notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eda9c1b2-4e9a-41ac-8da6-d4c98775f986",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 70,
    "lastExecutedAt": 1731095055341,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# Import necessary libraries\nimport pandas as pd\n\n# Load the dataset\nds_jobs = pd.read_csv(\"customer_train.csv\")\n\n# View the dataset\nds_jobs.head()",
    "outputsMetadata": {
     "0": {
      "height": 222,
      "type": "dataFrame"
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>student_id</th>\n",
       "      <th>city</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>gender</th>\n",
       "      <th>relevant_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>job_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8949</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevant 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>city_40</td>\n",
       "      <td>0.776</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevant 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>city_21</td>\n",
       "      <td>0.624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevant 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>city_115</td>\n",
       "      <td>0.789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevant 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>city_162</td>\n",
       "      <td>0.767</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevant 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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student_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",
       "       relevant_experience enrolled_university education_level  \\\n",
       "0  Has relevant experience       no_enrollment        Graduate   \n",
       "1   No relevant experience       no_enrollment        Graduate   \n",
       "2   No relevant experience    Full time course        Graduate   \n",
       "3   No relevant experience                 NaN        Graduate   \n",
       "4  Has relevant experience       no_enrollment         Masters   \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",
       "   training_hours  job_change  \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  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Load the dataset\n",
    "ds_jobs = pd.read_csv(\"customer_train.csv\")\n",
    "\n",
    "# View the dataset\n",
    "ds_jobs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13757e62-aca2-48f4-b569-a838af11bdd5",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 49,
    "lastExecutedAt": 1731095055390,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# Create a copy of ds_jobs for transforming\nds_jobs_transformed = ds_jobs.copy()\n\n# Start coding here. Use as many cells as you like!\nds_jobs_transformed.info()"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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   student_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   relevant_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  job_change              19158 non-null  float64\n",
      "dtypes: float64(2), int64(2), object(10)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Copy of ds_jobs for transforming\n",
    "ds_jobs_transformed = ds_jobs.copy()\n",
    "\n",
    "ds_jobs_transformed.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b932bdda-d537-4f86-98a0-166eef2b6c68",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    }
   },
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec61565",
   "metadata": {},
   "source": [
    "### Columns containing categories with only two factors must be stored as Booleans (bool)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bebed85-c285-4b40-83bc-4354dfe7ce77",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 52,
    "lastExecutedAt": 1731095055549,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# Columns containing categories with only two factors must be stored as Booleans (bool).\n\nrelevant_experience_dict = {\"Has relevant experience\": True, \"No relevant experience\": False}\njob_change_dict = {1: True, 0: False}\nds_jobs_transformed[\"relevant_experience\"] = ds_jobs_transformed[\"relevant_experience\"].map(relevant_experience_dict)\nds_jobs_transformed[\"job_change\"] = ds_jobs_transformed[\"job_change\"].map(job_change_dict)"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "relevant_experience    2\n",
       "job_change             2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding columns with only two factors\n",
    "ds_jobs_transformed.nunique()[ds_jobs_transformed.nunique() == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a0906f50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Has relevant experience    13792\n",
      "No relevant experience      5366\n",
      "Name: relevant_experience, dtype: int64\n",
      "\n",
      "0.0    14381\n",
      "1.0     4777\n",
      "Name: job_change, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(ds_jobs_transformed[\"relevant_experience\"].value_counts())\n",
    "print()\n",
    "print(ds_jobs_transformed[\"job_change\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bf56635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>student_id</th>\n",
       "      <th>city</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>gender</th>\n",
