Day_35_Handling_Missing_data.ipynb

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        "import pandas as pd\n",
        "import numpy as np"
      ]
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      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/campusx-official/100-days-of-machine-learning/main/day35-complete-case-analysis/data_science_job.csv')"
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        "df[df['city_development_index']==max(df.city_development_index)].shape,df[df['city_development_index']==min(df.city_development_index)].shape"
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              "       enrollee_id      city  city_development_index gender  \\\n",
              "12742         5273  city_136                   0.897   Male   \n",
              "6588         23916   city_98                   0.949    NaN   \n",
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              "      major_discipline  experience company_size   company_type  \\\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 19158 entries, 0 to 19157\n",
            "Data columns (total 13 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   enrollee_id             19158 non-null  int64  \n",
            " 1   city                    19158 non-null  object \n",
            " 2   city_development_index  18679 non-null  float64\n",
            " 3   gender                  14650 non-null  object \n",
            " 4   relevent_experience     19158 non-null  object \n",
            " 5   enrolled_university     18772 non-null  object \n",
            " 6   education_level         18698 non-null  object \n",
            " 7   major_discipline        16345 non-null  object \n",
            " 8   experience              19093 non-null  float64\n",
            " 9   company_size            13220 non-null  object \n",
            " 10  company_type            13018 non-null  object \n",
            " 11  training_hours          18392 non-null  float64\n",
            " 12  target                  19158 non-null  float64\n",
            "dtypes: float64(4), int64(1), object(8)\n",
            "memory usage: 1.9+ MB\n"
          ]
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      "cell_type": "code",
      "source": [
        "df.isna().mean()*100"
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        "id": "aTyo1YQYRNfL",
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      "execution_count": 4,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "enrollee_id                0.000000\n",
              "city                       0.000000\n",
              "city_development_index     2.500261\n",
              "gender                    23.530640\n",
              "relevent_experience        0.000000\n",
              "enrolled_university        2.014824\n",
              "education_level            2.401086\n",
              "major_discipline          14.683161\n",
              "experience                 0.339284\n",
              "company_size              30.994885\n",
              "company_type              32.049274\n",
              "training_hours             3.998330\n",
