDJT/aZK5nGislCT7tv/oSfJHwKaMewfsXNLuVzkbOI6udW1SSZ4xcP/LDnRfCreONsLZI8kC4ON0Ayvm2n0/q2XYumufv2OBy6rqqOmKr+9Wov4WjN3ukWR94OnAj6Ynyt74BnC/JC+DrisYeD/waeDOGYxrjTJRG7H2D+3PgKe1ocOXAP8I3Ag8E3hNG1Z8KfBXwM0zF+2sszfwwyQ/oBsB+jdVdeMMxzTTPkc3qngwUXt4kmsHpucDL6W7R+1C4DPAQa0bZS5bf+wRCXSj7r4GDD7yYPz9Ls8DSPI5upHdY/U8aZfzWmxV6u6JdJ/Dpw6U7z8DsffBqtTfFsDZSS6iex/24qo6Y/pDnzkD36/PT3IFXQPIr4G/bZusFf9mfTOBJElST9miJkmS1FMmapIkST1loiZJktRTJmqSJEk9ZaImSZLUUyZqklZbkr9LckmSi9ow+N1XsP3bk/z1BOUPTvKFVYzhkCQPnmTdp5PcmWSDgbIPJKkkD1rBcf92qvVT7PepJDuuYJtzkiyapHzJwPKiJOesShySZjcTNUmrJckT6J4JuGtV7QQ8jclfBj+l9qaJ561iKIfQvYh+MkvpXjVGknXoXug8zAOSVzpRSzKvqv5iNV92v1mS/Va8maS1mYmapNW1BfCzsXesVtXPqup6gCQ/HmuxmqBVaOck32kPfH5l22bbJD9s8/OSvC/Jea2l7lVjOyZ5c5KLk/wgyXvagywXASe1Fr31J4jzZOCFbX5P4Nt0rx0bO+b/S3J+axk8rJW9h3sfRnpSK3tJku+1sk+0p6GT5BdJ3t8ewPyEwdayJB9LsqQde/BBplN5H/B34wtbHf1nkgva9MetfM8k/57ktCRXtXo5qMV6ce59kfeCJF9s9Xpekie28icPPBj0+4Otj5JmjomapNX1NWBhkv9O8tEkTx5yv53oWrWeAPz9BN2WhwK3VdXjgccDr0yyXWtlOgDYvap2Bv6pqr4ALKF7w8IuVfWrCc7338CCJBvTvYT+5HHrX1FVj6NL+F6bZNOqegvwq3bMg5I8ki7Ze2JV7UL36q2D2v73B75bVTtX1bfGHfvvqmpRu+Ynp3uN3Ip8B/hNkqeMK78JeHpV7dpi+dDAup2BvwQeSffU/4dV1W7Ap4DXtG0+CBzd6vW5bR3AXwOHt+t6EjBRHUqaZvNnOgBJs1tV/SLJ4+i+3J8CfD7JW6rq0yvY9bSWUP0qydnAbsCFA+v3BnYae+0LsCHde0mfBhxfVXe28y9biXC/BLwI2B141bh1r03yZ21+YTvXz8dtsxfdS6DPS/eq1PW598XZ99C9YHwiL2itdPPpWiB3BC4aIt53AW8D3jxQti7wz0nGEsWHDaw7r6puAEhyJV0SDXAx3e8GuvrbMb9/1SsPTPIAuhbGo1rL4Zeq6toh4pM0YiZqklZbe0/oOcA5SS4GDqZ7MfLd3Ntyv9743VawHOA1VXXWcoXJPqsR6ueB84ETqup3Y8lKkj3pEpgnVNWdrYt2fLxjMZ1QVW+dYN2vJ3pfapLt6FqrHl9VtyT59CTH/gNV9c0k7wL2GCh+A/BTutazdejebTjmroH53w0s/457/79fB9ijqgb3A3hPki8D+wPfTrJPVc21l3xLvWPXp6TVkuThSXYYKNoFuLrN/5iuBQq6brZBByRZL8mmdPeMnTdu/VnA/0qybjvPw5LcH1gMvDzJ/Vr5Jm37O4Ap76uqqqvp7vv66LhVGwK3tCTtESyfGP12LAbgG8Dzkmw2du4k20x1TuCBwC+B25JsDqzsAIF3AW8aF+sNVfU7uu7NeSt5vK9xbzcorWWOJNtX1cVV9V6638UjVvK4kkbARE3S6noAcEKSS5NcRNet9/a27kjgg+keNTG+teki4GzgXOCdYwMQuLdl7VPApcAFbYDBJ4D5VfVV4HRgSZIL6VqroGvB+/gUgwm6g1d9oqquHFf8VWB+ksuA97SYxhwDXJTkpDaK823A19q1LqbrypxUVf0A+D7wI+CzdF2MQ6uqM4GbB4o+ChzcBi08gi4JXBmvBRa1ARqX0t3TBvD6JD9s1/Vb4CsreVxJI5Cq8b0NkjQz2r1uR1XVsAMSJGmtZouapF5oj7L4HN2oREkStqhJkiT1li1qkiRJPWWiJkmS1FMmapIkST1loiZJktRTJmqSJEk9ZaImSZLUU/8fAvrcbjvdDRYAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "data = {'CC':hc_price_CC,\n",
        "        'IFS':hc_price_IFS,\n",
        "        'VLSI':hc_price_VLSI,\n",
        "        'DE1':hc_price_DE1,\n",
        "        'DE2':hc_price_DE2,\n",
        "        'DE3':hc_price_DE3,\n",
        "        'OE1':hc_price_OE1}\n",
        "\n",
        "subjects = list(data.keys())\n",
        "values = list(data.values())\n",
        "  \n",
        "fig = plt.figure(figsize = (10, 5))\n",
        " \n",
        "# creating the bar plot\n",
        "plt.bar(subjects, values, color ='green',\n",
        "        width = 0.4)\n",
        " \n",
        "plt.xlabel(\"Subject Material Names\")\n",
        "plt.ylabel(\"Hardcopy Price (in Rs.)\")\n",
        "plt.title(\"Market price of materials (Hardcopy)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "cdd755ec",
      "metadata": {
        "id": "cdd755ec"
      },
      "outputs": [],
      "source": [
        "#material (eBook) prices in Rupees\n",
        "\n",
        "#Wireless communications - Theodore S Rapport\n",
        "sc_price_CC = 0.00 #source: www.researchgate.net\n",
        "\n",
        "#Probability and Statistics for Engineering and the Sciences - Jay L Devore\n",
        "sc_price_IFS = 0.00 #source: z-lib.org\n",
        "\n",
        "#CMOS VLSI design - Neil H. E. Weste and David Money Harris\n",
        "sc_price_VLSI = 404.00 #source: Amazon-Kindle Edition\n",
        "\n",
        "#Principles of Soft Computing - S. N. Sivanandan and S. N. Deepa\n",
        "sc_price_DE1 = 568.00 #Source: Amazon-Kindle Edition\n",
        "\n",
        "#Information Theory, Coding and Cryptography - Ranjan Bose\n",
        "sc_price_DE2 = 495.00 #eBook note available\n",
        "\n",
        "#Digital Image Processing - S Jayaraman, S Esakkirajan, T Veerakumar\n",
        "sc_price_DE3 = 738.00 #eBook note available\n",
        "\n",
        "#Quantum Computing Explained - David McMohan\n",
        "sc_price_OE1 = 0.00 #source: www.academia.edu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "d5d2f951",
      "metadata": {
        "id": "d5d2f951",
        "outputId": "efd6b352-a7c5-4275-80ca-f9de761355f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "data = {'CC':sc_price_CC,\n",
        "        'IFS':sc_price_IFS,\n",
        "        'VLSI':sc_price_VLSI,\n",
        "        'DE1':sc_price_DE1,\n",
        "        'DE2':sc_price_DE2,\n",
        "        'DE3':sc_price_DE3,\n",
        "        'OE1':sc_price_OE1}\n",
        "\n",
        "subjects1 = list(data.keys())\n",
        "values1 = list(data.values())\n",
        "  \n",
        "fig = plt.figure(figsize = (10, 5))\n",
        " \n",
        "# creating the bar plot\n",
        "plt.bar(subjects1, values1, color ='blue',\n",
        "        width = 0.4)\n",
        " \n",
        "plt.xlabel(\"Subject Material Names\")\n",
        "plt.ylabel(\"eBook Price (in Rs.)\")\n",
        "plt.title(\"Market price of materials (eBook)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "88f6e529",
      "metadata": {
        "id": "88f6e529"
      },
      "outputs": [],
      "source": [
        "#counting the preferences for each subject\n",
        "no_sc_CC = (df['pref_C1'] == 0).sum()\n",
        "no_hc_CC = (df['pref_C1'] == 1).sum()\n",
        "\n",
        "no_sc_IFS = (df['pref_C2'] == 0).sum()\n",
        "no_hc_IFS = (df['pref_C2'] == 1).sum()\n",
        "\n",
        "no_sc_VLSI = (df['pref_C3'] == 0).sum()\n",
        "no_hc_VLSI = (df['pref_C3'] == 1).sum()\n",
