15. Diversity in the workplace.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "14a89543",
   "metadata": {},
   "source": [
    "# Goal of Analysis:\n",
    "\n",
    "### Analyze the internal data about employees and see whether results suggest that the company is treating its employees fairly regarding the diversity situation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6bf0bfbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f8d4104d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>boss_id</th>\n",
       "      <th>dept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46456</td>\n",
       "      <td>175361.0</td>\n",
       "      <td>sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104708</td>\n",
       "      <td>29733.0</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120853</td>\n",
       "      <td>41991.0</td>\n",
       "      <td>sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>142630</td>\n",
       "      <td>171266.0</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72711</td>\n",
       "      <td>198240.0</td>\n",
       "      <td>sales</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id   boss_id   dept\n",
       "0        46456  175361.0  sales\n",
       "1       104708   29733.0     HR\n",
       "2       120853   41991.0  sales\n",
       "3       142630  171266.0     HR\n",
       "4        72711  198240.0  sales"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company = pd.read_csv('company_hierarchy.csv')\n",
    "company.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "564e2277",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>signing_bonus</th>\n",
       "      <th>salary</th>\n",
       "      <th>degree_level</th>\n",
       "      <th>sex</th>\n",
       "      <th>yrs_experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>138719</td>\n",
       "      <td>0</td>\n",
       "      <td>273000.0</td>\n",
       "      <td>Master</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3192</td>\n",
       "      <td>0</td>\n",
       "      <td>301000.0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114657</td>\n",
       "      <td>0</td>\n",
       "      <td>261000.0</td>\n",
       "      <td>Master</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29039</td>\n",
       "      <td>0</td>\n",
       "      <td>86000.0</td>\n",
       "      <td>High_School</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>118607</td>\n",
       "      <td>0</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id  signing_bonus    salary degree_level sex  yrs_experience\n",
       "0       138719              0  273000.0       Master   M               2\n",
       "1         3192              0  301000.0     Bachelor   F               1\n",
       "2       114657              0  261000.0       Master   F               2\n",
       "3        29039              0   86000.0  High_School   F               4\n",
       "4       118607              0  126000.0     Bachelor   F               3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employee = pd.read_csv('employee.csv')\n",
    "employee.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8c9e761d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000, 3), (10000, 6))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company.shape, employee.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6887628b",
   "metadata": {},
   "source": [
    "### Part 1: Data Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38beaa85",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>boss_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>signing_bonus</th>\n",
       "      <th>salary</th>\n",
       "      <th>degree_level</th>\n",
       "      <th>sex</th>\n",
       "      <th>yrs_experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46456</td>\n",
       "      <td>175361.0</td>\n",
       "      <td>sales</td>\n",
       "      <td>0</td>\n",
       "      <td>213000.0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104708</td>\n",
       "      <td>29733.0</td>\n",
       "      <td>HR</td>\n",
       "      <td>0</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120853</td>\n",
       "      <td>41991.0</td>\n",
       "      <td>sales</td>\n",
       "      <td>0</td>\n",
       "      <td>199000.0</td>\n",
       "      <td>High_School</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>142630</td>\n",
       "      <td>171266.0</td>\n",
       "      <td>HR</td>\n",
       "      <td>0</td>\n",
       "      <td>143000.0</td>\n",
       "      <td>PhD</td>\n",
       "      <td>F</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72711</td>\n",
       "      <td>198240.0</td>\n",
       "      <td>sales</td>\n",
       "      <td>0</td>\n",
       "      <td>235000.0</td>\n",
       "      <td>Master</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id   boss_id   dept  signing_bonus    salary degree_level sex  \\\n",
       "0        46456  175361.0  sales              0  213000.0     Bachelor   M   \n",
       "1       104708   29733.0     HR              0   79000.0     Bachelor   F   \n",
       "2       120853   41991.0  sales              0  199000.0  High_School   M   \n",
       "3       142630  171266.0     HR              0  143000.0          PhD   F   \n",
       "4        72711  198240.0  sales              0  235000.0       Master   M   \n",
       "\n",
       "   yrs_experience  \n",
       "0               5  \n",
       "1               3  \n",
       "2               5  \n",
       "3              13  \n",