       "      <th>relevant_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>job_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8949</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>True</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>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29725</td>\n",
       "      <td>city_40</td>\n",
       "      <td>0.776</td>\n",
       "      <td>Male</td>\n",
       "      <td>False</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>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11561</td>\n",
       "      <td>city_21</td>\n",
       "      <td>0.624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</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>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33241</td>\n",
       "      <td>city_115</td>\n",
       "      <td>0.789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</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>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666</td>\n",
       "      <td>city_162</td>\n",
       "      <td>0.767</td>\n",
       "      <td>Male</td>\n",
       "      <td>True</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>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student_id      city  city_development_index gender  relevant_experience  \\\n",
       "0        8949  city_103                   0.920   Male                 True   \n",
       "1       29725   city_40                   0.776   Male                False   \n",
       "2       11561   city_21                   0.624    NaN                False   \n",
       "3       33241  city_115                   0.789    NaN                False   \n",
       "4         666  city_162                   0.767   Male                 True   \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",
       "  company_size    company_type last_new_job  training_hours  job_change  \n",
       "0          NaN             NaN            1              36        True  \n",
       "1        50-99         Pvt Ltd           >4              47       False  \n",
       "2          NaN             NaN        never              83       False  \n",
       "3          NaN         Pvt Ltd        never              52        True  \n",
       "4        50-99  Funded Startup            4               8       False  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating dictionaries for mapping\n",
    "relevant_experience_dict = {\"Has relevant experience\": True, \"No relevant experience\": False}\n",
    "job_change_dict = {1: True, 0: False}\n",
    "\n",
    "# Mapping the values\n",
    "ds_jobs_transformed[\"relevant_experience\"] = ds_jobs_transformed[\"relevant_experience\"].map(relevant_experience_dict)\n",
    "ds_jobs_transformed[\"job_change\"] = ds_jobs_transformed[\"job_change\"].map(job_change_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2bd538df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True     13792\n",
      "False     5366\n",
      "Name: relevant_experience, dtype: int64\n",
      "\n",
      "False    14381\n",
      "True      4777\n",
      "Name: job_change, dtype: int64\n",
      "\n",
      "student_id                  int64\n",
      "city                       object\n",
      "city_development_index    float64\n",
      "gender                     object\n",
      "relevant_experience          bool\n",
      "enrolled_university        object\n",
      "education_level            object\n",
      "major_discipline           object\n",
      "experience                 object\n",
      "company_size               object\n",
      "company_type               object\n",
      "last_new_job               object\n",
      "training_hours              int64\n",
      "job_change                   bool\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Checking the changes\n",
    "print(ds_jobs_transformed[\"relevant_experience\"].value_counts())\n",
    "print()\n",
    "print(ds_jobs_transformed[\"job_change\"].value_counts())\n",
    "print()\n",
    "print(ds_jobs_transformed.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2ab02ad",
   "metadata": {},
   "source": [
    "### Columns containing integers only must be stored as 32-bit integers (int32)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f89e0cd2-4a57-4131-a1d8-6f4d4e487474",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 80,
    "lastExecutedAt": 1731095055629,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# Columns containing integers only must be stored as 32-bit integers (int32).\n\nds_jobs_transformed.dtypes[ds_jobs_transformed.dtypes == \"int64\"]"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "student_id        int64\n",
       "training_hours    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_jobs_transformed.dtypes[ds_jobs_transformed.dtypes == \"int64\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b0dbafd-59a7-497c-9600-c04238d97d43",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 48,
    "lastExecutedAt": 1731095055677,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "import numpy as np\nds_jobs_transformed[\"student_id\"] = ds_jobs_transformed[\"student_id\"].astype(np.int32)\nds_jobs_transformed[\"training_hours\"] = ds_jobs_transformed[\"training_hours\"].astype(np.int32)"
   },
   "outputs": [],
   "source": [
    "ds_jobs_transformed[\"student_id\"] = ds_jobs_transformed[\"student_id\"].astype(np.int32)\n",
    "ds_jobs_transformed[\"training_hours\"] = ds_jobs_transformed[\"training_hours\"].astype(np.int32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78e19832",