        "\n",
        "no_sc_DE1 = (df['pref_DE1'] == 0).sum()\n",
        "no_hc_DE1 = (df['pref_DE1'] == 1).sum()\n",
        "\n",
        "no_sc_DE2 = (df['pref_DE2'] == 0).sum()\n",
        "no_hc_DE2 = (df['pref_DE2'] == 1).sum()\n",
        "\n",
        "no_sc_DE3 = (df['pref_DE3'] == 0).sum()\n",
        "no_hc_DE3 = (df['pref_DE3'] == 1).sum()\n",
        "\n",
        "no_sc_OE1 = (df['pref_OE1'] == 0).sum()\n",
        "no_hc_OE1 = (df['pref_OE1'] == 1).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "87cafd5f",
      "metadata": {
        "id": "87cafd5f",
        "outputId": "2d20a951-6fe8-4b44-a107-29a2142cb96a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "X = ['CC','IFS','VLSI','DE1','DE2','DE3','OE1']\n",
        "eBook = [no_sc_CC,no_sc_IFS,no_sc_VLSI,no_sc_DE1,no_sc_DE2,no_sc_DE3,no_sc_OE1]\n",
        "Hardcopy = [no_hc_CC,no_hc_IFS,no_hc_VLSI,no_hc_DE1,no_hc_DE2,no_hc_DE3,no_hc_OE1]\n",
        "  \n",
        "X_axis = np.arange(len(X))\n",
        "  \n",
        "plt.bar(X_axis - 0.2, eBook, 0.4, label = 'eBook')\n",
        "plt.bar(X_axis + 0.2, Hardcopy, 0.4, label = 'Hardcopy')\n",
        "  \n",
        "plt.xticks(X_axis, X)\n",
        "plt.xlabel(\"Engineering (6th sem) study materials\")\n",
        "plt.ylabel(\"Quantity of materials\")\n",
        "plt.title(\"Subject wise material preferences (Hardcopy/eBook)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "5668f616",
      "metadata": {
        "id": "5668f616"
      },
      "outputs": [],
      "source": [
        "#calculation of savings\n",
        "\n",
        "saving_CC = (no_hc_CC * hc_price_CC) - (no_sc_CC * sc_price_CC)\n",
        "\n",
        "saving_IFS = (no_hc_IFS * hc_price_IFS) - (no_sc_IFS * sc_price_IFS)\n",
        "\n",
        "saving_VLSI = (no_hc_VLSI * hc_price_VLSI) - (no_sc_VLSI * sc_price_VLSI)\n",
        "\n",
        "saving_DE1 = (no_hc_DE1 * hc_price_DE1) - (no_sc_DE1 * sc_price_DE1)\n",
        "\n",
        "saving_DE2 = (no_hc_DE2 * hc_price_DE2) - (no_sc_DE2 * sc_price_DE2)\n",
        "\n",
        "saving_DE3 = (no_hc_DE3 * hc_price_DE3) - (no_sc_DE3 * sc_price_DE3)\n",
        "\n",
        "saving_OE1 = (no_hc_OE1 * hc_price_OE1) - (no_sc_OE1 * sc_price_OE1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "0734d267",
      "metadata": {
        "id": "0734d267",
        "outputId": "b6e43c3f-d511-4372-edf8-3f729c0b896c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Savings in Cellular Communication: Rs. 843922\n",
            "\n",
            "Savings in Inferential Statistics: Rs. 786625\n",
            "\n",
            "Savings in VLSI: Rs. 724644\n",
            "\n",
            "Savings in Department Elective 1: Rs. 362810\n",
            "\n",
            "Savings in Department Elective 2: Rs. 158895\n",
            "\n",
            "Savings in Department Elective 3: Rs. 245754\n",
            "\n",
            "Savings in Open Elective 1: Rs. 6747000\n"
          ]
        }
      ],
      "source": [
        "print(\"Savings in Cellular Communication: Rs.\", round(saving_CC))\n",
        "print(\"\\nSavings in Inferential Statistics: Rs.\", round(saving_IFS))\n",
        "print(\"\\nSavings in VLSI: Rs.\", round(saving_VLSI))\n",
        "print(\"\\nSavings in Department Elective 1: Rs.\", round(saving_DE1))\n",
        "print(\"\\nSavings in Department Elective 2: Rs.\", round(saving_DE2))\n",
        "print(\"\\nSavings in Department Elective 3: Rs.\", round(saving_DE3))\n",
        "print(\"\\nSavings in Open Elective 1: Rs.\", round(saving_OE1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "a7440440",
      "metadata": {
        "id": "a7440440",
        "outputId": "a276aa01-7502-455f-ef31-a6986ac98b49",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage savings (Cellular Communication): 100.0 %\n",
            "\n",
            "Percentage savings (Inferential Statistics): 100.0 %\n",
            "\n",
            "Percentage savings (VLSI): 73.31069870586046 %\n",
            "\n",
            "Percentage savings (Department Elective 1): 48.80611270296084 %\n",
            "\n",
            "Percentage savings (Department Elective 2): 31.50147203140334 %\n",
            "\n",
            "Percentage savings (Department Elective 3): 32.487804878048784 %\n",
            "\n",
            "Percentage savings (Open Elective 1): 100.0 %\n"
          ]
        }
      ],
      "source": [
        "#percentage savings per subject\n",
        "print(\"Percentage savings (Cellular Communication):\", (saving_CC/(no_hc_CC * hc_price_CC))*100,\"%\")\n",
        "print(\"\\nPercentage savings (Inferential Statistics):\", (saving_IFS/(no_hc_IFS * hc_price_IFS))*100,\"%\")\n",
        "print(\"\\nPercentage savings (VLSI):\", (saving_VLSI/(no_hc_VLSI * hc_price_VLSI))*100,\"%\")\n",
        "print(\"\\nPercentage savings (Department Elective 1):\", (saving_DE1/(no_hc_DE1 * hc_price_DE1))*100,\"%\")\n",
        "print(\"\\nPercentage savings (Department Elective 2):\", (saving_DE2/(no_hc_DE2 * hc_price_DE2))*100,\"%\")\n",
        "print(\"\\nPercentage savings (Department Elective 3):\", (saving_DE3/(no_hc_DE3 * hc_price_DE3))*100,\"%\")\n",
        "print(\"\\nPercentage savings (Open Elective 1):\", (saving_OE1/(no_hc_OE1 * hc_price_OE1))*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "656cebfe",
      "metadata": {
        "id": "656cebfe",
        "outputId": "1d1f260b-8b83-413e-dcf1-b2a544c2f0d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "data = {'CC':(saving_CC/(no_hc_CC * hc_price_CC))*100,\n",
        "        'IFS':(saving_IFS/(no_hc_IFS * hc_price_IFS))*100,\n",
        "        'VLSI':(saving_VLSI/(no_hc_VLSI * hc_price_VLSI))*100,\n",
