Data_Science_с_Глебом_Михайловым_Pandas.ipynb

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      "provenance": [],
      "authorship_tag": "ABX9TyMSXVbaMw0Zv6g1Hfq2/SIO",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ovttiras/HDAC1-inhibitors/blob/main/Data_Science_%D1%81_%D0%93%D0%BB%D0%B5%D0%B1%D0%BE%D0%BC_%D0%9C%D0%B8%D1%85%D0%B0%D0%B9%D0%BB%D0%BE%D0%B2%D1%8B%D0%BC_Pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://drive.google.com/file/d/13ih5GsMmnB7_8AfR0U_HnUajpgx_ptYh/view?usp=sharing"
      ],
      "metadata": {
        "id": "rr41ySpz-w51"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! gdown --id 13ih5GsMmnB7_8AfR0U_HnUajpgx_ptYh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f-TG4ZHu_FRk",
        "outputId": "cb4bccc7-d448-46bf-c87d-224d49299a6b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=13ih5GsMmnB7_8AfR0U_HnUajpgx_ptYh\n",
            "To: /content/hr-analitics.zip\n",
            "100% 302k/302k [00:00<00:00, 3.83MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip /content/hr-analitics.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MqFyE9PY-xtK",
        "outputId": "74d5afdf-a3af-452d-f48f-f1ada37064ed"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/hr-analitics.zip\n",
            "replace aug_test.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "AADfRt_n4zcL"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/aug_train.csv\", )\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "zdmDbuMG5bkp",
        "outputId": "2857e004-3fd5-4d9a-a748-40ff6849a568"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       enrollee_id      city  city_development_index gender  \\\n",
              "0             8949  city_103                   0.920   Male   \n",
              "1            29725   city_40                   0.776   Male   \n",
              "2            11561   city_21                   0.624    NaN   \n",
              "3            33241  city_115                   0.789    NaN   \n",
              "4              666  city_162                   0.767   Male   \n",
              "...            ...       ...                     ...    ...   \n",
              "19153         7386  city_173                   0.878   Male   \n",
              "19154        31398  city_103                   0.920   Male   \n",
              "19155        24576  city_103                   0.920   Male   \n",
              "19156         5756   city_65                   0.802   Male   \n",
              "19157        23834   city_67                   0.855    NaN   \n",
              "\n",
              "           relevent_experience enrolled_university education_level  \\\n",
              "0      Has relevent experience       no_enrollment        Graduate   \n",
              "1       No relevent experience       no_enrollment        Graduate   \n",
              "2       No relevent experience    Full time course        Graduate   \n",
              "3       No relevent experience                 NaN        Graduate   \n",
              "4      Has relevent experience       no_enrollment         Masters   \n",
              "...                        ...                 ...             ...   \n",
              "19153   No relevent experience       no_enrollment        Graduate   \n",
              "19154  Has relevent experience       no_enrollment        Graduate   \n",
              "19155  Has relevent experience       no_enrollment        Graduate   \n",
              "19156  Has relevent experience       no_enrollment     High School   \n",
              "19157   No relevent experience       no_enrollment  Primary School   \n",
              "\n",
              "      major_discipline experience company_size    company_type last_new_job  \\\n",
              "0                 STEM        >20          NaN             NaN            1   \n",
              "1                 STEM         15        50-99         Pvt Ltd           >4   \n",
              "2                 STEM          5          NaN             NaN        never   \n",
              "3      Business Degree         <1          NaN         Pvt Ltd        never   \n",
              "4                 STEM        >20        50-99  Funded Startup            4   \n",
              "...                ...        ...          ...             ...          ...   \n",
              "19153       Humanities         14          NaN             NaN            1   \n",
              "19154             STEM         14          NaN             NaN            4   \n",
              "19155             STEM        >20        50-99         Pvt Ltd            4   \n",
              "19156              NaN         <1      500-999         Pvt Ltd            2   \n",
              "19157              NaN          2          NaN             NaN            1   \n",
              "\n",
              "       training_hours  target  \n",
              "0                  36     1.0  \n",
              "1                  47     0.0  \n",
              "2                  83     0.0  \n",
              "3                  52     1.0  \n",
              "4                   8     0.0  \n",
              "...               ...     ...  \n",
              "19153              42     1.0  \n",
              "19154              52     1.0  \n",
              "19155              44     0.0  \n",
              "19156              97     0.0  \n",
              "19157             127     0.0  \n",
              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
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              "\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nO9gjxQZymU7",
        "outputId": "61f95ed1-54c3-401f-d64b-88d3b81f6daa"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 19158 entries, 0 to 19157\n",
            "Data columns (total 14 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  19158 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  object \n",
            " 9   company_size            13220 non-null  object \n",
            " 10  company_type            13018 non-null  object \n",
            " 11  last_new_job            18735 non-null  object \n",
            " 12  training_hours          19158 non-null  int64  \n",
            " 13  target                  19158 non-null  float64\n",
            "dtypes: float64(2), int64(2), object(10)\n",
            "memory usage: 2.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['city','city_development_index']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "P7JCvtHmDPhV",
        "outputId": "6bb7b10a-45cd-4490-bf2f-a95f51c34361"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           city  city_development_index\n",
              "0      city_103                   0.920\n",
              "1       city_40                   0.776\n",
              "2       city_21                   0.624\n",
              "3      city_115                   0.789\n",
              "4      city_162                   0.767\n",
              "...         ...                     ...\n",
              "19153  city_173                   0.878\n",
              "19154  city_103                   0.920\n",
              "19155  city_103                   0.920\n",
              "19156   city_65                   0.802\n",
              "19157   city_67                   0.855\n",
              "\n",
              "[19158 rows x 2 columns]"
            ],
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              "      <td>0.802</td>\n",
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              "    <tr>\n",
              "      <th>19157</th>\n",
              "      <td>city_67</td>\n",
              "      <td>0.855</td>\n",
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              "  </tbody>\n",
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              "<p>19158 rows × 2 columns</p>\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop('city', axis=1)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "lBkKzgNKNvJE",
        "outputId": "7efc94ba-5792-4ace-d6f6-49cf852d2af2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       enrollee_id  city_development_index gender      relevent_experience  \\\n",
              "0             8949                   0.920   Male  Has relevent experience   \n",
              "1            29725                   0.776   Male   No relevent experience   \n",
              "2            11561                   0.624    NaN   No relevent experience   \n",
              "3            33241                   0.789    NaN   No relevent experience   \n",
              "4              666                   0.767   Male  Has relevent experience   \n",
              "...            ...                     ...    ...                      ...   \n",
              "19153         7386                   0.878   Male   No relevent experience   \n",
              "19154        31398                   0.920   Male  Has relevent experience   \n",
              "19155        24576                   0.920   Male  Has relevent experience   \n",
              "19156         5756                   0.802   Male  Has relevent experience   \n",
              "19157        23834                   0.855    NaN   No relevent experience   \n",
              "\n",
              "      enrolled_university education_level major_discipline experience  \\\n",
              "0           no_enrollment        Graduate             STEM        >20   \n",
              "1           no_enrollment        Graduate             STEM         15   \n",
              "2        Full time course        Graduate             STEM          5   \n",
              "3                     NaN        Graduate  Business Degree         <1   \n",
              "4           no_enrollment         Masters             STEM        >20   \n",
              "...                   ...             ...              ...        ...   \n",
              "19153       no_enrollment        Graduate       Humanities         14   \n",
              "19154       no_enrollment        Graduate             STEM         14   \n",
              "19155       no_enrollment        Graduate             STEM        >20   \n",
              "19156       no_enrollment     High School              NaN         <1   \n",
              "19157       no_enrollment  Primary School              NaN          2   \n",
              "\n",
              "      company_size    company_type last_new_job  training_hours  target  \n",
              "0              NaN             NaN            1              36     1.0  \n",
              "1            50-99         Pvt Ltd           >4              47     0.0  \n",
              "2              NaN             NaN        never              83     0.0  \n",
              "3              NaN         Pvt Ltd        never              52     1.0  \n",
              "4            50-99  Funded Startup            4               8     0.0  \n",
              "...            ...             ...          ...             ...     ...  \n",
              "19153          NaN             NaN            1              42     1.0  \n",
              "19154          NaN             NaN            4              52     1.0  \n",
              "19155        50-99         Pvt Ltd            4              44     0.0  \n",
              "19156      500-999         Pvt Ltd            2              97     0.0  \n",
              "19157          NaN             NaN            1             127     0.0  \n",
              "\n",
              "[19158 rows x 13 columns]"
            ],
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              "      <th></th>\n",
              "      <th>enrollee_id</th>\n",
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              "      <th>training_hours</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8949</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>29725</td>\n",
              "      <td>0.776</td>\n",
              "      <td>Male</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>15</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>&gt;4</td>\n",
              "      <td>47</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11561</td>\n",
              "      <td>0.624</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>Full time course</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never</td>\n",
              "      <td>83</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33241</td>\n",
              "      <td>0.789</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Business Degree</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>never</td>\n",
              "      <td>52</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>666</td>\n",
              "      <td>0.767</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Funded Startup</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>0.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19153</th>\n",
              "      <td>7386</td>\n",
              "      <td>0.878</td>\n",
              "      <td>Male</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Humanities</td>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19154</th>\n",
              "      <td>31398</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>52</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19155</th>\n",
              "      <td>24576</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>4</td>\n",
              "      <td>44</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19156</th>\n",
              "      <td>5756</td>\n",
              "      <td>0.802</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>High School</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>500-999</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>2</td>\n",
              "      <td>97</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19157</th>\n",
              "      <td>23834</td>\n",
              "      <td>0.855</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Primary School</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>127</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19158 rows × 13 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "        document.querySelector('#df-ff205048-aef1-468f-8f04-66eb7abb6c33 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-ff205048-aef1-468f-8f04-66eb7abb6c33');\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",
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              "\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-27d72724-cb59-4660-b7e6-23548aff21f0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AjEZnA7ZOuBk",
        "outputId": "aa73a81f-5b4b-4c5d-cf7b-5d53514d1503"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['enrollee_id', 'city_development_index', 'gender',\n",
              "       'relevent_experience', 'enrolled_university', 'education_level',\n",
              "       'major_discipline', 'experience', 'company_size', 'company_type',\n",
              "       'last_new_job', 'training_hours', 'target'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['new_col']=1\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "S2we-Ty7PAMQ",
        "outputId": "cf3318db-ac73-4e12-da40-5aa858d7c91f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       enrollee_id  city_development_index gender      relevent_experience  \\\n",
              "0             8949                   0.920   Male  Has relevent experience   \n",
              "1            29725                   0.776   Male   No relevent experience   \n",
              "2            11561                   0.624    NaN   No relevent experience   \n",
              "3            33241                   0.789    NaN   No relevent experience   \n",
              "4              666                   0.767   Male  Has relevent experience   \n",
              "...            ...                     ...    ...                      ...   \n",
              "19153         7386                   0.878   Male   No relevent experience   \n",
              "19154        31398                   0.920   Male  Has relevent experience   \n",
              "19155        24576                   0.920   Male  Has relevent experience   \n",
              "19156         5756                   0.802   Male  Has relevent experience   \n",
              "19157        23834                   0.855    NaN   No relevent experience   \n",
              "\n",
              "      enrolled_university education_level major_discipline experience  \\\n",
              "0           no_enrollment        Graduate             STEM        >20   \n",
              "1           no_enrollment        Graduate             STEM         15   \n",
              "2        Full time course        Graduate             STEM          5   \n",
              "3                     NaN        Graduate  Business Degree         <1   \n",