   "metadata": {},
   "source": [
    "### Columns containing floats must be stored as 16-bit floats (float16).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "295b19e0-c573-4fd7-a12f-c57970bba1fb",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 56,
    "lastExecutedAt": 1731095055733,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "ds_jobs_transformed.dtypes[ds_jobs_transformed.dtypes == \"float64\"]"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city_development_index    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_jobs_transformed.dtypes[ds_jobs_transformed.dtypes == \"float64\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fff76f93-f06a-45de-a8b3-707516121740",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 48,
    "lastExecutedAt": 1731095055781,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# Columns containing floats must be stored as 16-bit floats (float16).\nds_jobs_transformed[\"city_development_index\"] = ds_jobs_transformed[\"city_development_index\"].astype(np.float16)"
   },
   "outputs": [],
   "source": [
    "ds_jobs_transformed[\"city_development_index\"] = ds_jobs_transformed[\"city_development_index\"].astype(np.float16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8916b483",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "student_id                  int32\n",
      "city                       object\n",
      "city_development_index    float16\n",
      "gender                     object\n",
      "relevant_experience          bool\n",
      "enrolled_university        object\n",
      "education_level            object\n",
      "major_discipline           object\n",
      "experience                 object\n",
      "company_size               object\n",
      "company_type               object\n",
      "last_new_job               object\n",
      "training_hours              int32\n",
      "job_change                   bool\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Checking the changes\n",
    "print(ds_jobs_transformed.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16f6ad61",
   "metadata": {},
   "source": [
    "### Columns containing nominal categorical data must be stored as the category data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "be3d41a5-92ea-430d-9dba-7c2b3b40c50c",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 48,
    "lastExecutedAt": 1731095055829,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# Columns containing nominal categorical data must be stored as the category data type.\ncategorical_cols = list(ds_jobs_transformed.columns[ds_jobs_transformed.dtypes == \"object\"])\nds_jobs_transformed[categorical_cols].head()"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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</th>\n",
       "      <th>gender</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>city_103</td>\n",
       "      <td>Male</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>city_40</td>\n",
       "      <td>Male</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>city_21</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>city_115</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>city_162</td>\n",
       "      <td>Male</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city gender enrolled_university education_level major_discipline  \\\n",
       "0  city_103   Male       no_enrollment        Graduate             STEM   \n",
       "1   city_40   Male       no_enrollment        Graduate             STEM   \n",
       "2   city_21    NaN    Full time course        Graduate             STEM   \n",
       "3  city_115    NaN                 NaN        Graduate  Business Degree   \n",
       "4  city_162   Male       no_enrollment         Masters             STEM   \n",
       "\n",
       "  experience company_size    company_type last_new_job  \n",
       "0        >20          NaN             NaN            1  \n",
       "1         15        50-99         Pvt Ltd           >4  \n",
       "2          5          NaN             NaN        never  \n",
       "3         <1          NaN         Pvt Ltd        never  \n",
       "4        >20        50-99  Funded Startup            4  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_cols = list(ds_jobs_transformed.columns[ds_jobs_transformed.dtypes == \"object\"])\n",
    "ds_jobs_transformed[categorical_cols].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6f2beceb-d527-4c8b-8e9e-048c1230d055",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 56,
    "lastExecutedAt": 1731095055885,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "nominal_cats = ['city', 'gender', 'major_discipline', 'company_type']\nordinal_cats = list(set(categorical_cols) - set(nominal_cats))\nprint(nominal_cats)\nprint(ordinal_cats)\n\nfor col in categorical_cols:\n    ds_jobs_transformed[col] = ds_jobs_transformed[col].astype(\"category\")",
    "outputsMetadata": {
     "0": {
      "height": 59,
      "type": "stream"
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['city', 'gender', 'major_discipline', 'company_type']\n",
      "['last_new_job', 'experience', 'education_level', 'company_size', 'enrolled_university']\n"
     ]
    }
   ],
   "source": [
    "nominal_cats = ['city', 'gender', 'major_discipline', 'company_type']\n",
    "ordinal_cats = list(set(categorical_cols) - set(nominal_cats))\n",
    "print(nominal_cats)\n",