              "target                     0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px"
      ],
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        "id": "choScnuxRQSm"
      },
      "execution_count": 16,
      "outputs": []
    },
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        "df.corr()['target']"
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "enrollee_id               0.049475\n",
              "city_development_index   -0.340239\n",
              "experience               -0.178238\n",
              "training_hours           -0.021706\n",
              "target                    1.000000\n",
              "Name: target, dtype: float64"
            ]
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          "execution_count": 19
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      "source": [
        "cols = [var for var in df.columns if df[var].isnull().mean()<=0.05 and df[var].isnull().mean()>0]\n",
        "cols"
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      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['city_development_index',\n",
              " 'enrolled_university',\n",
              " 'education_level',\n",
              " 'experience',\n",
              " 'training_hours']"
            ]
          },
          "metadata": {},
          "execution_count": 20
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      "cell_type": "code",
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      "execution_count": 21,
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            "text/plain": [
              "       city_development_index enrolled_university education_level  experience  \\\n",
              "0                       0.920       no_enrollment        Graduate        20.0   \n",
              "1                       0.776       no_enrollment        Graduate        15.0   \n",
              "2                       0.624    Full time course        Graduate         5.0   \n",
              "3                       0.789                 NaN        Graduate         0.0   \n",
              "4                       0.767       no_enrollment         Masters        20.0   \n",
              "...                       ...                 ...             ...         ...   \n",
              "19153                   0.878       no_enrollment        Graduate        14.0   \n",
              "19154                   0.920       no_enrollment        Graduate        14.0   \n",
              "19155                   0.920       no_enrollment        Graduate        20.0   \n",
              "19156                   0.802       no_enrollment     High School         0.0   \n",
              "19157                   0.855       no_enrollment  Primary School         2.0   \n",
              "\n",
              "       training_hours  \n",
              "0                36.0  \n",
              "1                47.0  \n",
              "2                83.0  \n",
              "3                52.0  \n",
              "4                 8.0  \n",
              "...               ...  \n",