        "        'DE1':(saving_DE1/(no_hc_DE1 * hc_price_DE1))*100,\n",
        "        'DE2':(saving_DE2/(no_hc_DE2 * hc_price_DE2))*100,\n",
        "        'DE3':(saving_DE3/(no_hc_DE3 * hc_price_DE3))*100,\n",
        "        'OE1':(saving_OE1/(no_hc_OE1 * hc_price_OE1))*100}\n",
        "\n",
        "subjects2 = list(data.keys())\n",
        "values2 = list(data.values())\n",
        "  \n",
        "fig = plt.figure(figsize = (10, 5))\n",
        " \n",
        "# creating the bar plot\n",
        "plt.bar(subjects2, values2, color ='maroon',\n",
        "        width = 0.4)\n",
        " \n",
        "plt.xlabel(\"Subject Names\")\n",
        "plt.ylabel(\"Percentage Savings (in Rs.)\")\n",
        "plt.title(\"Subject wise %age savings in study materials\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "ace6f883",
      "metadata": {
        "id": "ace6f883",
        "outputId": "eeaf786f-152a-4143-9e92-1a5ca85fd46a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total savings = 69.44372690261049 % = 69 %\n"
          ]
        }
      ],
      "source": [
        "#computing total percentage savings\n",
        "ultimate_savings_per = (((saving_CC/(no_hc_CC * hc_price_CC))*100)+\n",
        "                    ((saving_IFS/(no_hc_IFS * hc_price_IFS))*100)+\n",
        "                    ((saving_VLSI/(no_hc_VLSI * hc_price_VLSI))*100)+\n",
        "                    ((saving_DE1/(no_hc_DE1 * hc_price_DE1))*100)+\n",
        "                    ((saving_DE2/(no_hc_DE2 * hc_price_DE2))*100)+\n",
        "                    ((saving_DE3/(no_hc_DE3 * hc_price_DE3))*100)+\n",
        "                    ((saving_OE1/(no_hc_OE1 * hc_price_OE1))*100))/7\n",
        "\n",
        "print(\"Total savings =\",ultimate_savings_per,\"% =\", round(ultimate_savings_per),\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "73f561f2",
      "metadata": {
        "id": "73f561f2",
        "outputId": "f56c76eb-8268-49af-a293-b836a7ad1644",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total estimated savings: Rs. 9869650\n"
          ]
        }
      ],
      "source": [
        "#total savings for 6th semester\n",
        "total_savings = (saving_CC\n",
        "                +saving_IFS\n",
        "                +saving_VLSI\n",
        "                +saving_DE1\n",
        "                +saving_DE2\n",
        "                +saving_DE3\n",
        "                +saving_OE1)\n",
        "\n",
        "print(\"Total estimated savings: Rs.\", round(total_savings))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "dee45495",
      "metadata": {
        "id": "dee45495"
      },
      "outputs": [],
      "source": [
        "#computing the paper savings (in grams)\n",
        "#Source: Amazon.in\n",
        "\n",
        "#Wireless communications - Theodore S Rapport\n",
        "w_CC = 750\n",
        "\n",
        "#Probability and Statistics for Engineering and the Sciences - Jay L Devore\n",
        "w_IFS = 1540\n",
        "\n",
        "#CMOS VLSI design - Neil H. E. Weste and David Money Harris\n",
        "w_VLSI = 1290\n",
        "\n",
        "#Principles of Soft Computing - S. N. Sivanandan and S. N. Deepa\n",
        "w_DE1 = 1560\n",
        "\n",
        "#Information Theory, Coding and Cryptography - Ranjan Bose\n",
        "w_DE2 = 287\n",
        "\n",
        "#Digital Image Processing - S Jayaraman, S Esakkirajan, T Veerakumar\n",
        "w_DE3 = 1200\n",
        "\n",
        "#Quantum Computing Explained - David McMohan\n",
        "w_OE1 = 624"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "fe2cb14e",
      "metadata": {
        "id": "fe2cb14e",
        "outputId": "fd88fb18-cd36-4543-9f7e-86bc469d5230",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "data = {'CC':w_CC/1000,\n",
        "        'IFS':w_IFS/1000,\n",
        "        'VLSI':w_VLSI/1000,\n",
        "        'DE1':w_DE1/1000,\n",
        "        'DE2':w_DE2/1000,\n",
        "        'DE3':w_DE3/1000,\n",
        "        'OE1':w_OE1/1000}\n",
        "\n",
        "book = list(data.keys())\n",
        "weight = list(data.values())\n",
        "  \n",
        "fig = plt.figure(figsize = (10, 5))\n",
        " \n",
        "# creating the bar plot\n",
        "plt.bar(book, weight, color ='blue',\n",
        "        width = 0.4)\n",
        " \n",
        "plt.xlabel(\"Subject Material Names\")\n",
        "plt.ylabel(\"Weight (in kg)\")\n",
        "plt.title(\"Subject material names as per their respective weights per piece\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "8b7ed4c4",
      "metadata": {
        "id": "8b7ed4c4",
        "outputId": "b5ef75eb-d489-4781-eb45-9fb33db5cc6e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total weight = 7610445 gms = 7610.445 Kg = 7.6104449999999995 Tonnes\n"
          ]
        }
      ],
      "source": [
        "#computing the total weight of papers (in Online mode)\n",
        "\n",
        "w_CC_total = w_CC*no_hc_CC\n",
        "\n",
        "w_IFS_total = w_IFS*no_hc_IFS\n",
        "\n",
        "w_VLSI_total = w_VLSI*no_hc_VLSI\n",
        "\n",
        "w_DE1_total = w_DE1*no_hc_DE1\n",
        "\n",
        "w_DE2_total = w_DE2*no_hc_DE2\n",
        "\n",
        "w_DE3_total = w_DE3*no_hc_DE3\n",
        "\n",
        "w_OE1_total = w_OE1*no_hc_OE1\n",
        "\n",
        "w_total = w_CC_total+w_IFS_total+w_VLSI_total+w_DE1_total+w_DE2_total+w_DE3_total+w_OE1_total\n",
        "\n",
        "print(\"Total weight =\",w_total,\"gms =\",w_total/1000,\"Kg =\",(w_total/1000)/1000,\"Tonnes\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "61a857db",
      "metadata": {
        "id": "61a857db",