    "print(ordinal_cats)\n",
    "\n",
    "for col in categorical_cols:\n",
    "    ds_jobs_transformed[col] = ds_jobs_transformed[col].astype(\"category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c4f142",
   "metadata": {},
   "source": [
    "### Columns containing ordinal categorical data must be stored as ordered categories, and not mapped to numerical values, with an order that reflects the natural order of the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "76363897-a522-42a3-87ac-4ca6d49c9f3a",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 51,
    "lastExecutedAt": 1731095055937,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# Columns containing ordinal categorical data must be stored as ordered categories, and not mapped to numerical values, with an order that reflects the natural order of the column.\n\n\nfor col in ordinal_cats:\n    print(col)\n    print(ds_jobs_transformed[col].value_counts())\n    print()"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last_new_job\n",
      "1        8040\n",
      ">4       3290\n",
      "2        2900\n",
      "never    2452\n",
      "4        1029\n",
      "3        1024\n",
      "Name: last_new_job, dtype: int64\n",
      "\n",
      "experience\n",
      ">20    3286\n",
      "5      1430\n",
      "4      1403\n",
      "3      1354\n",
      "6      1216\n",
      "2      1127\n",
      "7      1028\n",
      "10      985\n",
      "9       980\n",
      "8       802\n",
      "15      686\n",
      "11      664\n",
      "14      586\n",
      "1       549\n",
      "<1      522\n",
      "16      508\n",
      "12      494\n",
      "13      399\n",
      "17      342\n",
      "19      304\n",
      "18      280\n",
      "20      148\n",
      "Name: experience, dtype: int64\n",
      "\n",
      "education_level\n",
      "Graduate          11598\n",
      "Masters            4361\n",
      "High School        2017\n",
      "Phd                 414\n",
      "Primary School      308\n",
      "Name: education_level, dtype: int64\n",
      "\n",
      "company_size\n",
      "50-99        3083\n",
      "100-499      2571\n",
      "10000+       2019\n",
      "10-49        1471\n",
      "1000-4999    1328\n",
      "<10          1308\n",
      "500-999       877\n",
      "5000-9999     563\n",
      "Name: company_size, dtype: int64\n",
      "\n",
      "enrolled_university\n",
      "no_enrollment       13817\n",
      "Full time course     3757\n",
      "Part time course     1198\n",
      "Name: enrolled_university, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in ordinal_cats:\n",
    "    print(col)\n",
    "    print(ds_jobs_transformed[col].value_counts())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "98c12a10-6a95-4fa8-8e1a-63705ac4f00a",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 52,
    "lastExecutedAt": 1731095055989,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "enrolled_university_order = [\"no_enrollment\", \"Part time course\", \"Full time course\"]\neducation_level_order = [\"Primary School\", \"High School\", \"Graduate\", \"Masters\", \"Phd\"]\ncompany_size_order = [\"<10\", \"10-49\", \"50-99\", \"100-499\", \"500-999\", \"1000-4999\", \"5000-9999\", \"10000+\"]\nexperience_order = np.append(np.insert(list(np.arange(1, 21).astype(str)), 0, \"<1\"), \">20\")\nlast_new_job_order = [\"never\", \"1\", \"2\", \"3\", \"4\", \">4\"]\norders = [enrolled_university_order, education_level_order, company_size_order, experience_order, last_new_job_order]"
   },
   "outputs": [],
   "source": [
    "# Creating lists of orders for ordinal categories\n",
    "enrolled_university_order = [\"no_enrollment\", \"Part time course\", \"Full time course\"]\n",
    "education_level_order = [\"Primary School\", \"High School\", \"Graduate\", \"Masters\", \"Phd\"]\n",
    "company_size_order = [\"<10\", \"10-49\", \"50-99\", \"100-499\", \"500-999\", \"1000-4999\", \"5000-9999\", \"10000+\"]\n",
    "experience_order = np.append(np.insert(list(np.arange(1, 21).astype(str)), 0, \"<1\"), \">20\")\n",
    "last_new_job_order = [\"never\", \"1\", \"2\", \"3\", \"4\", \">4\"]\n",
    "\n",
    "orders = [enrolled_university_order, education_level_order, company_size_order, experience_order, last_new_job_order]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0346d32d-d2f9-4be3-bb19-14119672002d",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 52,
    "lastExecutedAt": 1731095056093,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "ds_jobs_transformed[\"enrolled_university\"].cat.reorder_categories(\n    new_categories = orders[0], \n    ordered = True, \n    inplace = True\n)\n\nds_jobs_transformed[\"education_level\"].cat.reorder_categories(\n    new_categories = orders[1], \n    ordered = True, \n    inplace = True\n)\n\nds_jobs_transformed[\"company_size\"].cat.reorder_categories(\n    new_categories = orders[2], \n    ordered = True, \n    inplace = True\n)\n\nds_jobs_transformed[\"experience\"].cat.reorder_categories(\n    new_categories = orders[3], \n    ordered = True, \n    inplace = True\n)\n\nds_jobs_transformed[\"last_new_job\"].cat.reorder_categories(\n    new_categories = orders[4], \n    ordered = True, \n    inplace = True\n)"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0q/fy5tvxp924g_s_0ypkx1v_5w0000gn/T/ipykernel_12908/3518953860.py:1: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.\n",
      "  ds_jobs_transformed[\"enrolled_university\"].cat.reorder_categories(\n",