       "4               1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = pd.merge(left = company, right = employee, on = 'employee_id')\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7417bf5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc6e35df",
   "metadata": {},
   "source": [
    "### Part 2: Question 1:\n",
    "\n",
    "In the company there are 6 levels (described below). Identify, for each employee, her corresponding level.\n",
    "\n",
    "Individual Contributors (IC) - they don't manage anyone\n",
    "\n",
    "Middle Managers (MM) - they are the direct bosses of IC\n",
    "\n",
    "Directors (D) - they are the direct bosses of MM\n",
    "\n",
    "VP - D direct bosses\n",
    "\n",
    "Executives (E) - VP direct bosses\n",
    "\n",
    "CEO - The direct boss of E."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb4f1bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize\n",
    "new = pd.read_csv(\"company_hierarchy.csv\", index_col='employee_id')\n",
    "new['level'] = None \n",
    "\n",
    "# starting point\n",
    "new.loc[new.dept == 'CEO','level'] = 'CEO'\n",
    "\n",
    "# change all boss_id to integer\n",
    "new.loc[new.level == 'CEO','boss_id'] = -1\n",
    "new['boss_id'] = new.boss_id.astype(int)\n",
    "\n",
    "# function to count the value\n",
    "def set_level(boss_level,level):\n",
    "    boss_ids = new.loc[new.level == boss_level,:].index\n",
    "    is_subordinate = np.in1d(new.boss_id,boss_ids)\n",
    "    new.loc[is_subordinate,'level'] = level\n",
    "\n",
    "set_level('CEO','E')\n",
    "set_level('E','VP')\n",
    "set_level('VP','D')\n",
    "set_level('D','MM')\n",
    "set_level('MM','IC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9033925c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IC     9000\n",
       "MM      800\n",
       "D       160\n",
       "VP       35\n",
       "E         4\n",
       "CEO       1\n",
       "Name: level, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts for each group\n",
    "\n",
    "new.level.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89d37d2f",
   "metadata": {},
   "source": [
    "### Part 3: Qustion 2:\n",
    "\n",
    "How many people each employee manages? You should have a table with employee ids and the number of people managed. Consider that if John directly manages 2 people and these two people manage 5 people each, then we conclude that John manages 12 people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f57c07ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the managed employees for each employee\n",
    "levels = ['CEO', 'E', 'VP', 'D', 'MM', 'IC']\n",
    "combined.loc[combined['dept'] == 'CEO', 'level'] = 'CEO'\n",
    "combined['counts'] = 0\n",
    "\n",
    "for level in ['IC', 'MM', 'D', 'VP', 'E']:\n",
    "    boss_ids = combined[combined['level'] == level]['boss_id']\n",
    "    employee_ids = combined[combined['level'] == level]['employee_id']\n",
    "    for b_id, e_id in zip(boss_ids, employee_ids):\n",
    "        e_counts = int(1 + combined.loc[data['employee_id'] == e_id, 'counts'])\n",
    "        b_counts = int(combined.loc[combined['employee_id'] == b_id, 'counts'])\n",
    "        combined.loc[combined['employee_id'] == b_id, 'counts'] = e_counts + b_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0ae1dc9c",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>level</th>\n",
       "      <th>counts</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>7248</th>\n",
       "      <td>123666</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4268</th>\n",
       "      <td>196953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9490</th>\n",
       "      <td>127788</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7015</th>\n",
       "      <td>187173</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4952</th>\n",
       "      <td>6222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9883</th>\n",
       "      <td>187192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7068</th>\n",
       "      <td>143877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6047</th>\n",
       "      <td>139498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8904</th>\n",
       "      <td>165467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>195798</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      employee_id level  counts\n",
       "7248       123666   NaN       0\n",
       "4268       196953   NaN       0\n",
       "9490       127788   NaN       0\n",
       "7015       187173   NaN       0\n",
       "4952         6222   NaN       0\n",
       "9883       187192   NaN       0\n",
       "7068       143877   NaN       0\n",
       "6047       139498   NaN       0\n",
       "8904       165467   NaN       0\n",
       "1171       195798   NaN       0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take a sample to have a look at the table\n",
    "\n",
    "combined[['employee_id', 'level', 'counts']].sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112b3b0b",
   "metadata": {},
   "source": [
    "### Part 4: Question 3:\n",
    "\n",
    "Build a model to predict the salary of each employee."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d57ebed3",
   "metadata": {},
   "source": [
    "I will try to build machine learning models with light GBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "656e9cc0",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dept</th>\n",
       "      <th>signing_bonus</th>\n",
       "      <th>salary</th>\n",
       "      <th>degree_level</th>\n",