              "4           no_enrollment         Masters             STEM        >20   \n",
              "...                   ...             ...              ...        ...   \n",
              "19153       no_enrollment        Graduate       Humanities         14   \n",
              "19154       no_enrollment        Graduate             STEM         14   \n",
              "19155       no_enrollment        Graduate             STEM        >20   \n",
              "19156       no_enrollment     High School              NaN         <1   \n",
              "19157       no_enrollment  Primary School              NaN          2   \n",
              "\n",
              "      company_size    company_type last_new_job  training_hours  target  \\\n",
              "0              NaN             NaN            1              36     1.0   \n",
              "1            50-99         Pvt Ltd           >4              47     0.0   \n",
              "2              NaN             NaN        never              83     0.0   \n",
              "3              NaN         Pvt Ltd        never              52     1.0   \n",
              "4            50-99  Funded Startup            4               8     0.0   \n",
              "...            ...             ...          ...             ...     ...   \n",
              "19153          NaN             NaN            1              42     1.0   \n",
              "19154          NaN             NaN            4              52     1.0   \n",
              "19155        50-99         Pvt Ltd            4              44     0.0   \n",
              "19156      500-999         Pvt Ltd            2              97     0.0   \n",
              "19157          NaN             NaN            1             127     0.0   \n",
              "\n",
              "       new_col  \n",
              "0            1  \n",
              "1            1  \n",
              "2            1  \n",
              "3            1  \n",
              "4            1  \n",
              "...        ...  \n",
              "19153        1  \n",
              "19154        1  \n",
              "19155        1  \n",
              "19156        1  \n",
              "19157        1  \n",
              "\n",
              "[19158 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2ab62e6c-5953-4028-ba67-db6e61bb118d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>enrollee_id</th>\n",
              "      <th>city_development_index</th>\n",
              "      <th>gender</th>\n",
              "      <th>relevent_experience</th>\n",
              "      <th>enrolled_university</th>\n",
              "      <th>education_level</th>\n",
              "      <th>major_discipline</th>\n",
              "      <th>experience</th>\n",
              "      <th>company_size</th>\n",
              "      <th>company_type</th>\n",
              "      <th>last_new_job</th>\n",
              "      <th>training_hours</th>\n",
              "      <th>target</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8949</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
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              "      <td>&gt;20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>29725</td>\n",
              "      <td>0.776</td>\n",
              "      <td>Male</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>15</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>&gt;4</td>\n",
              "      <td>47</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11561</td>\n",
              "      <td>0.624</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>Full time course</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never</td>\n",
              "      <td>83</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33241</td>\n",
              "      <td>0.789</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Business Degree</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>never</td>\n",
              "      <td>52</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>666</td>\n",
              "      <td>0.767</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Funded Startup</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>0.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19153</th>\n",
              "      <td>7386</td>\n",
              "      <td>0.878</td>\n",
              "      <td>Male</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Humanities</td>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19154</th>\n",
              "      <td>31398</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>52</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19155</th>\n",
              "      <td>24576</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>4</td>\n",
              "      <td>44</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19156</th>\n",
              "      <td>5756</td>\n",
              "      <td>0.802</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>High School</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>500-999</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>2</td>\n",
              "      <td>97</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19157</th>\n",
              "      <td>23834</td>\n",
              "      <td>0.855</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Primary School</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>127</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19158 rows × 14 columns</p>\n",
              "</div>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-2ab62e6c-5953-4028-ba67-db6e61bb118d 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-2ab62e6c-5953-4028-ba67-db6e61bb118d');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-22ac7d22-bfcf-4a94-937a-069d0b8d81f2\">\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-22ac7d22-bfcf-4a94-937a-069d0b8d81f2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "KqeoxkxuQ3D7",
        "outputId": "b7a72f50-2448-434c-faed-3ecd14a196f8"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       enrollee_id  city_development_index gender      relevent_experience  \\\n",
              "0             8949                   0.920   Male  Has relevent experience   \n",
              "1            29725                   0.776   Male   No relevent experience   \n",
              "2            11561                   0.624    NaN   No relevent experience   \n",
              "3            33241                   0.789    NaN   No relevent experience   \n",
              "4              666                   0.767   Male  Has relevent experience   \n",
              "...            ...                     ...    ...                      ...   \n",
              "19153         7386                   0.878   Male   No relevent experience   \n",
              "19154        31398                   0.920   Male  Has relevent experience   \n",
              "19155        24576                   0.920   Male  Has relevent experience   \n",
              "19156         5756                   0.802   Male  Has relevent experience   \n",
              "19157        23834                   0.855    NaN   No relevent experience   \n",
              "\n",
              "      enrolled_university education_level major_discipline experience  \\\n",
              "0           no_enrollment        Graduate             STEM        >20   \n",
              "1           no_enrollment        Graduate             STEM         15   \n",
              "2        Full time course        Graduate             STEM          5   \n",
              "3                     NaN        Graduate  Business Degree         <1   \n",
              "4           no_enrollment         Masters             STEM        >20   \n",
              "...                   ...             ...              ...        ...   \n",
              "19153       no_enrollment        Graduate       Humanities         14   \n",
              "19154       no_enrollment        Graduate             STEM         14   \n",
              "19155       no_enrollment        Graduate             STEM        >20   \n",
              "19156       no_enrollment     High School              NaN         <1   \n",
              "19157       no_enrollment  Primary School              NaN          2   \n",
              "\n",
              "      company_size    company_type last_new_job  training_hours  target  \\\n",
              "0              NaN             NaN            1              36     1.0   \n",
              "1            50-99         Pvt Ltd           >4              47     0.0   \n",
              "2              NaN             NaN        never              83     0.0   \n",
              "3              NaN         Pvt Ltd        never              52     1.0   \n",
              "4            50-99  Funded Startup            4               8     0.0   \n",
              "...            ...             ...          ...             ...     ...   \n",
              "19153          NaN             NaN            1              42     1.0   \n",
              "19154          NaN             NaN            4              52     1.0   \n",
              "19155        50-99         Pvt Ltd            4              44     0.0   \n",
              "19156      500-999         Pvt Ltd            2              97     0.0   \n",
              "19157          NaN             NaN            1             127     0.0   \n",
              "\n",
              "       new_col  \n",
              "0            1  \n",
              "1            1  \n",
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              "3            1  \n",
              "4            1  \n",
              "...        ...  \n",
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              "19154        1  \n",
              "19155        1  \n",
              "19156        1  \n",
              "19157        1  \n",
              "\n",
              "[19158 rows x 14 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>enrollee_id</th>\n",
              "      <th>city_development_index</th>\n",
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              "      <th>0</th>\n",
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              "      <td>Male</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>15</td>\n",
              "      <td>50-99</td>\n",
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              "      <td>&gt;4</td>\n",
              "      <td>47</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>0.624</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
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              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33241</td>\n",
              "      <td>0.789</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Business Degree</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pvt Ltd</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>666</td>\n",
              "      <td>0.767</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Funded Startup</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
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              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19153</th>\n",
              "      <td>7386</td>\n",
              "      <td>0.878</td>\n",
              "      <td>Male</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Humanities</td>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19154</th>\n",
              "      <td>31398</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>52</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19155</th>\n",
              "      <td>24576</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>4</td>\n",
              "      <td>44</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19156</th>\n",
              "      <td>5756</td>\n",
              "      <td>0.802</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>High School</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>500-999</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>2</td>\n",
              "      <td>97</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19157</th>\n",
              "      <td>23834</td>\n",
              "      <td>0.855</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Primary School</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>127</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19158 rows × 14 columns</p>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0ace179a-0640-4350-ae2c-84c4c90597a9');\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['education_level']=='Masters']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "8AVDZYUQP3FE",
        "outputId": "5a4d6925-8608-4f07-d089-d5b1227df2a2"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       enrollee_id  city_development_index  gender      relevent_experience  \\\n",
              "4              666                   0.767    Male  Has relevent experience   \n",
              "20           31972                   0.843    Male  Has relevent experience   \n",
              "21           19061                   0.926    Male  Has relevent experience   \n",
              "22            6491                   0.804     NaN  Has relevent experience   \n",
              "40            2547                   0.926  Female  Has relevent experience   \n",
              "...            ...                     ...     ...                      ...   \n",
              "19131        18072                   0.743    Male   No relevent experience   \n",
              "19136        23672                   0.624     NaN  Has relevent experience   \n",
              "19138        19765                   0.698     NaN  Has relevent experience   \n",
              "19148         9212                   0.624     NaN  Has relevent experience   \n",
              "19149          251                   0.920    Male  Has relevent experience   \n",
              "\n",
              "      enrolled_university education_level major_discipline experience  \\\n",
              "4           no_enrollment         Masters             STEM        >20   \n",
              "20          no_enrollment         Masters             STEM         11   \n",
              "21          no_enrollment         Masters             STEM         11   \n",
              "22          no_enrollment         Masters             STEM         10   \n",
              "40       Full time course         Masters             STEM         16   \n",
              "...                   ...             ...              ...        ...   \n",
              "19131       no_enrollment         Masters             STEM          8   \n",
              "19136       no_enrollment         Masters             STEM          6   \n",
              "19138       no_enrollment         Masters             STEM         11   \n",
              "19148       no_enrollment         Masters             STEM          3   \n",
              "19149       no_enrollment         Masters             STEM          9   \n",
              "\n",
              "      company_size    company_type last_new_job  training_hours  target  \\\n",
              "4            50-99  Funded Startup            4               8     0.0   \n",
              "20         100-500         Pvt Ltd            1              68     0.0   \n",
              "21         100-500         Pvt Ltd            2              50     0.0   \n",
              "22             NaN             NaN            1              48     0.0   \n",
              "40       1000-4999   Public Sector            2              14     0.0   \n",
              "...            ...             ...          ...             ...     ...   \n",
              "19131       10000+             NaN            1              48     0.0   \n",
              "19136          NaN             NaN            1               5     1.0   \n",
              "19138      100-500         Pvt Ltd            1              17     0.0   \n",
              "19148      100-500         Pvt Ltd            3              40     1.0   \n",
              "19149        50-99         Pvt Ltd            1              36     1.0   \n",
              "\n",
              "       new_col  \n",
              "4            1  \n",
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              "22           1  \n",
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              "19138        1  \n",
              "19148        1  \n",
              "19149        1  \n",
              "\n",
              "[4361 rows x 14 columns]"
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              "      <td>0.926</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>11</td>\n",
              "      <td>100-500</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>6491</td>\n",