      "/var/folders/0q/fy5tvxp924g_s_0ypkx1v_5w0000gn/T/ipykernel_12908/3518953860.py:7: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.\n",
      "  ds_jobs_transformed[\"education_level\"].cat.reorder_categories(\n",
      "/var/folders/0q/fy5tvxp924g_s_0ypkx1v_5w0000gn/T/ipykernel_12908/3518953860.py:13: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.\n",
      "  ds_jobs_transformed[\"company_size\"].cat.reorder_categories(\n",
      "/var/folders/0q/fy5tvxp924g_s_0ypkx1v_5w0000gn/T/ipykernel_12908/3518953860.py:19: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.\n",
      "  ds_jobs_transformed[\"experience\"].cat.reorder_categories(\n",
      "/var/folders/0q/fy5tvxp924g_s_0ypkx1v_5w0000gn/T/ipykernel_12908/3518953860.py:25: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.\n",
      "  ds_jobs_transformed[\"last_new_job\"].cat.reorder_categories(\n"
     ]
    }
   ],
   "source": [
    "# Reordering the categories\n",
    "ds_jobs_transformed[\"enrolled_university\"].cat.reorder_categories(\n",
    "    new_categories = orders[0], \n",
    "    ordered = True, \n",
    "    inplace = True\n",
    ")\n",
    "\n",
    "ds_jobs_transformed[\"education_level\"].cat.reorder_categories(\n",
    "    new_categories = orders[1], \n",
    "    ordered = True, \n",
    "    inplace = True\n",
    ")\n",
    "\n",
    "ds_jobs_transformed[\"company_size\"].cat.reorder_categories(\n",
    "    new_categories = orders[2], \n",
    "    ordered = True, \n",
    "    inplace = True\n",
    ")\n",
    "\n",
    "ds_jobs_transformed[\"experience\"].cat.reorder_categories(\n",
    "    new_categories = orders[3], \n",
    "    ordered = True, \n",
    "    inplace = True\n",
    ")\n",
    "\n",
    "ds_jobs_transformed[\"last_new_job\"].cat.reorder_categories(\n",
    "    new_categories = orders[4], \n",
    "    ordered = True, \n",
    "    inplace = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "00c4be76-9fa3-4bba-9ce7-3f7afb9fdbf1",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 56,
    "lastExecutedAt": 1731095056201,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "print(ds_jobs.memory_usage().sum()/1024, \"kb\")\nprint(ds_jobs_transformed.memory_usage().sum()/1024, \"kb\")",
    "outputsMetadata": {
     "0": {
      "height": 59,
      "type": "stream"
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2095.53125 kb\n",
      "400.224609375 kb\n"
     ]
    }
   ],
   "source": [
    "# Checking the memory usage difference\n",
    "print(ds_jobs.memory_usage().sum()/1024, \"kb\")\n",
    "print(ds_jobs_transformed.memory_usage().sum()/1024, \"kb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "170b5d23",
   "metadata": {},
   "source": [
    "### The DataFrame should be filtered to only contain students with 10 or more years of experience at companies with at least 1000 employees, as their recruiter base is suited to more experienced professionals at enterprise companies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2710333a-c305-434c-92ab-5633faf6b58b",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 52,
    "lastExecutedAt": 1731095056253,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# The DataFrame should be filtered to only contain students with 10 or more years of experience at companies with at least 1000 employees, as their recruiter base is suited to more experienced professionals at enterprise companies.\n\nds_jobs_transformed = ds_jobs_transformed[(ds_jobs_transformed['experience'] >= \"10\") & (ds_jobs_transformed[\"company_size\"] >= \"1000-4999\")]"
   },
   "outputs": [],
   "source": [
    "ds_jobs_transformed = ds_jobs_transformed[(ds_jobs_transformed['experience'] >= \"10\") & (ds_jobs_transformed[\"company_size\"] >= \"1000-4999\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d7eb4882-33d6-4c6b-a743-856172665447",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 53,
    "lastExecutedAt": 1731095056307,
    "lastExecutedByKernel": "b77ec462-0459-4bb5-9950-4694fd0b25b4",
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "ds_jobs_transformed.head(10)"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>student_id</th>\n",
       "      <th>city</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>gender</th>\n",
       "      <th>relevant_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>job_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>699</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.919922</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>17</td>\n",
       "      <td>10000+</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>&gt;4</td>\n",
       "      <td>123</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>25619</td>\n",
       "      <td>city_61</td>\n",
       "      <td>0.913086</td>\n",
       "      <td>Male</td>\n",
       "      <td>True</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>1000-4999</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>22293</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.919922</td>\n",
       "      <td>Male</td>\n",
       "      <td>True</td>\n",
       "      <td>Part time course</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>19</td>\n",
       "      <td>5000-9999</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>&gt;4</td>\n",
       "      <td>141</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>26494</td>\n",
       "      <td>city_16</td>\n",