        "outputId": "a39db49d-dfee-4386-9aac-6f5b79bc2854",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "data = {'CC':w_CC_total/1000,\n",
        "        'IFS':w_IFS_total/1000,\n",
        "        'VLSI':w_VLSI_total/1000,\n",
        "        'DE1':w_DE1_total/1000,\n",
        "        'DE2':w_DE2_total/1000,\n",
        "        'DE3':w_DE3_total/1000,\n",
        "        'OE1':w_OE1_total/1000}\n",
        "\n",
        "book_t = list(data.keys())\n",
        "weight_t = list(data.values())\n",
        "  \n",
        "fig = plt.figure(figsize = (10, 5))\n",
        " \n",
        "# creating the bar plot\n",
        "plt.bar(book_t, weight_t, color ='magenta',\n",
        "        width = 0.4)\n",
        " \n",
        "plt.xlabel(\"Subject Material Names\")\n",
        "plt.ylabel(\"Weight (in kg)\")\n",
        "plt.title(\"Total subject wise weights of all the purchased materials\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "1b1947b2",
      "metadata": {
        "id": "1b1947b2",
        "outputId": "5879bcfa-3841-4928-c688-5b432795bac5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total courier charges (Online est.) = 722992.275 = 722992 Rupees\n"
          ]
        }
      ],
      "source": [
        "#courier delivery charges (in Rupees) per kg in India (Source: www.DTDC.in)\n",
        "courier_charges = 95\n",
        "\n",
        "total_charge = courier_charges*(w_total/1000)\n",
        "\n",
        "print(\"total courier charges (Online est.) =\",total_charge,\"=\",round(total_charge),\"Rupees\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "107b4543",
      "metadata": {
        "id": "107b4543",
        "outputId": "38fec8a8-714a-4af6-eddd-057ff42755de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total weight = 14284020 gms = 14284.02 Kg = 14.28402 Tonnes\n"
          ]
        }
      ],
      "source": [
        "#computing the total weight of papers (in conventional Offline mode) assuming pre-Corona times.\n",
        "\n",
        "w_CC_total1 = w_CC*no_hc_CC+w_CC*no_sc_CC\n",
        "\n",
        "w_IFS_total1 = w_IFS*no_hc_IFS+w_IFS*no_sc_IFS\n",
        "\n",
        "w_VLSI_total1 = w_VLSI*no_hc_VLSI+w_VLSI*no_hc_VLSI\n",
        "\n",
        "w_DE1_total1 = w_DE1*no_hc_DE1+w_DE1*no_hc_DE1\n",
        "\n",
        "w_DE2_total1 = w_DE2*no_hc_DE2+w_DE2*no_hc_DE2\n",
        "\n",
        "w_DE3_total1 = w_DE3*no_hc_DE3+w_DE3*no_hc_DE3\n",
        "\n",
        "w_OE1_total1 = w_OE1*no_hc_OE1+w_OE1*no_hc_OE1\n",
        "\n",
        "w_total1 = w_CC_total1+w_IFS_total1+w_VLSI_total1+w_DE1_total1+w_DE2_total1+w_DE3_total1+w_OE1_total1\n",
        "\n",
        "print(\"Total weight =\",w_total1,\"gms =\",w_total1/1000,\"Kg =\",(w_total1/1000)/1000,\"Tonnes\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "503302c7",
      "metadata": {
        "id": "503302c7",
        "outputId": "fd5418b0-2712-4f43-f1f1-128282502718",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "data = {'CC':w_CC_total1/1000,\n",
        "        'IFS':w_IFS_total1/1000,\n",
        "        'VLSI':w_VLSI_total1/1000,\n",
        "        'DE1':w_DE1_total1/1000,\n",
        "        'DE2':w_DE2_total1/1000,\n",
        "        'DE3':w_DE3_total1/1000,\n",
        "        'OE1':w_OE1_total1/1000}\n",
        "\n",
        "book_t1 = list(data.keys())\n",
        "weight_t1 = list(data.values())\n",
        "  \n",
        "fig = plt.figure(figsize = (10, 5))\n",
        " \n",
        "# creating the bar plot\n",
        "plt.bar(book_t1, weight_t1, color ='purple',\n",
        "        width = 0.4)\n",
        " \n",
        "plt.xlabel(\"Subject Material Names\")\n",
        "plt.ylabel(\"Weight (in kg)\")\n",
        "plt.title(\"Total subject wise weights (Hardcopies and eBooks combined)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "568b11d1",
      "metadata": {
        "id": "568b11d1",
        "outputId": "690336c4-657a-4be4-c4f1-145c1cb2715d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total courier charges (Offline est.) = 1356981.9000000001 = 1356982 Rupees\n"
          ]
        }
      ],
      "source": [
        "#courier delivery charges (in Rupees) per kg in India (Source: www.DTDC.in)\n",
        "\n",
        "total_charge1 = courier_charges*(w_total1/1000)\n",
        "\n",
        "print(\"total courier charges (Offline est.) =\",total_charge1,\"=\",round(total_charge1),\"Rupees\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "eda6a7d2",
      "metadata": {
        "id": "eda6a7d2",
        "outputId": "e8f20b9f-9e51-4fec-b8b7-2bfaa0100c81",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rs. 633989.6250000001 /-\n"
          ]
        }
      ],
      "source": [
        "#total savings in courier delivery charges\n",
        "print(\"Rs.\",total_charge1-total_charge,\"/-\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "d22c847d",
      "metadata": {
        "id": "d22c847d",
        "outputId": "fd8bfc3c-9496-4583-a999-427d79510f5e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Papers saved: 6.673575 Tonnes\n"
          ]
        }
      ],
      "source": [
        "#total paper savings\n",
        "print(\"Papers saved:\",((w_total1-w_total)/1000)/1000,\"Tonnes\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "c60548ef",
      "metadata": {
        "id": "c60548ef",
        "outputId": "6b627d00-8ad7-457d-9aa4-15f701ccfe86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "y = np.array([total_charge1, total_charge])\n",
        "# or y = np.array([w_total1, w_total])\n",
        "mylabels = [\"Pre-Covid\", \"Post-Covid\"]\n",
        "\n",
        "plt.pie(y, labels = mylabels, startangle = 90)\n",
        "plt.show() "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b5a73005",