              "      <td>0.804</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>48</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2547</td>\n",
              "      <td>0.926</td>\n",
              "      <td>Female</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>Full time course</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>16</td>\n",
              "      <td>1000-4999</td>\n",
              "      <td>Public Sector</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>0.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19131</th>\n",
              "      <td>18072</td>\n",
              "      <td>0.743</td>\n",
              "      <td>Male</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>8</td>\n",
              "      <td>10000+</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>48</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19136</th>\n",
              "      <td>23672</td>\n",
              "      <td>0.624</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19138</th>\n",
              "      <td>19765</td>\n",
              "      <td>0.698</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>11</td>\n",
              "      <td>100-500</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19148</th>\n",
              "      <td>9212</td>\n",
              "      <td>0.624</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>3</td>\n",
              "      <td>100-500</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>3</td>\n",
              "      <td>40</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19149</th>\n",
              "      <td>251</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>9</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4361 rows × 14 columns</p>\n",
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              "        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",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gy8TRR-c2FzK"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.query('education_level==\"Masters\"')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "xT0dh99oRGg0",
        "outputId": "dc34eac3-8f43-4220-8181-2674badfeca9"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       enrollee_id  city_development_index  gender      relevent_experience  \\\n",
              "4              666                   0.767    Male  Has relevent experience   \n",
              "20           31972                   0.843    Male  Has relevent experience   \n",
              "21           19061                   0.926    Male  Has relevent experience   \n",
              "22            6491                   0.804     NaN  Has relevent experience   \n",
              "40            2547                   0.926  Female  Has relevent experience   \n",
              "...            ...                     ...     ...                      ...   \n",
              "19131        18072                   0.743    Male   No relevent experience   \n",
              "19136        23672                   0.624     NaN  Has relevent experience   \n",
              "19138        19765                   0.698     NaN  Has relevent experience   \n",
              "19148         9212                   0.624     NaN  Has relevent experience   \n",
              "19149          251                   0.920    Male  Has relevent experience   \n",
              "\n",
              "      enrolled_university education_level major_discipline experience  \\\n",
              "4           no_enrollment         Masters             STEM        >20   \n",
              "20          no_enrollment         Masters             STEM         11   \n",
              "21          no_enrollment         Masters             STEM         11   \n",
              "22          no_enrollment         Masters             STEM         10   \n",
              "40       Full time course         Masters             STEM         16   \n",
              "...                   ...             ...              ...        ...   \n",
              "19131       no_enrollment         Masters             STEM          8   \n",
              "19136       no_enrollment         Masters             STEM          6   \n",
              "19138       no_enrollment         Masters             STEM         11   \n",
              "19148       no_enrollment         Masters             STEM          3   \n",
              "19149       no_enrollment         Masters             STEM          9   \n",
              "\n",
              "      company_size    company_type last_new_job  training_hours  target  \\\n",
              "4            50-99  Funded Startup            4               8     0.0   \n",
              "20         100-500         Pvt Ltd            1              68     0.0   \n",
              "21         100-500         Pvt Ltd            2              50     0.0   \n",
              "22             NaN             NaN            1              48     0.0   \n",
              "40       1000-4999   Public Sector            2              14     0.0   \n",
              "...            ...             ...          ...             ...     ...   \n",
              "19131       10000+             NaN            1              48     0.0   \n",
              "19136          NaN             NaN            1               5     1.0   \n",
              "19138      100-500         Pvt Ltd            1              17     0.0   \n",
              "19148      100-500         Pvt Ltd            3              40     1.0   \n",
              "19149        50-99         Pvt Ltd            1              36     1.0   \n",
              "\n",
              "       new_col  \n",
              "4            1  \n",
              "20           1  \n",
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              "22           1  \n",
              "40           1  \n",
              "...        ...  \n",
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              "19138        1  \n",
              "19148        1  \n",
              "19149        1  \n",
              "\n",
              "[4361 rows x 14 columns]"
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              "      <th>4</th>\n",
              "      <td>666</td>\n",
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              "      <th>21</th>\n",
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              "      <td>Has relevent experience</td>\n",
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              "      <td>50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>6491</td>\n",
              "      <td>0.804</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>48</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2547</td>\n",
              "      <td>0.926</td>\n",
              "      <td>Female</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>Full time course</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>16</td>\n",
              "      <td>1000-4999</td>\n",
              "      <td>Public Sector</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>0.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19131</th>\n",
              "      <td>18072</td>\n",
              "      <td>0.743</td>\n",
              "      <td>Male</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>8</td>\n",
              "      <td>10000+</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>48</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19136</th>\n",
              "      <td>23672</td>\n",
              "      <td>0.624</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19138</th>\n",
              "      <td>19765</td>\n",
              "      <td>0.698</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>11</td>\n",
              "      <td>100-500</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19148</th>\n",
              "      <td>9212</td>\n",
              "      <td>0.624</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>3</td>\n",
              "      <td>100-500</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>3</td>\n",
              "      <td>40</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19149</th>\n",
              "      <td>251</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>9</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4361 rows × 14 columns</p>\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[(df['education_level']=='Masters')& (df['gender']=='Male')]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "ZGu2oIrGRtp7",
        "outputId": "8d3474cd-bf62-4a2d-bb88-1657f00ba350"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       enrollee_id  city_development_index gender      relevent_experience  \\\n",
              "4              666                   0.767   Male  Has relevent experience   \n",
              "20           31972                   0.843   Male  Has relevent experience   \n",
              "21           19061                   0.926   Male  Has relevent experience   \n",
              "44            5590                   0.624   Male  Has relevent experience   \n",
              "63            8433                   0.887   Male  Has relevent experience   \n",
              "...            ...                     ...    ...                      ...   \n",
              "19112         2225                   0.897   Male  Has relevent experience   \n",
              "19117         3458                   0.926   Male  Has relevent experience   \n",
              "19121         1940                   0.895   Male  Has relevent experience   \n",
              "19131        18072                   0.743   Male   No relevent experience   \n",
              "19149          251                   0.920   Male  Has relevent experience   \n",
              "\n",
              "      enrolled_university education_level major_discipline experience  \\\n",
              "4           no_enrollment         Masters             STEM        >20   \n",
              "20          no_enrollment         Masters             STEM         11   \n",
              "21          no_enrollment         Masters             STEM         11   \n",
              "44       Part time course         Masters             STEM          9   \n",
              "63          no_enrollment         Masters       Humanities        >20   \n",
              "...                   ...             ...              ...        ...   \n",
              "19112       no_enrollment         Masters             STEM         12   \n",
              "19117       no_enrollment         Masters             STEM        >20   \n",
              "19121       no_enrollment         Masters             STEM        >20   \n",
              "19131       no_enrollment         Masters             STEM          8   \n",
              "19149       no_enrollment         Masters             STEM          9   \n",
              "\n",
              "      company_size    company_type last_new_job  training_hours  target  \\\n",
              "4            50-99  Funded Startup            4               8     0.0   \n",
              "20         100-500         Pvt Ltd            1              68     0.0   \n",
              "21         100-500         Pvt Ltd            2              50     0.0   \n",
              "44       5000-9999         Pvt Ltd            3               7     1.0   \n",
              "63         100-500         Pvt Ltd           >4              48     0.0   \n",
              "...            ...             ...          ...             ...     ...   \n",
              "19112        10/49         Pvt Ltd            3              55     0.0   \n",
              "19117          NaN             NaN           >4             214     0.0   \n",
              "19121      100-500         Pvt Ltd            1               4     0.0   \n",
              "19131       10000+             NaN            1              48     0.0   \n",
              "19149        50-99         Pvt Ltd            1              36     1.0   \n",
              "\n",
              "       new_col  \n",
              "4            1  \n",
              "20           1  \n",
              "21           1  \n",
              "44           1  \n",
              "63           1  \n",
              "...        ...  \n",
              "19112        1  \n",
              "19117        1  \n",
              "19121        1  \n",
              "19131        1  \n",
              "19149        1  \n",
              "\n",
              "[2957 rows x 14 columns]"
            ],
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              "      <td>Has relevent experience</td>\n",
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              "      <th>44</th>\n",
              "      <td>5590</td>\n",
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              "      <td>Has relevent experience</td>\n",
              "      <td>Part time course</td>\n",
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              "      <td>Pvt Ltd</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>8433</td>\n",
              "      <td>0.887</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>Humanities</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>100-500</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>&gt;4</td>\n",
              "      <td>48</td>\n",
              "      <td>0.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19112</th>\n",
              "      <td>2225</td>\n",
              "      <td>0.897</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>12</td>\n",
              "      <td>10/49</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>3</td>\n",
              "      <td>55</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19117</th>\n",
              "      <td>3458</td>\n",
              "      <td>0.926</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&gt;4</td>\n",
              "      <td>214</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19121</th>\n",
              "      <td>1940</td>\n",
              "      <td>0.895</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>100-500</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19131</th>\n",
              "      <td>18072</td>\n",
              "      <td>0.743</td>\n",
              "      <td>Male</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>8</td>\n",
              "      <td>10000+</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>48</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19149</th>\n",
              "      <td>251</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>9</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2957 rows × 14 columns</p>\n",
              "</div>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-46682314-8278-451b-9f6d-f0d295b7d5b6');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
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              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "MtsI6WHrDzL6",
        "outputId": "b8a58365-0740-4d24-f04c-763726cae437"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       enrollee_id  city_development_index gender      relevent_experience  \\\n",
              "0             8949                   0.920   Male  Has relevent experience   \n",
              "1            29725                   0.776   Male   No relevent experience   \n",
              "2            11561                   0.624    NaN   No relevent experience   \n",
              "3            33241                   0.789    NaN   No relevent experience   \n",
              "4              666                   0.767   Male  Has relevent experience   \n",
              "...            ...                     ...    ...                      ...   \n",
              "19153         7386                   0.878   Male   No relevent experience   \n",
              "19154        31398                   0.920   Male  Has relevent experience   \n",
              "19155        24576                   0.920   Male  Has relevent experience   \n",
              "19156         5756                   0.802   Male  Has relevent experience   \n",
              "19157        23834                   0.855    NaN   No relevent experience   \n",
              "\n",
              "      enrolled_university education_level major_discipline experience  \\\n",
              "0           no_enrollment        Graduate             STEM        >20   \n",
              "1           no_enrollment        Graduate             STEM         15   \n",
              "2        Full time course        Graduate             STEM          5   \n",
              "3                     NaN        Graduate  Business Degree         <1   \n",
              "4           no_enrollment         Masters             STEM        >20   \n",