       "      <td>0.910156</td>\n",
       "      <td>Male</td>\n",
       "      <td>True</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Business Degree</td>\n",
       "      <td>12</td>\n",
       "      <td>5000-9999</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2547</td>\n",
       "      <td>city_114</td>\n",
       "      <td>0.925781</td>\n",
       "      <td>Female</td>\n",
       "      <td>True</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>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>25987</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.919922</td>\n",
       "      <td>Other</td>\n",
       "      <td>True</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>19</td>\n",
       "      <td>10000+</td>\n",
       "      <td>Public Sector</td>\n",
       "      <td>4</td>\n",
       "      <td>52</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1180</td>\n",
       "      <td>city_16</td>\n",
       "      <td>0.910156</td>\n",
       "      <td>Male</td>\n",
       "      <td>True</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>12</td>\n",
       "      <td>5000-9999</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>25349</td>\n",
       "      <td>city_16</td>\n",
       "      <td>0.910156</td>\n",
       "      <td>Male</td>\n",
       "      <td>True</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>1000-4999</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>&gt;4</td>\n",
       "      <td>28</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>20576</td>\n",
       "      <td>city_97</td>\n",
       "      <td>0.924805</td>\n",
       "      <td>Male</td>\n",
       "      <td>True</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>1000-4999</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>&gt;4</td>\n",
       "      <td>19</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>3921</td>\n",
       "      <td>city_36</td>\n",
       "      <td>0.893066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Phd</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>1000-4999</td>\n",
       "      <td>Public Sector</td>\n",
       "      <td>&gt;4</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     student_id      city  city_development_index  gender  \\\n",
       "9           699  city_103                0.919922     NaN   \n",
       "12        25619   city_61                0.913086    Male   \n",
       "31        22293  city_103                0.919922    Male   \n",
       "34        26494   city_16                0.910156    Male   \n",
       "40         2547  city_114                0.925781  Female   \n",
       "47        25987  city_103                0.919922   Other   \n",
       "104        1180   city_16                0.910156    Male   \n",
       "108       25349   city_16                0.910156    Male   \n",
       "115       20576   city_97                0.924805    Male   \n",
       "130        3921   city_36                0.893066     NaN   \n",
       "\n",
       "     relevant_experience enrolled_university education_level major_discipline  \\\n",
       "9                   True       no_enrollment        Graduate             STEM   \n",
       "12                  True       no_enrollment        Graduate             STEM   \n",
       "31                  True    Part time course        Graduate             STEM   \n",
       "34                  True       no_enrollment        Graduate  Business Degree   \n",
       "40                  True    Full time course         Masters             STEM   \n",
       "47                  True       no_enrollment        Graduate             STEM   \n",
       "104                 True       no_enrollment        Graduate             STEM   \n",
       "108                 True       no_enrollment        Graduate             STEM   \n",
       "115                 True       no_enrollment        Graduate             STEM   \n",
       "130                False       no_enrollment             Phd             STEM   \n",
       "\n",
       "    experience company_size   company_type last_new_job  training_hours  \\\n",
       "9           17       10000+        Pvt Ltd           >4             123   \n",
       "12         >20    1000-4999        Pvt Ltd            3              23   \n",
       "31          19    5000-9999        Pvt Ltd           >4             141   \n",
       "34          12    5000-9999        Pvt Ltd            3             145   \n",
       "40          16    1000-4999  Public Sector            2              14   \n",
       "47          19       10000+  Public Sector            4              52   \n",
       "104         12    5000-9999        Pvt Ltd            1              52   \n",
       "108        >20    1000-4999        Pvt Ltd           >4              28   \n",
       "115        >20    1000-4999        Pvt Ltd           >4              19   \n",
       "130        >20    1000-4999  Public Sector           >4               4   \n",
       "\n",
       "     job_change  \n",
       "9         False  \n",
       "12        False  \n",
       "31        False  \n",
       "34        False  \n",
       "40        False  \n",
       "47         True  \n",
       "104       False  \n",
       "108       False  \n",
       "115       False  \n",
       "130       False  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_jobs_transformed.head(10)"
   ]
  }
 ],
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