              "19153            42.0  \n",
              "19154            52.0  \n",
              "19155            44.0  \n",
              "19156            97.0  \n",
              "19157           127.0  \n",
              "\n",
              "[19158 rows x 5 columns]"
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              "      <td>Graduate</td>\n",
              "      <td>5.0</td>\n",
              "      <td>83.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.789</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>0.0</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.767</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>20.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19153</th>\n",
              "      <td>0.878</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>14.0</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19154</th>\n",
              "      <td>0.920</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>14.0</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19155</th>\n",
              "      <td>0.920</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>20.0</td>\n",
              "      <td>44.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19156</th>\n",
              "      <td>0.802</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>High School</td>\n",
              "      <td>0.0</td>\n",
              "      <td>97.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19157</th>\n",
              "      <td>0.855</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Primary School</td>\n",
              "      <td>2.0</td>\n",
              "      <td>127.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19158 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b77c1ae-ceea-4202-bc6a-2a201a9a7cce')\"\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9b77c1ae-ceea-4202-bc6a-2a201a9a7cce 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-9b77c1ae-ceea-4202-bc6a-2a201a9a7cce');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df[cols].dropna())/len(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-_OMtEtTltB",
        "outputId": "a6e489ab-0197-4134-b9ce-604f8d489ae8"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8968577095730244"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = df[cols].dropna()\n",
        "new_df.shape, df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9VTclp4uTlk4",
        "outputId": "b1b5f11d-8799-4a3e-99a2-ccc367398f4a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((17182, 5), (19158, 13))"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.hist(bins = 50, density = True, figsize = (12,6))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "uROaEDxSXCbS",
        "outputId": "92c8012c-af01-4f7f-bbe9-6c0b5daebf78"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, we have applied CCA succesfully. Now we need to check that the distribution of data before and after CCA should be almost equal. We will do it using graphs."
      ],
      "metadata": {
        "id": "LALVvBxwUhz5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (14,4))\n",
        "plt.subplot(111)\n",