              "...                   ...             ...              ...        ...   \n",
              "19153       no_enrollment        Graduate       Humanities         14   \n",
              "19154       no_enrollment        Graduate             STEM         14   \n",
              "19155       no_enrollment        Graduate             STEM        >20   \n",
              "19156       no_enrollment     High School              NaN         <1   \n",
              "19157       no_enrollment  Primary School              NaN          2   \n",
              "\n",
              "      company_size    company_type last_new_job  training_hours  target  \\\n",
              "0              NaN             NaN            1              36     1.0   \n",
              "1            50-99         Pvt Ltd           >4              47     0.0   \n",
              "2              NaN             NaN        never              83     0.0   \n",
              "3              NaN         Pvt Ltd        never              52     1.0   \n",
              "4            50-99  Funded Startup            4               8     0.0   \n",
              "...            ...             ...          ...             ...     ...   \n",
              "19153          NaN             NaN            1              42     1.0   \n",
              "19154          NaN             NaN            4              52     1.0   \n",
              "19155        50-99         Pvt Ltd            4              44     0.0   \n",
              "19156      500-999         Pvt Ltd            2              97     0.0   \n",
              "19157          NaN             NaN            1             127     0.0   \n",
              "\n",
              "       new_col  \n",
              "0            1  \n",
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              "...        ...  \n",
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              "19156        1  \n",
              "19157        1  \n",
              "\n",
              "[19158 rows x 14 columns]"
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              "      <th></th>\n",
              "      <th>enrollee_id</th>\n",
              "      <th>city_development_index</th>\n",
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              "      <th>2</th>\n",
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              "      <td>1</td>\n",
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              "      <th>4</th>\n",
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              "      <td>Funded Startup</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>0.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19153</th>\n",
              "      <td>7386</td>\n",
              "      <td>0.878</td>\n",
              "      <td>Male</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Humanities</td>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19154</th>\n",
              "      <td>31398</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>52</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19155</th>\n",
              "      <td>24576</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>4</td>\n",
              "      <td>44</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19156</th>\n",
              "      <td>5756</td>\n",
              "      <td>0.802</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>High School</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>500-999</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>2</td>\n",
              "      <td>97</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19157</th>\n",
              "      <td>23834</td>\n",
              "      <td>0.855</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Primary School</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>127</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19158 rows × 14 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a270eb82-4839-4f9d-bb19-58e6233a47f7')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-a270eb82-4839-4f9d-bb19-58e6233a47f7 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-a270eb82-4839-4f9d-bb19-58e6233a47f7');\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",
              "\n",
              "\n",
              "<div id=\"df-660c02a6-abab-49e8-8fb4-e9038d2115c2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-660c02a6-abab-49e8-8fb4-e9038d2115c2')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-660c02a6-abab-49e8-8fb4-e9038d2115c2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().mean().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cih7Q3NOEn4a",
        "outputId": "811fd7ba-e660-479b-ddb8-efdac41baba0"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "company_type              0.320493\n",
              "company_size              0.309949\n",
              "gender                    0.235306\n",
              "major_discipline          0.146832\n",
              "education_level           0.024011\n",
              "last_new_job              0.022080\n",
              "enrolled_university       0.020148\n",
              "experience                0.003393\n",
              "enrollee_id               0.000000\n",
              "city_development_index    0.000000\n",
              "relevent_experience       0.000000\n",
              "training_hours            0.000000\n",
              "target                    0.000000\n",
              "new_col                   0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "XKtVKET1F88S",
        "outputId": "11e31853-e368-4475-af15-1554f26dc7fc"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        enrollee_id  city_development_index  training_hours        target  \\\n",
              "count  19158.000000            19158.000000    19158.000000  19158.000000   \n",
              "mean   16875.358179                0.828848       65.366896      0.249348   \n",
              "std     9616.292592                0.123362       60.058462      0.432647   \n",
              "min        1.000000                0.448000        1.000000      0.000000   \n",
              "25%     8554.250000                0.740000       23.000000      0.000000   \n",
              "50%    16982.500000                0.903000       47.000000      0.000000   \n",
              "75%    25169.750000                0.920000       88.000000      0.000000   \n",
              "max    33380.000000                0.949000      336.000000      1.000000   \n",
              "\n",
              "       new_col  \n",
              "count  19158.0  \n",
              "mean       1.0  \n",
              "std        0.0  \n",
              "min        1.0  \n",
              "25%        1.0  \n",
              "50%        1.0  \n",
              "75%        1.0  \n",
              "max        1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e1d98b8-5cb1-494b-b4a2-9485e74116a8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>enrollee_id</th>\n",
              "      <th>city_development_index</th>\n",
              "      <th>training_hours</th>\n",
              "      <th>target</th>\n",
              "      <th>new_col</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>19158.000000</td>\n",
              "      <td>19158.000000</td>\n",
              "      <td>19158.000000</td>\n",
              "      <td>19158.000000</td>\n",
              "      <td>19158.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>16875.358179</td>\n",
              "      <td>0.828848</td>\n",
              "      <td>65.366896</td>\n",
              "      <td>0.249348</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9616.292592</td>\n",
              "      <td>0.123362</td>\n",
              "      <td>60.058462</td>\n",
              "      <td>0.432647</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.448000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8554.250000</td>\n",
              "      <td>0.740000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>16982.500000</td>\n",
              "      <td>0.903000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>25169.750000</td>\n",
              "      <td>0.920000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>33380.000000</td>\n",
              "      <td>0.949000</td>\n",
              "      <td>336.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e1d98b8-5cb1-494b-b4a2-9485e74116a8')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\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-6e1d98b8-5cb1-494b-b4a2-9485e74116a8 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-6e1d98b8-5cb1-494b-b4a2-9485e74116a8');\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",
              "\n",
              "\n",
              "<div id=\"df-6dc8d280-4351-4c8a-b2b9-296207e64b00\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6dc8d280-4351-4c8a-b2b9-296207e64b00')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6dc8d280-4351-4c8a-b2b9-296207e64b00 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['city_development_index'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "Ve5hU6VxGjiK",
        "outputId": "ef36c186-a134-4f2a-a8bf-2cf98763c404"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "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['gender'].value_counts(dropna=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LvpQiX09H12T",
        "outputId": "e18df343-a0cd-4683-cbcc-b843a739bd23"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male      13221\n",
              "NaN        4508\n",
              "Female     1238\n",
              "Other       191\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "q=df['city_development_index'].quantile(0.25)\n",
        "q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "azEXLNBpJEQi",
        "outputId": "8a5cccd1-dd0b-4c57-caf1-149c70a6937d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.74"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['city_development_index']<q]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "fm3-7rilJjLy",
        "outputId": "43825355-868b-406a-8a15-8a990fb900a8"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       enrollee_id  city_development_index gender      relevent_experience  \\\n",
              "2            11561                   0.624    NaN   No relevent experience   \n",
              "10           29452                   0.624    NaN   No relevent experience   \n",
              "13            5826                   0.624   Male   No relevent experience   \n",
              "14            8722                   0.624    NaN   No relevent experience   \n",
              "17            5764                   0.624    NaN  Has relevent experience   \n",
              "...            ...                     ...    ...                      ...   \n",
              "19144        17191                   0.624    NaN   No relevent experience   \n",
              "19145          155                   0.725    NaN   No relevent experience   \n",
              "19147        21319                   0.624   Male   No relevent experience   \n",
              "19148         9212                   0.624    NaN  Has relevent experience   \n",
              "19151        11385                   0.689   Male   No relevent experience   \n",
              "\n",
              "      enrolled_university education_level major_discipline experience  \\\n",
              "2        Full time course        Graduate             STEM          5   \n",
              "10       Full time course     High School              NaN          2   \n",
              "13                    NaN             NaN              NaN          2   \n",
              "14       Full time course     High School              NaN          5   \n",
              "17          no_enrollment        Graduate             STEM          2   \n",
              "...                   ...             ...              ...        ...   \n",
              "19144    Full time course        Graduate             STEM          4   \n",
              "19145    Full time course        Graduate             STEM          5   \n",
              "19147    Full time course        Graduate             STEM          1   \n",
              "19148       no_enrollment         Masters             STEM          3   \n",
              "19151    Full time course        Graduate              NaN          2   \n",
              "\n",
              "      company_size company_type last_new_job  training_hours  target  new_col  \n",
              "2              NaN          NaN        never              83     0.0        1  \n",
              "10             NaN          NaN        never              32     1.0        1  \n",
              "13             NaN          NaN        never              24     0.0        1  \n",
              "14             NaN          NaN        never              26     0.0        1  \n",
              "17       5000-9999      Pvt Ltd            2               7     0.0        1  \n",
              "...            ...          ...          ...             ...     ...      ...  \n",
              "19144          NaN          NaN        never              48     1.0        1  \n",
              "19145          NaN      Pvt Ltd        never             190     0.0        1  \n",
              "19147      100-500      Pvt Ltd            1              52     1.0        1  \n",
              "19148      100-500      Pvt Ltd            3              40     1.0        1  \n",
              "19151          NaN          NaN            1              60     0.0        1  \n",
              "\n",
              "[4771 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
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              "      <td>NaN</td>\n",
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              "      <td>...</td>\n",
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              "      <td>0.725</td>\n",
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              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pvt Ltd</td>\n",
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              "      <td>190</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>19147</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>19148</th>\n",
              "      <td>9212</td>\n",
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              "      <td>3</td>\n",
              "      <td>100-500</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>19151</th>\n",
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              "\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "CSq1fqowa0i5",
        "outputId": "ff0bcdd6-323a-4fb5-9433-97b611d9438a"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       enrollee_id  city_development_index gender      relevent_experience  \\\n",
              "0             8949                   0.920   Male  Has relevent experience   \n",
              "1            29725                   0.776   Male   No relevent experience   \n",
              "2            11561                   0.624    NaN   No relevent experience   \n",
              "3            33241                   0.789    NaN   No relevent experience   \n",
              "4              666                   0.767   Male  Has relevent experience   \n",
              "...            ...                     ...    ...                      ...   \n",
              "19153         7386                   0.878   Male   No relevent experience   \n",
              "19154        31398                   0.920   Male  Has relevent experience   \n",
              "19155        24576                   0.920   Male  Has relevent experience   \n",
              "19156         5756                   0.802   Male  Has relevent experience   \n",
              "19157        23834                   0.855    NaN   No relevent experience   \n",
              "\n",
              "      enrolled_university education_level major_discipline experience  \\\n",
              "0           no_enrollment        Graduate             STEM        >20   \n",
              "1           no_enrollment        Graduate             STEM         15   \n",
              "2        Full time course        Graduate             STEM          5   \n",
              "3                     NaN        Graduate  Business Degree         <1   \n",
              "4           no_enrollment         Masters             STEM        >20   \n",
              "...                   ...             ...              ...        ...   \n",
              "19153       no_enrollment        Graduate       Humanities         14   \n",
              "19154       no_enrollment        Graduate             STEM         14   \n",
              "19155       no_enrollment        Graduate             STEM        >20   \n",