      "metadata": {
        "id": "b5a73005"
      },
      "source": [
        "# Scatter plot for subjectwise student data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "4ee67d7c",
      "metadata": {
        "id": "4ee67d7c",
        "outputId": "90c2357e-9e06-460f-8ef7-b9da4466b39f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '0: eBook, 1: Hardcopy')"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(df.sdur_C1, df.pref_C1, marker = '+', color = 'red')\n",
        "plt.title('Cellular Communication')\n",
        "plt.xlabel('Monthly Study Duration (in hours)')\n",
        "plt.ylabel('0: eBook, 1: Hardcopy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "19de7b04",
      "metadata": {
        "id": "19de7b04",
        "outputId": "c10fa656-dc79-4120-8818-9f0dec6924fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '0: eBook, 1: Hardcopy')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(df.sdur_C2, df.pref_C2, marker = '+', color = 'green')\n",
        "plt.title('Inferential Statistics')\n",
        "plt.xlabel('Monthly Study Duration (in hours)')\n",
        "plt.ylabel('0: eBook, 1: Hardcopy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "f8f60917",
      "metadata": {
        "id": "f8f60917",
        "outputId": "8a3b11a9-f404-4087-b023-a679ed7d9ddc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '0: eBook, 1: Hardcopy')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(df.sdur_C3, df.pref_C3, marker = '+', color = 'blue')\n",
        "plt.title('VLSI Technology')\n",
        "plt.xlabel('Monthly Study Duration (in hours)')\n",
        "plt.ylabel('0: eBook, 1: Hardcopy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "fd8017c3",
      "metadata": {
        "id": "fd8017c3",
        "outputId": "0fc6ef91-98ce-4355-b586-ef57f08e3a18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '0: eBook, 1: Hardcopy')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(df.sdur_DE1, df.pref_DE1, marker = '+', color = 'brown')\n",
        "plt.title('Department Elective 1 (Neural Networks & ML)')\n",
        "plt.xlabel('Monthly Study Duration (in hours)')\n",
        "plt.ylabel('0: eBook, 1: Hardcopy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "831a1cd8",
      "metadata": {
        "id": "831a1cd8",
        "outputId": "4f7b51d1-f19a-4272-8ac3-0fc1a0f5d65e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '0: eBook, 1: Hardcopy')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(df.sdur_DE2, df.pref_DE2, marker = '+', color = 'black')\n",
        "plt.title('Department Elective 2 (Image Processing)')\n",
        "plt.xlabel('Monthly Study Duration (in hours)')\n",
        "plt.ylabel('0: eBook, 1: Hardcopy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "b4595716",
      "metadata": {
        "id": "b4595716",
        "outputId": "65087539-3c8f-40c8-caba-3ffd67298e4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '0: eBook, 1: Hardcopy')"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(df.sdur_DE3, df.pref_DE3, marker = '+', color = 'magenta')\n",
        "plt.title('Department Elective 3 (Information Theory & Coding)')\n",
        "plt.xlabel('Monthly Study Duration (in hours)')\n",
        "plt.ylabel('0: eBook, 1: Hardcopy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "52d07c1e",
      "metadata": {
        "id": "52d07c1e",
        "outputId": "695873f9-9742-4e21-de9d-f3095c82b83c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '0: eBook, 1: Hardcopy')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(df.sdur_OE1, df.pref_OE1, marker = '+', color = 'purple')\n",
        "plt.title('Open Elective 1 (Quantum Computation)')\n",
        "plt.xlabel('Monthly Study Duration (in hours)')\n",
        "plt.ylabel('0: eBook, 1: Hardcopy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "a700578d",
      "metadata": {
        "id": "a700578d",
        "outputId": "7fb376f9-3f64-4150-bdcf-046231f4f119",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1717, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "01ed0ea8",
      "metadata": {
        "id": "01ed0ea8"
      },
      "source": [
        "# Applying machine learning algorithm to the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "620f10c4",
      "metadata": {
        "id": "620f10c4",
        "outputId": "2e8ba002-1bda-4dd2-ce74-93cc33f251e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: sklearn in /usr/local/lib/python3.7/dist-packages (0.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sklearn) (1.0.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn) (3.1.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn) (1.1.0)\n",
            "Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn) (1.21.6)\n"
          ]
        }
      ],
      "source": [
        "#!pip install sklearn"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "pATHRm_Es_Ra"
      },
      "id": "pATHRm_Es_Ra",
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "84764c39",
      "metadata": {
        "id": "84764c39"
      },
      "outputs": [],
      "source": [
        "#splitting the dataset into train and test for Cellular Communication\n",
        "X1_train, X1_test, y1_train, y1_test = train_test_split(df[['sdur_C1']], df.pref_C1, test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "ed337d17",
      "metadata": {
        "id": "ed337d17"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "2dae0d13",
      "metadata": {
        "id": "2dae0d13"
      },
      "outputs": [],
      "source": [
        "model1 = LogisticRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "252a18a1",
      "metadata": {
        "id": "252a18a1",
        "outputId": "05a52bb1-d463-4bd1-9603-7ece387d4ad6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "#training our ML model\n",