        "plt.hist(df['training_hours'],bins = 50, color = 'r', density = True)\n",
        "# plt.show()\n",
        "plt.subplot(111)\n",
        "plt.hist(new_df['training_hours'],bins = 50, color = 'g', density = True, alpha = 0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "HbYTfTN3XmVN",
        "outputId": "60b4e6c9-5f35-40e1-fe8b-1a0898838107"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (14,4))\n",
        "plt.subplot(121)\n",
        "plt.hist(df['training_hours'],bins = 50, color = 'r', density = True)\n",
        "# plt.show()\n",
        "plt.subplot(121)\n",
        "plt.hist(new_df['training_hours'],bins = 50, color = 'g', density = True, alpha = 0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "qVZW5MwNUhTs",
        "outputId": "07729f50-76da-49fb-b465-73116ba2bd0f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x400 with 1 Axes>"
            ],
            "image/png": 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vA9PQoUOVn5+v4uJixcXF6Z/+6Z908OBBzZ49u4eGAgAA2CjksJObm6ujR4+quLhYXq9X6enpqqqqCtxg3NjYGDQDM23aNJWXl2v58uVatmyZUlNTtXnzZk2YMCFQ89BDD6mlpUXz58/XiRMnNH36dFVVVSk2NlbSl5fPqqqq9OMf/1jf+c53dObMGV1//fV68cUXlZaW9nXHAAAAWCzk5+z0V5f6OTvh+gydruA5OwCAUPT0ezbfjQUAAKxG2AEAAFYj7AAAAKsRdgAAgNX6xROU0c9FdOHm7fC4Tx4A0AcIO7jksvM6r9ly6bsBAAhTXMYCAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVuhV21q1bp7Fjxyo2NlZut1s7d+7ssL6iokLjxo1TbGysJk6cqK1btwZtN8aouLhYiYmJiouLU2Zmpvbv33/Rfn7729/K7XYrLi5OQ4cOVU5OTne6DwAAwkjIYWfTpk0qKipSSUmJdu/erbS0NGVlZenIkSNt1u/YsUN5eXkqLCxUQ0ODcnJylJOTo7179wZqVq5cqTVr1qisrEx1dXW68sorlZWVpVOnTgVq/vM//1P33HOPCgoK9Ic//EFvvPGG7rrrrm4cMgAACCcRxhgTSgO3263Jkydr7dq1kiS/36/k5GQtXLhQS5Ysuag+NzdXLS0tqqysDKybOnWq0tPTVVZWJmOMkpKStHjxYj3wwAOSpObmZjmdTm3YsEFz587V2bNnNXbsWP3kJz9RYWFhtw7U5/MpPj5ezc3Ncjgc3dpHR7LviujxfYaTLeUhnYYAAIv19Ht2SDM7p0+fVn19vTIzMy/sIDJSmZmZqq2tbbNNbW1tUL0kZWVlBeoPHjwor9cbVBMfHy+32x2o2b17tz7++GNFRkbqxhtvVGJiom677bag2aGvam1tlc/nC1oAAED4CSnsHDt2TOfOnZPT6Qxa73Q65fV622zj9Xo7rD//s6OaP/7xj5Kkhx9+WMuXL1dlZaWGDh2qW265RcePH2/zdUtLSxUfHx9YkpOTQzlUAABgiX7xaSy/3y9J+vGPf6w777xTGRkZeuaZZxQREaGKioo22yxdulTNzc2B5dChQ73ZZQAAcJkIKewkJCQoKipKTU1NQeubmprkcrnabONyuTqsP/+zo5rExERJ0vjx4wPbY2JidPXVV6uxsbHN142JiZHD4QhaAABA+Akp7ERHRysjI0M1NTWBdX6/XzU1NfJ4PG228Xg8QfWSVF1dHahPSUmRy+UKqvH5fKqrqwvUZGRkKCYmRvv27QvUnDlzRh999JHGjBkTyiEAAIAwMyDUBkVFRcrPz9ekSZM0ZcoUrV69Wi0tLSooKJAkzZs3T6NGjVJpaakkadGiRZoxY4ZWrVql2bNna+PGjdq1a5fWr18vSYqIiND999+vRx55RKmpqUpJSdGKFSuUlJQUeI6Ow+HQfffdp5KSEiUnJ2vMmDF67LHHJEnf/e53e2IcAACApUIOO7m5uTp69KiKi4vl9XqVnp6uqqqqwA3GjY2Nioy8MGE0bdo0lZeXa/ny5Vq2bJlSU1O1efNmTZgwIVDz0EMPqaWlRfPnz9eJEyc0ffp0VVVVKTY2NlDz2GOPacCAAbrnnnv0xRdfyO12a9u2bRo6dOjXOX4AAGC5kJ+z01/xnJ3LG8/ZAQCc16fP2QEAAOhvCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKuF/ARloM9EdOHBjeHxjEwAQAgIO+g3svM6r9ly6bsBAOhnuIwFAACsRtgBAABW4zIWrNKVL2TlS0cBILwwswMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArMYTlHFZ6MqTjwEA6A5mdgAAgNWY2UH4iejCLJLh+7MAwBaEHYSd7LzOa7Zc+m4AAHoJl7EAAIDVCDsAAMBqhB0AAGA1wg4AALAaNygDl1BXnh+0pZxPfgHApcTMDgAAsBphBwAAWI2wAwAArNatsLNu3TqNHTtWsbGxcrvd2rlzZ4f1FRUVGjdunGJjYzVx4kRt3bo1aLsxRsXFxUpMTFRcXJwyMzO1f//+NvfV2tqq9PR0RUREaM+ePd3pPgAACCMhh51NmzapqKhIJSUl2r17t9LS0pSVlaUjR460