              "19156       no_enrollment     High School              NaN         <1   \n",
              "19157       no_enrollment  Primary School              NaN          2   \n",
              "\n",
              "      company_size    company_type last_new_job  training_hours  target  \\\n",
              "0              NaN             NaN            1              36     1.0   \n",
              "1            50-99         Pvt Ltd           >4              47     0.0   \n",
              "2              NaN             NaN        never              83     0.0   \n",
              "3              NaN         Pvt Ltd        never              52     1.0   \n",
              "4            50-99  Funded Startup            4               8     0.0   \n",
              "...            ...             ...          ...             ...     ...   \n",
              "19153          NaN             NaN            1              42     1.0   \n",
              "19154          NaN             NaN            4              52     1.0   \n",
              "19155        50-99         Pvt Ltd            4              44     0.0   \n",
              "19156      500-999         Pvt Ltd            2              97     0.0   \n",
              "19157          NaN             NaN            1             127     0.0   \n",
              "\n",
              "       new_col  \n",
              "0            1  \n",
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              "...        ...  \n",
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              "19157        1  \n",
              "\n",
              "[19158 rows x 14 columns]"
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              "      <th>city_development_index</th>\n",
              "      <th>gender</th>\n",
              "      <th>relevent_experience</th>\n",
              "      <th>enrolled_university</th>\n",
              "      <th>education_level</th>\n",
              "      <th>major_discipline</th>\n",
              "      <th>experience</th>\n",
              "      <th>company_size</th>\n",
              "      <th>company_type</th>\n",
              "      <th>last_new_job</th>\n",
              "      <th>training_hours</th>\n",
              "      <th>target</th>\n",
              "      <th>new_col</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8949</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>29725</td>\n",
              "      <td>0.776</td>\n",
              "      <td>Male</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>15</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>&gt;4</td>\n",
              "      <td>47</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11561</td>\n",
              "      <td>0.624</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>Full time course</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never</td>\n",
              "      <td>83</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33241</td>\n",
              "      <td>0.789</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Business Degree</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>never</td>\n",
              "      <td>52</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>666</td>\n",
              "      <td>0.767</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Funded Startup</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>0.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19153</th>\n",
              "      <td>7386</td>\n",
              "      <td>0.878</td>\n",
              "      <td>Male</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Humanities</td>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19154</th>\n",
              "      <td>31398</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>52</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19155</th>\n",
              "      <td>24576</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>4</td>\n",
              "      <td>44</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19156</th>\n",
              "      <td>5756</td>\n",
              "      <td>0.802</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>High School</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>500-999</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>2</td>\n",
              "      <td>97</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19157</th>\n",
              "      <td>23834</td>\n",
              "      <td>0.855</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Primary School</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>127</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19158 rows × 14 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38b30669-5800-4a5f-8b75-6956e2cb1fce')\"\n",
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              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "        document.querySelector('#df-38b30669-5800-4a5f-8b75-6956e2cb1fce button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-ab50be80-625a-4ead-95ac-898eb5dc201f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ab50be80-625a-4ead-95ac-898eb5dc201f')\"\n",
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              "            style=\"display:none;\">\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ab50be80-625a-4ead-95ac-898eb5dc201f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['target'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "igPxj5dDb9Tp",
        "outputId": "4177aacf-2e7f-4e71-c85c-157e34402665"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.24934753105752167"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('gender', dropna=False)['target'].agg(['count', 'mean'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FyqhMG-vcvq1",
        "outputId": "62246a8d-b981-411a-c38f-3251023126c5"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        count      mean\n",
              "gender                 \n",
              "Female   1238  0.263328\n",
              "Male    13221  0.227819\n",
              "Other     191  0.261780\n",
              "NaN      4508  0.308119"
            ],
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              "      <td>1238</td>\n",
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              "    .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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-95c02161-6f5c-43db-a5db-b801f4139562 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-95c02161-6f5c-43db-a5db-b801f4139562');\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",
              "\n",
              "\n",
              "<div id=\"df-6df7cb21-d5ea-4aeb-a622-c357aebabd09\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6df7cb21-d5ea-4aeb-a622-c357aebabd09')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6df7cb21-d5ea-4aeb-a622-c357aebabd09 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('gender')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nU_BfjzLcvzN",
        "outputId": "37b1aa8f-605b-4977-d383-1dead0cdd395"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7cb011a81990>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('education_level', dropna=False)['target'].agg(['count', 'mean'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "V_6WCiNLmU0D",
        "outputId": "63880383-1e61-4f2c-dba3-8e526b45ba23"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 count      mean\n",
              "education_level                 \n",
              "Graduate         11598  0.279790\n",
              "High School       2017  0.195340\n",
              "Masters           4361  0.214400\n",
              "Phd                414  0.140097\n",
              "Primary School     308  0.133117\n",
              "NaN                460  0.226087"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-21fdeaf4-dad3-41e6-ba9e-eae0f27fe555\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_level</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Graduate</th>\n",
              "      <td>11598</td>\n",
              "      <td>0.279790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High School</th>\n",
              "      <td>2017</td>\n",
              "      <td>0.195340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Masters</th>\n",
              "      <td>4361</td>\n",
              "      <td>0.214400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Phd</th>\n",
              "      <td>414</td>\n",
              "      <td>0.140097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Primary School</th>\n",
              "      <td>308</td>\n",
              "      <td>0.133117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>460</td>\n",
              "      <td>0.226087</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21fdeaf4-dad3-41e6-ba9e-eae0f27fe555')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-21fdeaf4-dad3-41e6-ba9e-eae0f27fe555 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-21fdeaf4-dad3-41e6-ba9e-eae0f27fe555');\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",
              "\n",
              "\n",
              "<div id=\"df-54c546b6-423a-4e1b-bf82-b49123fe9519\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-54c546b6-423a-4e1b-bf82-b49123fe9519')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-54c546b6-423a-4e1b-bf82-b49123fe9519 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['city_development_index_group']=pd.cut(df['city_development_index'], 5)"
      ],
      "metadata": {
        "id": "JYmdHNBEnQ4L"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('city_development_index_group', dropna=False)['target'].agg(['count', 'mean'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "jPybJlKEn7-B",
        "outputId": "c95cad73-7bc9-47d7-faea-f481634863e1"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              count      mean\n",
              "city_development_index_group                 \n",
              "(0.447, 0.548]                  178  0.578652\n",
              "(0.548, 0.648]                 3297  0.582651\n",
              "(0.648, 0.749]                 1529  0.273381\n",
              "(0.749, 0.849]                 1949  0.194459\n",
              "(0.849, 0.949]                12205  0.160262"
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['city_development_index_group_q']=pd.qcut(df['city_development_index'], 5, duplicates='drop')"
      ],
      "metadata": {
        "id": "BAcUTQJvoYxp"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('city_development_index_group_q', dropna=False)['target'].agg(['count', 'mean'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ftGU0PEroxAa",
        "outputId": "0553558f-92bd-44a5-b193-8175ef707fb8"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                count      mean\n",
              "city_development_index_group_q                 \n",
              "(0.447, 0.691]                   3869  0.550530\n",
              "(0.691, 0.878]                   3827  0.206428\n",
              "(0.878, 0.92]                    8925  0.178375\n",
              "(0.92, 0.949]                    2537  0.104454"
            ],
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "    }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-aa7ad80d-34d8-4c3b-9570-5dadad18ad70 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['city_development_index_group_custom']=pd.cut(df['city_development_index'], [0, 0.1, 0.2, 0.5, float('inf')])"
      ],
      "metadata": {
        "id": "THGHFfndpin5"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('city_development_index_group_custom', dropna=False)['target'].agg(['count', 'mean'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "WTr1lucl0AU-",
        "outputId": "04d3255c-ce7b-46e6-b9c5-bdc728d0cd11"
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      "execution_count": 45,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                                     count      mean\n",
              "city_development_index_group_custom                 \n",
              "(0.0, 0.1]                               0       NaN\n",
              "(0.1, 0.2]                               0       NaN\n",
              "(0.2, 0.5]                              63  0.587302\n",
              "(0.5, inf]                           19095  0.248233"
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('education_level', dropna=False)['target'].agg(['count', 'mean'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "EpapHVLQ0hDo",
        "outputId": "c5b88eb7-adea-4155-b4c6-38da03c79ac4"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 count      mean\n",
              "education_level                 \n",
              "Graduate         11598  0.279790\n",
              "High School       2017  0.195340\n",
              "Masters           4361  0.214400\n",
              "Phd                414  0.140097\n",
              "Primary School     308  0.133117\n",
              "NaN                460  0.226087"
            ],
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a4222ba2-0143-452c-9b2e-5d290caef761 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def education_group(x):\n",
        "  if x in ['High School', 'Primary School']:\n",
        "    return 'School'\n",
        "  if x in ['Masters', 'Phd']:\n",
        "    return 'Masters_and_phd'\n",
        "  return x"
      ],
      "metadata": {
        "id": "Vi4su8rs1BwX"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['new_education_level']=df['education_level'].apply(education_group)"
      ],
      "metadata": {
        "id": "Sf3ZAXQJ2GRH"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('new_education_level', dropna=False)['target'].agg(['count', 'mean'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "57Cfz8FR2dwK",
        "outputId": "3ddf8e95-d8cc-4b42-b870-cfa8d11bb480"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     count      mean\n",
              "new_education_level                 \n",
              "Graduate             11598  0.279790\n",
              "Masters_and_phd       4775  0.207958\n",
              "School                2325  0.187097\n",
              "NaN                    460  0.226087"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3dc53782-fc2e-4dd8-ae7c-82c99bcf0c3e\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new_education_level</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Graduate</th>\n",
              "      <td>11598</td>\n",
              "      <td>0.279790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Masters_and_phd</th>\n",
              "      <td>4775</td>\n",
              "      <td>0.207958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>School</th>\n",
              "      <td>2325</td>\n",
              "      <td>0.187097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>460</td>\n",
              "      <td>0.226087</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3dc53782-fc2e-4dd8-ae7c-82c99bcf0c3e')\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [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-3dc53782-fc2e-4dd8-ae7c-82c99bcf0c3e 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-3dc53782-fc2e-4dd8-ae7c-82c99bcf0c3e');\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",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8ce6440d-1f7d-4410-bdaa-69a9422031ad button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['education_level'].replace('Primary School', 'School')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TV339JFn3D-m",
        "outputId": "7da31ec6-ef46-437a-b1b4-cf38ee147f2e"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0           Graduate\n",
              "1           Graduate\n",
              "2           Graduate\n",
              "3           Graduate\n",
              "4            Masters\n",
              "            ...     \n",
              "19153       Graduate\n",
              "19154       Graduate\n",
              "19155       Graduate\n",
              "19156    High School\n",
              "19157         School\n",
              "Name: education_level, Length: 19158, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def new_training_hours(row):\n",
        "  if row['education_level']=='Phd':\n",
        "    return row['training_hours']+1000\n",
        "  return row['training_hours']"
      ],
      "metadata": {
        "id": "qXptsGF96cGP"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['new_training_hours'] = df.apply(new_training_hours, axis=1)"
      ],
      "metadata": {
        "id": "EauL9hHe7Xkw"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['education_level']=='Phd']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "bq9DGIku7_dv",
        "outputId": "8311718d-9b3d-400d-82b9-0d77567555eb"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       enrollee_id  city_development_index gender      relevent_experience  \\\n",