        "model1.fit(X1_train, y1_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "833fcdb5",
      "metadata": {
        "id": "833fcdb5",
        "outputId": "d79df27a-3207-4662-dd9f-e4ecb348b400",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0,\n",
              "       1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,\n",
              "       0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
              "       0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,\n",
              "       0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
              "       1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
              "       0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1,\n",
              "       0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,\n",
              "       0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0,\n",
              "       0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
              "       0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0,\n",
              "       0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0,\n",
              "       0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
              "       0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1,\n",
              "       1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "model1.predict(X1_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "d836c366",
      "metadata": {
        "id": "d836c366",
        "outputId": "a40d6957-bd8c-4191-fc6c-540302e9c8de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score for Cellular Communication: 92.15116279069767 %\n"
          ]
        }
      ],
      "source": [
        "print(\"Score for Cellular Communication:\", model1.score(X1_test, y1_test)*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "b0e9e706",
      "metadata": {
        "id": "b0e9e706"
      },
      "outputs": [],
      "source": [
        "#splitting the dataset into train and test for Inferential Statistics\n",
        "X2_train, X2_test, y2_train, y2_test = train_test_split(df[['sdur_C2']], df.pref_C2, test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "a53f6607",
      "metadata": {
        "id": "a53f6607"
      },
      "outputs": [],
      "source": [
        "model2 = LogisticRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "45adf7c8",
      "metadata": {
        "id": "45adf7c8",
        "outputId": "34576442-f114-4f53-b97d-de4390d613c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "#training our ML model\n",
        "model2.fit(X2_train, y2_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "de8dc8a9",
      "metadata": {
        "id": "de8dc8a9",
        "outputId": "868fe28e-49b9-46f2-9675-09f365d14d82",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
              "       1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
              "       1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1,\n",
              "       0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0,\n",
              "       0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1,\n",
              "       1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1,\n",
              "       0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1,\n",
              "       0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1,\n",
              "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
              "       0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0,\n",
              "       0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "model2.predict(X2_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "c5f7b44e",
      "metadata": {
        "id": "c5f7b44e",
        "outputId": "82c33572-d2bf-486a-e24e-e4398bfd6925",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score for Inferential Statistics: 93.8953488372093 %\n"
          ]
        }
      ],
      "source": [
        "print(\"Score for Inferential Statistics:\", model2.score(X2_test, y2_test)*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "49c1e508",
      "metadata": {
        "id": "49c1e508"
      },
      "outputs": [],
      "source": [
        "#splitting the dataset into train and test for VLSI\n",
        "X3_train, X3_test, y3_train, y3_test = train_test_split(df[['sdur_C3']], df.pref_C3, test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "038ba5ee",
      "metadata": {
        "id": "038ba5ee"
      },
      "outputs": [],
      "source": [
        "model3 = LogisticRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "b2ce1113",
      "metadata": {
        "id": "b2ce1113",
        "outputId": "a0afa43c-59b0-43ae-bcfe-5d52a46df7ea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "#training our ML model\n",
        "model3.fit(X3_train, y3_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "a4e089b8",
      "metadata": {
        "id": "a4e089b8",
        "outputId": "17e5738d-426e-45a7-f44f-cba7997a1b6f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0,\n",
              "       1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0,\n",
              "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1,\n",
              "       1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
              "       0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
              "       1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
              "       1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1,\n",
              "       1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1,\n",