Wb9jxw7l5eWpsLBQDQ0NysnJUU5Ojvbu3RuoWblypdasWaOysjLV1dXpyiuvVFZWlk6dOnXR/h566CElJSWF2m0AABCmQg47TzzxhO69914VFBRo/PjxKisr0xVXXKF/+Zd/abP+5z//uWbNmqUHH3xQ3/zmN/XTn/5UN910k9auXSvpy1md1atXa/ny5ZozZ45uuOEG/frXv9bhw4e1efPmoH29/PLL+t3vfqfHH3+80362trbK5/MFLQAAIPyEFHZOnz6t+vp6ZWZmXthBZKQyMzNVW1vbZpva2tqgeknKysoK1B88eFBerzeoJj4+Xm63O2ifTU1Nuvfee/Wv//qvuuKKKzrta2lpqeLj4wNLcnJyKIcKAAAsEVLYOXbsmM6dOyen0xm03ul0yuv1ttnG6/V2WH/+Z0c1xhh973vf03333adJkyZ1qa9Lly5Vc3NzYDl06FCX2gEAALv0i4cKPvXUUzp58qSWLl3a5TYxMTGKiYm5hL0CAAD9QUgzOwkJCYqKilJTU1PQ+qamJrlcrjbbuFyuDuvP/+yoZtu2baqtrVVMTIwGDBiga6+9VpI0adIk5efnh3IIAAAgzIQUdqKjo5WRkaGamprAOr/fr5qaGnk8njbbeDyeoHpJqq6uDtSnpKTI5XIF1fh8PtXV1QVq1qxZoz/84Q/as2eP9uzZE/jo+qZNm/SP//iPoRwCAAAIMyFfxioqKlJ+fr4mTZqkKVOmaPXq1WppaVFBQYEkad68eRo1apRKS0slSYsWLdKMGTO0atUqzZ49Wxs3btSuXbu0fv16SVJERITuv/9+PfLII0pNTVVKSopWrFihpKQk5eTkSJJGjx4d1IdBgwZJkq655hpdddVV3T544GuJ6Px7r5R36bsBAOhYyGEnNzdXR48eVXFxsbxer9LT01VVVRW4wbixsVGRkRcmjKZNm6by8nItX75cy5YtU2pqqjZv3qwJEyYEah566CG1tLRo/vz5OnHihKZPn66qqirFxsb2wCECl0Y2QQYA+oUIY0xYfOWyz+dTfHy8mpub5XA4enz/Xfl2a6AtfOs5AATr6fdsvhsLAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYL+YtAAfSs7OeyO63ZkrelF3oCAHYi7AB9bUtl5zV8wzoAdBuXsQAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALBat8LOunXrNHbsWMXGxsrtdmvnzp0d1ldUVGjcuHGKjY3VxIkTtXXr1qDtxhgVFxcrMTFRcXFxyszM1P79+wPbP/roIxUWFiolJUVxcXG65pprVFJSotOnT3en+wAAIIyEHHY2bdqkoqIilZSUaPfu3UpLS1NWVpaOHDnSZv2OHTuUl5enwsJCNTQ0KCcnRzk5Odq7d2+gZuXKlVqzZo3KyspUV1enK6+8UllZWTp16pQk6YMPPpDf79cvf/lLvfvuu3ryySdVVlamZcuWdfOwAQBAuIgwxphQGrjdbk2ePFlr166VJPn9fiUnJ2vhwoVasmTJRfW5ublqaWlRZWVlYN3UqVOVnp6usrIyGWOUlJSkxYsX64EHHpAkNTc3y+l0asOGDZo7d26b/Xjsscf09NNP649//GOX+u3z+RQfH6/m5mY5HI5QDrlLsu+K6PF9AudtKQ/pzxQA+rWefs8OaWbn9OnTqq+vV2Zm5oUdREYqMzNTtbW1bbapra0NqpekrKysQP3Bgwfl9XqDauLj4+V2u9vdp/RlIBo2bFi721tbW+Xz+YIWAAAQfkIKO8eOHdO5c+fkdDqD1judTnm93jbbeL3eDuvP/wxlnwcOHNBTTz2lv/3bv223r6WlpYqPjw8sycnJHR8cAACwUr/7NNbHH3+sWbNm6bvf/a7uvffeduuWLl2q5ubmwHLo0KFe7CUAALhchBR2EhISFBUVpaampqD1TU1NcrlcbbZxuVwd1p//2ZV9Hj58WH/2Z3+madOmaf369R32NSYmRg6HI2gBAADhJ6SwEx0drYyMDNXU1ATW+f1+1dTUyOPxtNnG4/EE1UtSdXV1oD4lJUUulyuoxufzqa6uLmifH3/8sW655RZlZGTomWeeUWRkv5uUAgAAfWBAqA2KioqUn5+vSZMmacqUKVq9erVaWlpUUFAgSZo3b55GjRql0tJSSdKiRYs0Y8YMrVq1SrNnz9bGjRu1a9euwMxMRESE7r//fj3yyCNKTU1VSkqKVqxYoaSkJOXk5Ei6EHTGjBmjxx9/XEePHg30p70ZJQAAAKkbYSc3N1dHjx5VcXGxvF6v0tPTVVVVFbjBuLGxMWjWZdq0aSovL9fy5cu1bNkypaamavPmzZowYUKg5qGHHlJLS4vmz5+vEydOaPr06aqqqlJsbKykL2eCDhw4oAMHDuiqq64K6k+In5wHrJX9XHanNVvytvRCT3pWVx7rwEfzAXQk5Ofs9Fc8Zwf9WVfezG0NBbYeF4D29fR7dsgzOwDQU+GekAKgN3CXLwAAsBozO0AYudwut3blPiMA+LoIOwD6zpbKzmsA4GviMhYAALAaYQcAAFiNsAMAAKxG2AEAAFbjBmWgH7jcPkUFAP0JMzsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI0vAgWA/9WVL1zdUm56oSchiOjCl8Say6zPQC8j7ABAP5ad13nNlkvfDeCyRtgBgD7QL2eRgH6KsAMgPHTlck8XZkm6ElJ6Sm++FmAzwg6A/q8LQaYrl3sA2ImwA6DfI8gA6AgfPQcAAFYj7AAAAKtxGQsALNffPvnV3/prM1v+LZjZAQAAVmNmBwDQI7Kfy+60Zkve5fWIw8tt5qLH+sMnFIMQdgAAPWNLZec1PfQGe7mFlK7oqecmdWk/YRRkuoKwAwDoVK++UV9m+mOfEaxb9+ysW7dOY8eOVWxsrNxut3bu3NlhfUVFhcaNG6fY2FhNnDhRW7duDdpujFFxcbESExMVFxenzMxM7d+/P6jm+PHjuvvuu+VwODRkyBAVFhbqs88+6073AQBfkX1XRIcL0J+FPLOzadMmFRUVqaysTG63W6tXr1ZWVpb27dunkSNHXlS/Y8cO5eXlqbS0VLfffrvKy8uVk5Oj3bt3a