              "37           10164                   0.926   Male  Has relevent experience   \n",
              "121          13915                   0.910   Male  Has relevent experience   \n",
              "130           3921                   0.893    NaN   No relevent experience   \n",
              "135          23947                   0.920    NaN   No relevent experience   \n",
              "152          31786                   0.920   Male   No relevent experience   \n",
              "...            ...                     ...    ...                      ...   \n",
              "18738         1477                   0.767   Male  Has relevent experience   \n",
              "18800        18365                   0.920   Male  Has relevent experience   \n",
              "18807          229                   0.926   Male   No relevent experience   \n",
              "18944         2678                   0.924    NaN   No relevent experience   \n",
              "19044        14441                   0.925   Male  Has relevent experience   \n",
              "\n",
              "      enrolled_university education_level major_discipline experience  \\\n",
              "37          no_enrollment             Phd             STEM        >20   \n",
              "121         no_enrollment             Phd             STEM        >20   \n",
              "130         no_enrollment             Phd             STEM        >20   \n",
              "135         no_enrollment             Phd             STEM        NaN   \n",
              "152         no_enrollment             Phd             STEM        >20   \n",
              "...                   ...             ...              ...        ...   \n",
              "18738       no_enrollment             Phd             STEM        >20   \n",
              "18800       no_enrollment             Phd             STEM         15   \n",
              "18807       no_enrollment             Phd             STEM         14   \n",
              "18944       no_enrollment             Phd             STEM        >20   \n",
              "19044       no_enrollment             Phd             STEM          6   \n",
              "\n",
              "      company_size         company_type last_new_job  training_hours  target  \\\n",
              "37         100-500              Pvt Ltd            4              42     1.0   \n",
              "121        100-500              Pvt Ltd            2              20     1.0   \n",
              "130      1000-4999        Public Sector           >4               4     0.0   \n",
              "135            NaN                  NaN          NaN              70     0.0   \n",
              "152          50-99              Pvt Ltd           >4              14     0.0   \n",
              "...            ...                  ...          ...             ...     ...   \n",
              "18738       10000+              Pvt Ltd            1              34     0.0   \n",
              "18800        50-99       Funded Startup            4             108     0.0   \n",
              "18807    5000-9999        Public Sector           >4               9     1.0   \n",
              "18944        50-99              Pvt Ltd          NaN              27     0.0   \n",
              "19044        50-99  Early Stage Startup            2             105     0.0   \n",
              "\n",
              "       new_col city_development_index_group city_development_index_group_q  \\\n",
              "37           1               (0.849, 0.949]                  (0.92, 0.949]   \n",
              "121          1               (0.849, 0.949]                  (0.878, 0.92]   \n",
              "130          1               (0.849, 0.949]                  (0.878, 0.92]   \n",
              "135          1               (0.849, 0.949]                  (0.878, 0.92]   \n",
              "152          1               (0.849, 0.949]                  (0.878, 0.92]   \n",
              "...        ...                          ...                            ...   \n",
              "18738        1               (0.749, 0.849]                 (0.691, 0.878]   \n",
              "18800        1               (0.849, 0.949]                  (0.878, 0.92]   \n",
              "18807        1               (0.849, 0.949]                  (0.92, 0.949]   \n",
              "18944        1               (0.849, 0.949]                  (0.92, 0.949]   \n",
              "19044        1               (0.849, 0.949]                  (0.92, 0.949]   \n",
              "\n",
              "      city_development_index_group_custom new_education_level  \\\n",
              "37                             (0.5, inf]     Masters_and_phd   \n",
              "121                            (0.5, inf]     Masters_and_phd   \n",
              "130                            (0.5, inf]     Masters_and_phd   \n",
              "135                            (0.5, inf]     Masters_and_phd   \n",
              "152                            (0.5, inf]     Masters_and_phd   \n",
              "...                                   ...                 ...   \n",
              "18738                          (0.5, inf]     Masters_and_phd   \n",
              "18800                          (0.5, inf]     Masters_and_phd   \n",
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              "18944                          (0.5, inf]     Masters_and_phd   \n",
              "19044                          (0.5, inf]     Masters_and_phd   \n",
              "\n",
              "       new_training_hours  \n",
              "37                   1042  \n",
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              "130                  1004  \n",
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              "152                  1014  \n",
              "...                   ...  \n",
              "18738                1034  \n",
              "18800                1108  \n",
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              "19044                1105  \n",
              "\n",
              "[414 rows x 19 columns]"
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              "      <td>1105</td>\n",
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              "<p>414 rows × 19 columns</p>\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        document.querySelector('#df-5c49ea66-78ab-43b4-a293-43ecf9df482e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('education_level', dropna=False)['target'].agg(['count', 'mean'])"
      ],
      "metadata": {
        "id": "WwCuZSLe_g2n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "493c6236-601b-4f75-89ae-9e818afe71e5"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 count      mean\n",
              "education_level                 \n",
              "Graduate         11598  0.279790\n",
              "High School       2017  0.195340\n",
              "Masters           4361  0.214400\n",
              "Phd                414  0.140097\n",
              "Primary School     308  0.133117\n",
              "NaN                460  0.226087"
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              "\n",
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              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
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              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
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          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t=df.pivot_table(index='education_level', values='target', aggfunc=['count', 'mean'])"
      ],
      "metadata": {
        "id": "xxxOZkk0_qkW"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
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        "t"
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          "base_uri": "https://localhost:8080/",
          "height": 269
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        "id": "bs5GomMxAgo2",
        "outputId": "0d5bce0f-61ef-4892-cfee-eb97e0e5d78a"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 count      mean\n",
              "                target    target\n",
              "education_level                 \n",
              "Graduate         11598  0.279790\n",
              "High School       2017  0.195340\n",
              "Masters           4361  0.214400\n",
              "Phd                414  0.140097\n",
              "Primary School     308  0.133117"
            ],
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              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3abc08ed-8506-49f9-9252-9b98196a5228 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t.reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "_MIJmgfbAiTN",
        "outputId": "670b57a6-5e3b-4a53-d469-5589d411fc9f"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  education_level  count      mean\n",
              "                  target    target\n",
              "0        Graduate  11598  0.279790\n",
              "1     High School   2017  0.195340\n",
              "2         Masters   4361  0.214400\n",
              "3             Phd    414  0.140097\n",
              "4  Primary School    308  0.133117"
            ],
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              "      <td>High School</td>\n",
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              "    <tr>\n",
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              "      <td>Masters</td>\n",
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              "    <tr>\n",
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              "      <td>Phd</td>\n",
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              "      <td>0.140097</td>\n",
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              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1d27435a-ee68-48d4-bf50-a8d5afe003ac 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-1d27435a-ee68-48d4-bf50-a8d5afe003ac');\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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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    display: none;\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b25576c3-394c-4ee1-aeaf-74e0404a1ff0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t.columns = ['_'.join(col).strip() for col in t.columns.values]\n",
        "t=t.reset_index()\n"
      ],
      "metadata": {
        "id": "Q6ehCTUJAxtW"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Q259a-B-BWF3",
        "outputId": "cc51e851-968e-41f4-ab3b-cebc0fb4a04e"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  education_level  count_target  mean_target\n",
              "0        Graduate         11598     0.279790\n",
              "1     High School          2017     0.195340\n",
              "2         Masters          4361     0.214400\n",
              "3             Phd           414     0.140097\n",
              "4  Primary School           308     0.133117"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>education_level</th>\n",
              "      <th>count_target</th>\n",
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              "      <td>0.133117</td>\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-b92f9931-695c-4053-956c-9e64d9430f99 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-b92f9931-695c-4053-956c-9e64d9430f99');\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",
              "\n",
              "\n",
              "<div id=\"df-1fe82736-0909-4119-8349-b3169e2e874a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1fe82736-0909-4119-8349-b3169e2e874a')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1fe82736-0909-4119-8349-b3169e2e874a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.pivot_table(index='education_level',\n",
        "               columns='gender',  values='target',\n",
        "               aggfunc=['count', 'mean'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "4EyE32pMBjLN",
        "outputId": "f464d806-88f1-4d93-dfbc-1698fefb025b"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 count                  mean                    \n",
              "gender          Female  Male Other    Female      Male     Other\n",
              "education_level                                                 \n",
              "Graduate           773  8144   112  0.279431  0.257122  0.330357\n",
              "High School         67  1395    33  0.268657  0.182796  0.181818\n",
              "Masters            339  2957    27  0.247788  0.191410  0.074074\n",
              "Phd                 47   280     4  0.127660  0.132143  0.250000\n",
              "Primary School       4   203     6  0.250000  0.093596  0.333333"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-90ff1657-0b92-4253-a77e-b509cdf7e437\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">count</th>\n",
              "      <th colspan=\"3\" halign=\"left\">mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <th>Female</th>\n",
              "      <th>Male</th>\n",
              "      <th>Other</th>\n",
              "      <th>Female</th>\n",
              "      <th>Male</th>\n",
              "      <th>Other</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_level</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Graduate</th>\n",
              "      <td>773</td>\n",
              "      <td>8144</td>\n",
              "      <td>112</td>\n",
              "      <td>0.279431</td>\n",
              "      <td>0.257122</td>\n",
              "      <td>0.330357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High School</th>\n",
              "      <td>67</td>\n",
              "      <td>1395</td>\n",
              "      <td>33</td>\n",
              "      <td>0.268657</td>\n",
              "      <td>0.182796</td>\n",
              "      <td>0.181818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Masters</th>\n",
              "      <td>339</td>\n",
              "      <td>2957</td>\n",
              "      <td>27</td>\n",
              "      <td>0.247788</td>\n",
              "      <td>0.191410</td>\n",
              "      <td>0.074074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Phd</th>\n",
              "      <td>47</td>\n",
              "      <td>280</td>\n",
              "      <td>4</td>\n",
              "      <td>0.127660</td>\n",
              "      <td>0.132143</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Primary School</th>\n",
              "      <td>4</td>\n",
              "      <td>203</td>\n",
              "      <td>6</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.093596</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90ff1657-0b92-4253-a77e-b509cdf7e437')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-90ff1657-0b92-4253-a77e-b509cdf7e437 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-90ff1657-0b92-4253-a77e-b509cdf7e437');\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",
              "\n",
              "\n",
              "<div id=\"df-31eab985-8a16-4ec7-aeb3-36b31f03a92e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-31eab985-8a16-4ec7-aeb3-36b31f03a92e')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-31eab985-8a16-4ec7-aeb3-36b31f03a92e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "HEATMAP"
      ],
      "metadata": {
        "id": "NG1LsVP02cjG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['gender']= df['gender'].fillna('no info')"
      ],
      "metadata": {
        "id": "GEQiYMaX2m4G"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t = df.pivot_table(index='education_level',\n",
        "               columns='gender',  values='target',\n",
        "               aggfunc=['mean'])"
      ],
      "metadata": {
        "id": "S6HGJb242S-H"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "yQJhbKWe3Lm3",
        "outputId": "df128e60-507d-4ff4-d653-fdd8f885ea60"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     mean                              \n",
              "gender             Female      Male     Other   no info\n",
              "education_level                                        \n",
              "Graduate         0.279431  0.257122  0.330357  0.349552\n",
              "High School      0.268657  0.182796  0.181818  0.220307\n",
              "Masters          0.247788  0.191410  0.074074  0.272640\n",
              "Phd              0.127660  0.132143  0.250000  0.168675\n",
              "Primary School   0.250000  0.093596  0.333333  0.200000"
            ],
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              "    </tr>\n",
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              "      <th>gender</th>\n",
              "      <th>Female</th>\n",