              "       1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0,\n",
              "       1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "       1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1,\n",
              "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1,\n",
              "       0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "model3.predict(X3_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "id": "05f62a63",
      "metadata": {
        "id": "05f62a63",
        "outputId": "45de8cc9-4964-4a21-abf7-f80b05bd4d4b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score for VLSI: 93.31395348837209 %\n"
          ]
        }
      ],
      "source": [
        "print(\"Score for VLSI:\", model3.score(X3_test, y3_test)*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "id": "2b6ccaff",
      "metadata": {
        "id": "2b6ccaff"
      },
      "outputs": [],
      "source": [
        "#splitting the dataset into train and test for DE1\n",
        "X4_train, X4_test, y4_train, y4_test = train_test_split(df[['sdur_DE1']], df.pref_DE1, test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "id": "05519a44",
      "metadata": {
        "id": "05519a44"
      },
      "outputs": [],
      "source": [
        "model4 = LogisticRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "id": "ca0ae706",
      "metadata": {
        "id": "ca0ae706",
        "outputId": "2d5f41f6-b169-4787-b8c9-321f40318744",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "#training our ML model\n",
        "model4.fit(X4_train, y4_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "id": "94bb7819",
      "metadata": {
        "id": "94bb7819",
        "outputId": "f6149567-8550-4d73-8c13-e9034bb3c965",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1,\n",
              "       0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1,\n",
              "       0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1,\n",
              "       1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0,\n",
              "       0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0,\n",
              "       1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1,\n",
              "       0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
              "       1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0,\n",
              "       0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
              "       1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1,\n",
              "       1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n",
              "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1,\n",
              "       0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "model4.predict(X4_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "id": "c19c1b2a",
      "metadata": {
        "id": "c19c1b2a",
        "outputId": "90f0b084-d7d2-4279-bf4d-ef43db431ce9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score for DE1: 93.02325581395348 %\n"
          ]
        }
      ],
      "source": [
        "print(\"Score for DE1:\", model4.score(X4_test, y4_test)*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "88668647",
      "metadata": {
        "id": "88668647"
      },
      "outputs": [],
      "source": [
        "#splitting the dataset into train and test for DE2\n",
        "X5_train, X5_test, y5_train, y5_test = train_test_split(df[['sdur_DE2']], df.pref_DE2, test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "id": "0763f04f",
      "metadata": {
        "id": "0763f04f"
      },
      "outputs": [],
      "source": [
        "model5 = LogisticRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "id": "e982af45",
      "metadata": {
        "id": "e982af45",
        "outputId": "ed5cdb93-48fd-4d0d-e3a7-84a4e723efe9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "#training our ML model\n",
        "model5.fit(X5_train, y5_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "id": "1c7e56d0",
      "metadata": {
        "id": "1c7e56d0",
        "outputId": "7421955a-67f6-4240-8dd6-2e3ed8ccad5a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0,\n",
              "       1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
              "       0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
              "       1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1,\n",
              "       1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
              "       0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0,\n",
              "       1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
              "       1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0,\n",
              "       0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n",
              "       1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
              "       0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0,\n",
              "       0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1,\n",
              "       1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
              "       1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "model4.predict(X5_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "id": "517915d6",
      "metadata": {
        "id": "517915d6",
        "outputId": "6e23c2df-d633-44f5-da7f-8bf427692057",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score for DE2: 92.44186046511628 %\n"
          ]
        }
      ],
      "source": [
        "print(\"Score for DE2:\", model5.score(X5_test, y5_test)*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "id": "e383ed74",
      "metadata": {
        "id": "e383ed74"
      },
      "outputs": [],