8KECZKklStXas2aNXr22WeVkpKiFStWKCsrS++9955iY2MlSXfffbc++eQTVVdX68yZMyooKND8+fNVXl7+NYcAAGAjQhrOizDGhHRR0+12a/LkyVq7dq0kye/3Kzk5WQsXLtSSJUsuqs/NzVVLS4sqKy9cy506darS09NVVlYmY4ySkpK0ePFiPfDAA5Kk5uZmOZ1ObdiwQXPnztX777+v8ePH66233tKkSZMkSVVVVfrzP/9z/elPf1JSUtJFr9va2qrW1tbA783NzRo9erQOHTokh8MRyiF3yf8rjO/xfQIAcLn7zT839/g+fT6fkpOTdeLECcXH98D7qwlBa2uriYqKMi+88ELQ+nnz5pm/+Iu/aLNNcnKyefLJJ4PWFRcXmxtuuMEYY8yHH35oJJmGhoagmptvvtn88Ic/NMYY88///M9myJAhQdvPnDljoqKizPPPP9/m65aUlBhJLCwsLCwsLP10OXToUBcTSsdCuox17NgxnTt3Tk6nM2i90+nUBx980GYbr9fbZr3X6w1sP7+uo5qvXiIbMGCAhg0bFqj5qqVLl6qoqCjwu9/v1/HjxzV8+HBFdOXbj7vgfPK8VLNF/QljcQFjcQFjcQFjcQFjcQFjccH/HYvBgwfr5MmTbV656Q5rP40VExOjmJiYoHVDhgy5JK/lcDjC/iQ9j7G4gLG4gLG4gLG4gLG4gLG44PxY9Mjlq/8V0qexEhISFBUVpaampqD1TU1NcrlcbbZxuVwd1p//2VnNkSNHgrafPXtWx48fb/d1AQAApBDDTnR0tDIyMlRTUxNY5/f7VVNTI4/H02Ybj8cTVC9J1dXVgfqUlBS5XK6gGp/Pp7q6ukCNx+PRiRMnVF9fH6jZtm2b/H6/3G53KIcAAADCTag3+WzcuNHExMSYDRs2mPfee8/Mnz/fDBkyxHi9XmOMMffcc49ZsmRJoP6NN94wAwYMMI8//rh5//33TUlJiRk4cKB55513AjWPPvqoGTJkiHnxxRfN22+/bebMmWNSUlLMF198EaiZNWuWufHGG01dXZ15/fXXTWpqqsnLy/s69yt9badOnTIlJSXm1KlTfdqPywFjcQFjcQFjcQFjcQFjcQFjccGlHIuQw44xxjz11FNm9OjRJjo62kyZMsW8+eabgW0zZsww+fn5QfW/+c1vzDe+8Q0THR1trr/+evPb3/42aLvf7zcrVqwwTqfTxMTEmFtvvdXs27cvqObTTz81eXl5ZtCgQcbhcJiCggJz8uTJ7nQfAACEkZCfswMAANCfdOvrIgAAAPoLwg4AALAaYQcAAFiNsAMAAKxG2Pka1q1bp7Fjxyo2NlZut1s7d+7s6y5dcg8//LAiIiKClnHjxgW2nzp1SgsWLNDw4cM1aNAg3XnnnRc9MLK/eu2115Sdna2kpCRFRERo8+bNQduNMSouLlZiYqLi4uKUmZmp/fv3B9UcP35cd999txwOh4YMGaLCwkJ99tlnvXgUX19n4/C9733vonNk1qxZQTU2jIMklZaWavLkyRo8eLBGjhypnJwc7du3L6imK38TjY2Nmj17tq644gqNHDlSDz74oM6ePdubh/K1dWUsbrnllovOjfvuuy+oxoaxePrpp3XDDTcEngTs8Xj08ssvB7aHyzkhdT4WvXVOEHa6adOmTSoqKlJJSYl2796ttLQ0ZWVlXfSkZxtdf/31+uSTTwLL66+/Htj2ox/9SFu2bFFFRYW2b9+uw4cP64477ujD3vaclpYWpaWlad26dW1uX7lypdasWaOysjLV1dXpyiuvVFZWlk6dOhWoufvuu/Xuu++qurpalZWVeu211zR//vzeOoQe0dk4SNKsWbOCzpHnnnsuaLsN4yBJ27dv14IFC/Tmm2+qurpaZ86c0cyZM9XS0hKo6exv4ty5c5o9e7ZOnz6tHTt26Nlnn9WGDRtUXFzcF4fUbV0ZC0m69957g86NlStXBrbZMhZXXXWVHn30UdXX12vXrl36zne+ozlz5ujdd9+VFD7nhNT5WEi9dE708Uff+60pU6aYBQsWBH4/d+6cSUpKMqWlpX3Yq0uvpKTEpKWltbntxIkTZuDAgaaioiKw7v333zeSTG1tbS/1sHdIMi+88ELgd7/fb1wul3nssccC606cOGFiYmLMc889Z4wx5r333jOSzFtvvRWoefnll01ERIT5+OOPe63vPemr42CMMfn5+WbOnDnttrFxHM47cuSIkWS2b99ujOna38TWrVtNZGRk4MGsxhjz9NNPG4fDYVpbW3v3AHrQV8fCmC+fw7Zo0aJ229g6FsYYM3ToUPOrX/0qrM+J886PhTG9d04ws9MNp0+fVn19vTIzMwPrIiMjlZmZqdra2j7sWe/Yv3+/kpKSdPXVV+vuu+9WY2OjJKm+vl5nzpwJGpdx48Zp9OjR1o/LwYMH5fV6g449Pj5ebrc7cOy1tbUaMmSIJk2aFKjJzMxUZGSk6urqer3Pl9Krr76qkSNH6rrrrtP3v/99ffrpp4FtNo9Dc3OzJGnYsGGSuvY3UVtbq4kTJ8rpdAZqsrKy5PP5gv7vt7/56lic9+///u9KSEjQhAkTtHTpUn3++eeBbTaOxblz57Rx40a1tLTI4/GE9Tnx1bE4rzfOCWu/9fxSOnbsmM6dOxc0+JLkdDr1wQcf9FGveofb7daGDRt03XXX6ZNPPtFPfvITffvb39bevXvl9XoVHR190bfLO51Oeb3evulwLzl/fG2dE+e3eb1ejRw5Mmj7gAEDNGzYMKvGZ9asWbrjjjuUkpKiDz/8UMuWLdNtt92m2tpaRUVFWTsOfr9f999/v771rW9pwoQJktSlvwmv19vmeXN+W3/U1lhI0l133aUxY8YoKSlJb7/9tv7+7/9e+/bt0/PPPy/JrrF455135PF4dOrUKQ0aNEgvvPCCxo8frz179oTdOdHeWEi9d04QdhCS2267LfDfN9xwg9xut8aMGaPf/OY3iouL68Oe4XIxd+7cwH9PnDhRN9xwg6655hq9+uqruvXWW/uwZ5fWggULtHfv3qB72MJVe2Pxf+/LmjhxohITE3Xrrbfqww8/1DXXXNPb3bykrrvuOu3Zs0fNzc36j//4D+Xn52v79u193a0+0d5YjB8/vtfOCS5jdUNCQoKioqIuunu+qalJLperj3rVN4YMGaJvfOMbOnDggFwul06fPq0TJ04E1YTDuJw/vo7OCZfLddEN7GfPntXx48etHp+rr75aCQkJOnDggCQ7x+EHP/iBKisr9corr+iqq64KrO/K34TL5WrzvDm/rb9pbyza4na7JSno3LBlLKKjo3XttdcqIyNDpaWlSktL089//vOwPCfaG4u2XKpzgrDTDdHR0crIyFBNTU1gnd/vV01NTdB1yHDw2Wef6cMPP1RiYqIyMjI0cODAoHHZt2+fGhsbrR+XlJQUuVyuoGP3+Xyqq6sLHLvH49GJEydUX18fqNm2bZv8fn/gD9xGf/rTn/Tpp58qMTFRkl3jYIzRD37wA73wwgvatm2bUlJSgrZ35W/C4/HonXfeCQqA1dXVcjgcgan+/qCzsWjLnj17JCno3LBhLNri9/vV2toaVudEe86PRVsu2TnRzZupw97GjRtNTEyM2bBhg3nvvffM/PnzzZAhQ4LuGLfR4sWLzauvvmoOHjxo3njjDZOZmWkSEhLMkSNHjDHG3HfffWb06NFm27ZtZteuXcbj8RiPx9PHve4ZJ0+eNA0NDaahocFIMk888YRpaGgw//M//2OMMebRRx81Q4YMMS+++KJ5++23zZw5c0xKSor54osvAvuYNWuWufHGG01dXZ15/fXXTWpqqsnLy+urQ+qWjsbh5MmT5oEHHjC1tbXm4MGD5ve//7256aabTGpqqjl16lRgHzaMgzHGfP/73zfx8fHm1VdfNZ988klg+fzzzwM1nf1NnD171kyYMMHMnDnT7Nmzx1RVVZkRI0aYpUuX9sUhdVtnY3HgwAHzD//wD2bXrl3m4MGD5sUXXzRXX321ufnmmwP7sGUslixZYrZv324OHjxo3n77bbNkyRITERFhfve73xljwuecMKbjsejNc4Kw8zU89dRTZvTo0SY6OtpMmTLFvPnmm33dpUsuNzfXJCYmmujoaDNq1CiTm5trDhw4ENj+xRdfmL/7u78zQ4cONVdccYX5y7/8S/PJJ5/0YY97ziuvvGIkXbTk5+cbY778+PmKFSuM0+k0MTEx5tZbbzX79u0L2senn35q8vLyzKBBg4zD4TAFBQXm5MmTfXA03dfROHz++edm5syZZsSIEWbgwIFmzJgx5t57773ofwJsGAdjTJvjIMk888wzgZqu/E189NFH5rbbbjNxcXEmISHBLF682Jw5c6aXj+br6WwsGhsbzc0332yGDRtmYmJizLXXXmsefPBB09zcHLQfG8bib/7mb8yYMWNMdHS0GTFihLn11lsDQceY8DknjOl4LHrznIgwxpiuzwMBAAD0L9yzAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACr/X+16qe9kcjQ+QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111)\n",
        "\n",
        "df['city_development_index'].hist(bins = 50,ax = ax, density = True, color = 'r')\n",
        "\n",
        "new_df['city_development_index'].hist(bins = 50,ax = ax, density = True, color = 'g',alpha = 0.7)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "pXOG17l8WrGI",
        "outputId": "ab4c1da9-7374-4120-eeec-4fb160de10d1"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fig = plt.figure()\n",
        "# ax = fig.add_subplot(111)\n",
        "\n",
        "df['city_development_index'].hist(bins = 50, density = True, color = 'r')\n",
        "\n",
        "new_df['city_development_index'].hist(bins = 50, density = True, color = 'g',alpha = 0.7)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "RjjegIE3Vp1P",
        "outputId": "e0c9edba-22ac-4d86-a78f-ba7469638b07"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['experience'].plot.density(color = 'red')\n",
        "new_df['experience'].plot.density(color = 'green')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "adWYHBGWT8TE",
        "outputId": "eeacc799-6ee4-43a0-8650-89a561af3e0d"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "All the graphs shows that the distribution of data after transformation is overlapping with the data before transformation"
      ],
      "metadata": {
        "id": "LpwLROTxZKvj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, Let's verify the constraint with categorical data also."
      ],
      "metadata": {
        "id": "tE2WgHMOZzu3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temp = pd.concat([\n",
        "            # percentage of observations per category, original data\n",
        "            df['enrolled_university'].value_counts() / len(df),\n",