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              "      <th>no info</th>\n",
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              "      <th>Graduate</th>\n",
              "      <td>0.279431</td>\n",
              "      <td>0.257122</td>\n",
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              "      <td>0.349552</td>\n",
              "    </tr>\n",
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              "      <td>0.272640</td>\n",
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              "      <td>0.168675</td>\n",
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              "      <td>0.250000</td>\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "cOt_ZjzP3VhB"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "   sns.heatmap(t)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "mhDGZRUp3fTO",
        "outputId": "bba5736c-6ba5-4841-c2a1-7cf59923315f"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='None-gender', ylabel='education_level'>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "sw7oHXGv4ONA",
        "outputId": "23a6a92b-bcaf-49ef-fbcf-a2ee5d6ee40e"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-72-2f6f6606aa2c>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  df.corr()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        enrollee_id  city_development_index  training_hours  \\\n",
              "enrollee_id                1.000000               -0.040455        0.000998   \n",
              "city_development_index    -0.040455                1.000000        0.001920   \n",
              "training_hours             0.000998                0.001920        1.000000   \n",
              "target                     0.049475               -0.341665       -0.021577   \n",
              "new_col                         NaN                     NaN             NaN   \n",
              "new_training_hours        -0.027681                0.060995        0.385957   \n",
              "\n",
              "                          target  new_col  new_training_hours  \n",
              "enrollee_id             0.049475      NaN           -0.027681  \n",
              "city_development_index -0.341665      NaN            0.060995  \n",
              "training_hours         -0.021577      NaN            0.385957  \n",
              "target                  1.000000      NaN           -0.042844  \n",
              "new_col                      NaN      NaN                 NaN  \n",
              "new_training_hours     -0.042844      NaN            1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d1e35283-7cb8-4faf-94e4-e11a798a38fd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>enrollee_id</th>\n",
              "      <th>city_development_index</th>\n",
              "      <th>training_hours</th>\n",
              "      <th>target</th>\n",
              "      <th>new_col</th>\n",
              "      <th>new_training_hours</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>enrollee_id</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.040455</td>\n",
              "      <td>0.000998</td>\n",
              "      <td>0.049475</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.027681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>city_development_index</th>\n",
              "      <td>-0.040455</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.001920</td>\n",
              "      <td>-0.341665</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.060995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>training_hours</th>\n",
              "      <td>0.000998</td>\n",
              "      <td>0.001920</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.021577</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.385957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>0.049475</td>\n",
              "      <td>-0.341665</td>\n",
              "      <td>-0.021577</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.042844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new_col</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new_training_hours</th>\n",
              "      <td>-0.027681</td>\n",
              "      <td>0.060995</td>\n",
              "      <td>0.385957</td>\n",
              "      <td>-0.042844</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1e35283-7cb8-4faf-94e4-e11a798a38fd')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-d1e35283-7cb8-4faf-94e4-e11a798a38fd 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-d1e35283-7cb8-4faf-94e4-e11a798a38fd');\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",
              "\n",
              "\n",
              "<div id=\"df-3510b7b8-48a2-4d8c-9103-0bdf7695f9b2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3510b7b8-48a2-4d8c-9103-0bdf7695f9b2')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3510b7b8-48a2-4d8c-9103-0bdf7695f9b2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['city_development_index', 'training_hours', 'target']].corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "hdFK3nO46DZp",
        "outputId": "c128fb7c-8fe9-423e-c999-1d54265f0555"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        city_development_index  training_hours    target\n",
              "city_development_index                1.000000        0.001920 -0.341665\n",
              "training_hours                        0.001920        1.000000 -0.021577\n",
              "target                               -0.341665       -0.021577  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0c523d0d-9707-4fe8-a312-f1d40adbcdf8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city_development_index</th>\n",
              "      <th>training_hours</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>city_development_index</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.001920</td>\n",
              "      <td>-0.341665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>training_hours</th>\n",
              "      <td>0.001920</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.021577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>-0.341665</td>\n",
              "      <td>-0.021577</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0c523d0d-9707-4fe8-a312-f1d40adbcdf8')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-0c523d0d-9707-4fe8-a312-f1d40adbcdf8 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-0c523d0d-9707-4fe8-a312-f1d40adbcdf8');\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",
              "\n",
              "\n",
              "<div id=\"df-83fbe324-ceb8-4286-9e0e-39fe2508fb5f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-83fbe324-ceb8-4286-9e0e-39fe2508fb5f')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-83fbe324-ceb8-4286-9e0e-39fe2508fb5f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df[['city_development_index', 'training_hours', 'target']].corr())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "LW_4jwmT5h0X",
        "outputId": "95e7dbcf-0021-4b35-bc9b-65d88b2ca338"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install phik"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4YtFmWdU7wfQ",
        "outputId": "907eb817-e29f-4a28-84e0-43b64011f78e"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting phik\n",
            "  Downloading phik-0.12.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (679 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/679.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m337.9/679.5 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m679.5/679.5 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.10/dist-packages (from phik) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.5.2 in /usr/local/lib/python3.10/dist-packages (from phik) (1.11.4)\n",
            "Requirement already satisfied: pandas>=0.25.1 in /usr/local/lib/python3.10/dist-packages (from phik) (1.5.3)\n",
            "Requirement already satisfied: matplotlib>=2.2.3 in /usr/local/lib/python3.10/dist-packages (from phik) (3.7.1)\n",
            "Requirement already satisfied: joblib>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from phik) (1.3.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2.3->phik) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2.3->phik) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2.3->phik) (4.47.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2.3->phik) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2.3->phik) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2.3->phik) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2.3->phik) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2.3->phik) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.25.1->phik) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=2.2.3->phik) (1.16.0)\n",
            "Installing collected packages: phik\n",
            "Successfully installed phik-0.12.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import phik\n",
        "from phik.report import plot_correlation_matrix\n",
        "from phik import report"
      ],
      "metadata": {
        "id": "yacqoA_24g9g"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "phik_overview=df.phik_matrix()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sKUo5gkV8YdT",
        "outputId": "1724da50-95ce-4907-9160-5188b0e1e893"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "interval columns not set, guessing: ['enrollee_id', 'city_development_index', 'training_hours', 'target', 'new_col', 'new_training_hours']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/phik/data_quality.py:72: UserWarning: Not enough unique value for variable new_col for analysis 1. Dropping this column\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "phik_overview"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "hQtYieH58uKZ",
        "outputId": "c82fa537-8def-40c8-d91e-fa952ab9a44f"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     enrollee_id  city_development_index  \\\n",
              "enrollee_id                             1.000000                0.125181   \n",
              "city_development_index                  0.125181                1.000000   \n",
              "gender                                  0.125045                0.163535   \n",
              "relevent_experience                     0.073145                0.101448   \n",
              "enrolled_university                     0.051188                0.245979   \n",
              "education_level                         0.076123                0.224368   \n",
              "major_discipline                        0.030968                0.129083   \n",
              "experience                              0.086949                0.307770   \n",
              "company_size                            0.032956                0.130592   \n",
              "company_type                            0.022543                0.112234   \n",
              "last_new_job                            0.045252                0.172067   \n",
              "training_hours                          0.021517                0.000000   \n",
              "target                                  0.067472                0.482287   \n",
              "city_development_index_group            0.118153                1.000000   \n",
              "city_development_index_group_q          0.101484                0.886906   \n",
              "city_development_index_group_custom     0.030314                0.764609   \n",
              "new_education_level                     0.061054                0.203795   \n",
              "new_training_hours                      0.033738                0.062888   \n",
              "\n",
              "                                       gender  relevent_experience  \\\n",
              "enrollee_id                          0.125045             0.073145   \n",
              "city_development_index               0.163535             0.101448   \n",
              "gender                               1.000000             0.175232   \n",
              "relevent_experience                  0.175232             1.000000   \n",
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              "major_discipline                     0.087460             0.098661   \n",
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              "company_size                         0.036975             0.074259   \n",
              "company_type                         0.073239             0.284810   \n",
              "last_new_job                         0.093596             0.539529   \n",
              "training_hours                       0.009124             0.020727   \n",
              "target                               0.116653             0.199859   \n",
              "city_development_index_group         0.114481             0.059986   \n",
              "city_development_index_group_q       0.228761             0.094370   \n",
              "city_development_index_group_custom  0.068991             0.048628   \n",
              "new_education_level                  0.052497             0.187300   \n",
              "new_training_hours                   0.024818             0.039599   \n",
              "\n",
              "                                     enrolled_university  education_level  \\\n",
              "enrollee_id                                     0.051188         0.076123   \n",
              "city_development_index                          0.245979         0.224368   \n",
              "gender                                          0.081010         0.051922   \n",
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              "enrolled_university                             1.000000         0.215062   \n",
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              "major_discipline                                0.151561         0.104318   \n",
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              "company_size                                    0.067175         0.082131   \n",
              "company_type                                    0.231786         0.116321   \n",
              "last_new_job                                    0.430596         0.253235   \n",
              "training_hours                                  0.000000         0.030104   \n",
              "target                                          0.093803         0.075999   \n",
              "city_development_index_group                    0.174196         0.127624   \n",
              "city_development_index_group_q                  0.147038         0.119003   \n",
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              "new_education_level                             0.400491         1.000000   \n",
              "new_training_hours                              0.101667         0.639933   \n",
              "\n",
              "                                     major_discipline  experience  \\\n",
              "enrollee_id                                  0.030968    0.086949   \n",
              "city_development_index                       0.129083    0.307770   \n",
              "gender                                       0.087460    0.202654   \n",
              "relevent_experience                          0.098661    0.500107   \n",
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              "experience                                   0.116505    1.000000   \n",
              "company_size                                 0.038923    0.127647   \n",
              "company_type                                 0.089031    0.112121   \n",
              "last_new_job                                 0.047317    0.468247   \n",
              "training_hours                               0.000000    0.000000   \n",
              "target                                       0.029200    0.238725   \n",
              "city_development_index_group                 0.101880    0.335891   \n",
              "city_development_index_group_q               0.129442    0.358784   \n",
              "city_development_index_group_custom          0.033484    0.042371   \n",
              "new_education_level                          0.084839    0.359742   \n",
              "new_training_hours                           0.020618    0.148504   \n",
              "\n",
              "                                     company_size  company_type  last_new_job  \\\n",
              "enrollee_id                              0.032956      0.022543      0.045252   \n",
              "city_development_index                   0.130592      0.112234      0.172067   \n",
              "gender                                   0.036975      0.073239      0.093596   \n",
              "relevent_experience                      0.074259      0.284810      0.539529   \n",
              "enrolled_university                      0.067175      0.231786      0.430596   \n",