      "source": [
        "#splitting the dataset into train and test for DE3\n",
        "X6_train, X6_test, y6_train, y6_test = train_test_split(df[['sdur_DE3']], df.pref_DE3, test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "id": "8dbe1d14",
      "metadata": {
        "id": "8dbe1d14"
      },
      "outputs": [],
      "source": [
        "model6 = LogisticRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "id": "88ec7c87",
      "metadata": {
        "id": "88ec7c87",
        "outputId": "a333389e-8973-444f-b49c-26dcb94ba41a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "#training our ML model\n",
        "model6.fit(X6_train, y6_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "id": "54872995",
      "metadata": {
        "id": "54872995",
        "outputId": "d8204f07-6c32-48f5-dc7a-c67217e60fe7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0,\n",
              "       1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1,\n",
              "       0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0,\n",
              "       0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0,\n",
              "       1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1,\n",
              "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1,\n",
              "       1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1,\n",
              "       1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
              "       0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1,\n",
              "       0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1,\n",
              "       1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0,\n",
              "       1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "model6.predict(X6_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "id": "1ce637cc",
      "metadata": {
        "id": "1ce637cc",
        "outputId": "4d125585-83af-4f62-9fb4-568c69e6f362",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score for DE2: 92.44186046511628 %\n"
          ]
        }
      ],
      "source": [
        "print(\"Score for DE2:\", model6.score(X6_test, y6_test)*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "id": "3abfbcac",
      "metadata": {
        "id": "3abfbcac"
      },
      "outputs": [],
      "source": [
        "#splitting the dataset into train and test for OE1\n",
        "X7_train, X7_test, y7_train, y7_test = train_test_split(df[['sdur_OE1']], df.pref_OE1, test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "id": "e781365e",
      "metadata": {
        "id": "e781365e"
      },
      "outputs": [],
      "source": [
        "model7 = LogisticRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "id": "0f1c2a7c",
      "metadata": {
        "id": "0f1c2a7c",
        "outputId": "fb931eba-e8b3-4224-9266-a4fb9029fc3e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "#training our ML model\n",
        "model7.fit(X7_train, y7_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "id": "9be9aa87",
      "metadata": {
        "id": "9be9aa87",
        "outputId": "f6a4f480-720d-431b-8f46-5a6480733c0c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
              "       0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1,\n",
              "       0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
              "       1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1,\n",
              "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
              "       1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0,\n",
              "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1,\n",
              "       0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
              "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
              "       1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
              "       0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "model7.predict(X7_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "id": "df810319",
      "metadata": {
        "id": "df810319",
        "outputId": "61dd7f50-8f7a-4d02-be6c-9006e7bec00f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score for OE1: 95.63953488372093 %\n"
          ]
        }
      ],
      "source": [
        "print(\"Score for OE1:\", model7.score(X7_test, y7_test)*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "id": "f6c528c9",
      "metadata": {
        "id": "f6c528c9",
        "outputId": "10147245-0876-446c-ea45-a864d3f0952c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#plotting model scores\n",
        "\n",
        "data = {'C1':model1.score(X1_test, y1_test)*100,\n",
        "        'C2':model2.score(X2_test, y2_test)*100,\n",
        "        'C3':model3.score(X3_test, y3_test)*100,\n",
        "        'DE1':model4.score(X4_test, y4_test)*100,\n",
        "        'DE2':model5.score(X5_test, y5_test)*100,\n",
        "        'DE3':model6.score(X6_test, y6_test)*100,\n",
        "        'OE1':model7.score(X7_test, y7_test)*100}\n",
        "\n",
        "keyy = list(data.keys())\n",
        "val = list(data.values())\n",
        "  \n",
        "fig = plt.figure(figsize = (10, 5))\n",
        " \n",
        "# creating the bar plot\n",
        "plt.bar(keyy, val,\n",
        "        color=['blue', 'cyan', 'blue', 'cyan', 'blue', 'cyan', 'blue'],\n",
        "        width = 0.4)\n",
        " \n",
        "plt.xlabel(\"Subjects\")\n",
        "plt.ylabel(\"Model Score (in %)\")\n",
        "plt.title(\"Logistic Regression Model Scores (in %)\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "name": "minor_EC3082.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}