        "\n",
        "            # percentage of observations per category, cca data\n",
        "            new_df['enrolled_university'].value_counts() / len(new_df)\n",
        "        ],\n",
        "        axis=1)\n",
        "\n",
        "# add column names\n",
        "temp.columns = ['original', 'cca']\n",
        "\n",
        "temp"
      ],
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      "execution_count": 71,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                  original       cca\n",
              "no_enrollment     0.721213  0.735188\n",
              "Full time course  0.196106  0.200733\n",
              "Part time course  0.062533  0.064079"
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              "      <th>Full time course</th>\n",
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              "      <th>Part time course</th>\n",
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        "temp = pd.concat([\n",
        "            # percentage of observations per category, original data\n",
        "            df['education_level'].value_counts() / len(df),\n",
        "\n",
        "            # percentage of observations per category, cca data\n",
        "            new_df['education_level'].value_counts() / len(new_df)\n",
        "        ],\n",
        "        axis=1)\n",
        "\n",
        "# add column names\n",
        "temp.columns = ['original', 'cca']\n",
        "\n",
        "temp"
      ],
      "metadata": {
        "colab": {
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          "height": 206
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      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                original       cca\n",
              "Graduate        0.605387  0.619835\n",
              "Masters         0.227633  0.234082\n",
              "High School     0.105282  0.107380\n",
              "Phd             0.021610  0.022116\n",
              "Primary School  0.016077  0.016587"
            ],
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              "      <td>0.227633</td>\n",
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              "      <td>0.107380</td>\n",
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              "    <tr>\n",
              "      <th>Phd</th>\n",
              "      <td>0.021610</td>\n",
              "      <td>0.022116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Primary School</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
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        "cols"
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      "metadata": {
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        "id": "TojTNqaAWHhF",
        "outputId": "b78c2bf9-9c85-479a-a9b0-e73e11c358d3"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['city_development_index',\n",
              " 'enrolled_university',\n",
              " 'education_level',\n",
              " 'experience',\n",
              " 'training_hours']"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    }
  ]
}