              "education_level                          0.082131      0.116321      0.253235   \n",
              "major_discipline                         0.038923      0.089031      0.047317   \n",
              "experience                               0.127647      0.112121      0.468247   \n",
              "company_size                             1.000000      0.332074      0.102736   \n",
              "company_type                             0.332074      1.000000      0.208470   \n",
              "last_new_job                             0.102736      0.208470      1.000000   \n",
              "training_hours                           0.000000      0.017970      0.015103   \n",
              "target                                   0.071953      0.066792      0.114708   \n",
              "city_development_index_group             0.095912      0.085063      0.144716   \n",
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              "new_education_level                      0.106308      0.159540      0.488684   \n",
              "new_training_hours                       0.041586      0.173288      0.100784   \n",
              "\n",
              "                                     training_hours    target  \\\n",
              "enrollee_id                                0.021517  0.067472   \n",
              "city_development_index                     0.000000  0.482287   \n",
              "gender                                     0.009124  0.116653   \n",
              "relevent_experience                        0.020727  0.199859   \n",
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              "education_level                            0.030104  0.075999   \n",
              "major_discipline                           0.000000  0.029200   \n",
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              "company_size                               0.000000  0.071953   \n",
              "company_type                               0.017970  0.066792   \n",
              "last_new_job                               0.015103  0.114708   \n",
              "training_hours                             1.000000  0.026849   \n",
              "target                                     0.026849  1.000000   \n",
              "city_development_index_group               0.000000  0.302403   \n",
              "city_development_index_group_q             0.000000  0.523691   \n",
              "city_development_index_group_custom        0.000000  0.066180   \n",
              "new_education_level                        0.000000  0.053437   \n",
              "new_training_hours                         0.829467  0.054314   \n",
              "\n",
              "                                     city_development_index_group  \\\n",
              "enrollee_id                                              0.118153   \n",
              "city_development_index                                   1.000000   \n",
              "gender                                                   0.114481   \n",
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              "target                                                   0.302403   \n",
              "city_development_index_group                             1.000000   \n",
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              "new_education_level                                      0.070334   \n",
              "new_training_hours                                       0.049519   \n",
              "\n",
              "                                     city_development_index_group_q  \\\n",
              "enrollee_id                                                0.101484   \n",
              "city_development_index                                     0.886906   \n",
              "gender                                                     0.228761   \n",
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              "education_level                                            0.119003   \n",
              "major_discipline                                           0.129442   \n",
              "experience                                                 0.358784   \n",
              "company_size                                               0.150605   \n",
              "company_type                                               0.111799   \n",
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              "training_hours                                             0.000000   \n",
              "target                                                     0.523691   \n",
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              "city_development_index_group_q                             1.000000   \n",
              "city_development_index_group_custom                        0.164956   \n",
              "new_education_level                                        0.113660   \n",
              "new_training_hours                                         0.063447   \n",
              "\n",
              "                                     city_development_index_group_custom  \\\n",
              "enrollee_id                                                     0.030314   \n",
              "city_development_index                                          0.764609   \n",
              "gender                                                          0.068991   \n",
              "relevent_experience                                             0.048628   \n",
              "enrolled_university                                             0.022034   \n",
              "education_level                                                 0.017598   \n",
              "major_discipline                                                0.033484   \n",
              "experience                                                      0.042371   \n",
              "company_size                                                    0.013814   \n",
              "company_type                                                    0.037485   \n",
              "last_new_job                                                    0.019462   \n",
              "training_hours                                                  0.000000   \n",
              "target                                                          0.066180   \n",
              "city_development_index_group                                    0.406680   \n",
              "city_development_index_group_q                                  0.164956   \n",
              "city_development_index_group_custom                             1.000000   \n",
              "new_education_level                                             0.009209   \n",
              "new_training_hours                                              0.012649   \n",
              "\n",
              "                                     new_education_level  new_training_hours  \n",
              "enrollee_id                                     0.061054            0.033738  \n",
              "city_development_index                          0.203795            0.062888  \n",
              "gender                                          0.052497            0.024818  \n",
              "relevent_experience                             0.187300            0.039599  \n",
              "enrolled_university                             0.400491            0.101667  \n",
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              "major_discipline                                0.084839            0.020618  \n",
              "experience                                      0.359742            0.148504  \n",
              "company_size                                    0.106308            0.041586  \n",
              "company_type                                    0.159540            0.173288  \n",
              "last_new_job                                    0.488684            0.100784  \n",
              "training_hours                                  0.000000            0.829467  \n",
              "target                                          0.053437            0.054314  \n",
              "city_development_index_group                    0.070334            0.049519  \n",
              "city_development_index_group_q                  0.113660            0.063447  \n",
              "city_development_index_group_custom             0.009209            0.012649  \n",
              "new_education_level                             1.000000            0.401391  \n",
              "new_training_hours                              0.401391            1.000000  "
            ],
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              "      <td>0.539529</td>\n",
              "      <td>0.430596</td>\n",
              "      <td>0.253235</td>\n",
              "      <td>0.047317</td>\n",
              "      <td>0.468247</td>\n",
              "      <td>0.102736</td>\n",
              "      <td>0.208470</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.015103</td>\n",
              "      <td>0.114708</td>\n",
              "      <td>0.144716</td>\n",
              "      <td>0.178297</td>\n",
              "      <td>0.019462</td>\n",
              "      <td>0.488684</td>\n",
              "      <td>0.100784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>training_hours</th>\n",
              "      <td>0.021517</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009124</td>\n",
              "      <td>0.020727</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.030104</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.017970</td>\n",
              "      <td>0.015103</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.026849</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.829467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>0.067472</td>\n",
              "      <td>0.482287</td>\n",
              "      <td>0.116653</td>\n",
              "      <td>0.199859</td>\n",
              "      <td>0.093803</td>\n",
              "      <td>0.075999</td>\n",
              "      <td>0.029200</td>\n",
              "      <td>0.238725</td>\n",
              "      <td>0.071953</td>\n",
              "      <td>0.066792</td>\n",
              "      <td>0.114708</td>\n",
              "      <td>0.026849</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.302403</td>\n",
              "      <td>0.523691</td>\n",
              "      <td>0.066180</td>\n",
              "      <td>0.053437</td>\n",
              "      <td>0.054314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>city_development_index_group</th>\n",
              "      <td>0.118153</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.114481</td>\n",
              "      <td>0.059986</td>\n",
              "      <td>0.174196</td>\n",
              "      <td>0.127624</td>\n",
              "      <td>0.101880</td>\n",
              "      <td>0.335891</td>\n",
              "      <td>0.095912</td>\n",
              "      <td>0.085063</td>\n",
              "      <td>0.144716</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.302403</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.778388</td>\n",
              "      <td>0.406680</td>\n",
              "      <td>0.070334</td>\n",
              "      <td>0.049519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>city_development_index_group_q</th>\n",
              "      <td>0.101484</td>\n",
              "      <td>0.886906</td>\n",
              "      <td>0.228761</td>\n",
              "      <td>0.094370</td>\n",
              "      <td>0.147038</td>\n",
              "      <td>0.119003</td>\n",
              "      <td>0.129442</td>\n",
              "      <td>0.358784</td>\n",
              "      <td>0.150605</td>\n",
              "      <td>0.111799</td>\n",
              "      <td>0.178297</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.523691</td>\n",
              "      <td>0.778388</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.164956</td>\n",
              "      <td>0.113660</td>\n",
              "      <td>0.063447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>city_development_index_group_custom</th>\n",
              "      <td>0.030314</td>\n",
              "      <td>0.764609</td>\n",
              "      <td>0.068991</td>\n",
              "      <td>0.048628</td>\n",
              "      <td>0.022034</td>\n",
              "      <td>0.017598</td>\n",
              "      <td>0.033484</td>\n",
              "      <td>0.042371</td>\n",
              "      <td>0.013814</td>\n",
              "      <td>0.037485</td>\n",
              "      <td>0.019462</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.066180</td>\n",
              "      <td>0.406680</td>\n",
              "      <td>0.164956</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.009209</td>\n",
              "      <td>0.012649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new_education_level</th>\n",
              "      <td>0.061054</td>\n",
              "      <td>0.203795</td>\n",
              "      <td>0.052497</td>\n",
              "      <td>0.187300</td>\n",
              "      <td>0.400491</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.084839</td>\n",
              "      <td>0.359742</td>\n",
              "      <td>0.106308</td>\n",
              "      <td>0.159540</td>\n",
              "      <td>0.488684</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.053437</td>\n",
              "      <td>0.070334</td>\n",
              "      <td>0.113660</td>\n",
              "      <td>0.009209</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.401391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new_training_hours</th>\n",
              "      <td>0.033738</td>\n",
              "      <td>0.062888</td>\n",
              "      <td>0.024818</td>\n",
              "      <td>0.039599</td>\n",
              "      <td>0.101667</td>\n",
              "      <td>0.639933</td>\n",
              "      <td>0.020618</td>\n",
              "      <td>0.148504</td>\n",
              "      <td>0.041586</td>\n",
              "      <td>0.173288</td>\n",
              "      <td>0.100784</td>\n",
              "      <td>0.829467</td>\n",
              "      <td>0.054314</td>\n",
              "      <td>0.049519</td>\n",
              "      <td>0.063447</td>\n",
              "      <td>0.012649</td>\n",
              "      <td>0.401391</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-42e8c824-4cc2-4f19-8e47-bfb3ec91a064')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-42e8c824-4cc2-4f19-8e47-bfb3ec91a064 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-42e8c824-4cc2-4f19-8e47-bfb3ec91a064');\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",
              "\n",
              "\n",
              "<div id=\"df-f26f34a3-607b-4b09-b975-1753a00e0a1a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f26f34a3-607b-4b09-b975-1753a00e0a1a')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f26f34a3-607b-4b09-b975-1753a00e0a1a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(phik_overview)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713
        },
        "id": "vO0mHP-k86rI",
        "outputId": "fff73233-9e69-4403-d63b-529ac81bcad4"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "phik_overview['target'].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pVL06mVO9ZPp",
        "outputId": "7b9772e2-4471-425c-9604-da96cab8c855"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target                                 1.000000\n",
              "city_development_index_group_q         0.523691\n",
              "city_development_index                 0.482287\n",
              "city_development_index_group           0.302403\n",
              "experience                             0.238725\n",
              "relevent_experience                    0.199859\n",
              "gender                                 0.116653\n",
              "last_new_job                           0.114708\n",
              "enrolled_university                    0.093803\n",
              "education_level                        0.075999\n",
              "company_size                           0.071953\n",
              "enrollee_id                            0.067472\n",
              "company_type                           0.066792\n",
              "city_development_index_group_custom    0.066180\n",
              "new_training_hours                     0.054314\n",
              "new_education_level                    0.053437\n",
              "major_discipline                       0.029200\n",
              "training_hours                         0.026849\n",
              "Name: target, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g1E_3pst93zt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}