       "      <th>sex</th>\n",
       "      <th>yrs_experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sales</td>\n",
       "      <td>0</td>\n",
       "      <td>213000.0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HR</td>\n",
       "      <td>0</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sales</td>\n",
       "      <td>0</td>\n",
       "      <td>199000.0</td>\n",
       "      <td>High_School</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HR</td>\n",
       "      <td>0</td>\n",
       "      <td>143000.0</td>\n",
       "      <td>PhD</td>\n",
       "      <td>F</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sales</td>\n",
       "      <td>0</td>\n",
       "      <td>235000.0</td>\n",
       "      <td>Master</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dept  signing_bonus    salary degree_level sex  yrs_experience\n",
       "0  sales              0  213000.0     Bachelor   M               5\n",
       "1     HR              0   79000.0     Bachelor   F               3\n",
       "2  sales              0  199000.0  High_School   M               5\n",
       "3     HR              0  143000.0          PhD   F              13\n",
       "4  sales              0  235000.0       Master   M               1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop CEO department\n",
    "combined = combined[combined['dept'] != 'CEO']\n",
    "\n",
    "# drop useless features\n",
    "combined = combined.drop(labels=['employee_id', 'boss_id', 'level', 'counts'], axis=1)\n",
    "\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cf28ec13",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import lightgbm as lgb\n",
    "\n",
    "# encoding categorical data\n",
    "combined['sex'] = (combined['sex'] == 'M').astype(int)\n",
    "\n",
    "degree_encoder = LabelEncoder()\n",
    "combined['degree_level'] = degree_encoder.fit_transform(combined['degree_level'])\n",
    "\n",
    "dept_encoder = LabelEncoder()\n",
    "combined['dept'] = dept_encoder.fit_transform(combined['dept'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ede944a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define categorical features\n",
    "\n",
    "categorical_feature = ['signing_bonus', 'degree_level', 'sex', 'dept']\n",
    "combined[categorical_feature] = combined[categorical_feature].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f691608d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into training and test data set\n",
    "X = combined.drop('salary', axis=1)\n",
    "y = combined['salary']\n",
    "\n",
    "X_train, X_test, y_train, y_test \\\n",
    "    = train_test_split(X, y, test_size=0.25, random_state=42)\n",
    "\n",
    "# transform into log scale\n",
    "y_train = np.log(y_train)\n",
    "y_test = np.log(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "72e52347",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create LightGBM dataset\n",
    "d_train = lgb.Dataset(data=X_train, label=y_train, free_raw_data=False, \n",
    "                      categorical_feature=categorical_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2736f37b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000162 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 44\n",
      "[LightGBM] [Info] Number of data points in the train set: 5996, number of used features: 5\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000121 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 44\n",
      "[LightGBM] [Info] Number of data points in the train set: 5996, number of used features: 5\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000130 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 44\n",
      "[LightGBM] [Info] Number of data points in the train set: 5996, number of used features: 5\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000656 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 44\n",
      "[LightGBM] [Info] Number of data points in the train set: 5996, number of used features: 5\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000126 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 44\n",
      "[LightGBM] [Info] Number of data points in the train set: 5996, number of used features: 5\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Start training from score 12.028289\n",
      "[LightGBM] [Info] Start training from score 12.025542\n",
      "[LightGBM] [Info] Start training from score 12.022573\n",
      "[LightGBM] [Info] Start training from score 12.024960\n",
      "[LightGBM] [Info] Start training from score 12.025837\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "Best rounds:\t 593\n"
     ]
    }
   ],
   "source": [
    "# Cross validation\n",
    "params = {'learning_rate': 0.01, \n",
    "          'boosting_type': 'gbdt', \n",
    "          'objective': 'regression', \n",
    "          'metric': 'rmse', \n",
    "          'sub_feature':0.5, \n",
    "          'num_leaves': 31, \n",
    "          'min_data': 50, \n",
    "          'max_depth': 30}\n",
    "\n",
    "history = lgb.cv(params, train_set=d_train, num_boost_round=1000, nfold=5, \n",
    "                 early_stopping_rounds=20, seed=42, stratified=False)\n",
    "\n",
    "best_round = len(history['rmse-mean'])\n",
    "print('Best rounds:\\t', best_round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "353a2c9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001419 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 44\n",
      "[LightGBM] [Info] Number of data points in the train set: 7499, number of used features: 5\n",
      "[LightGBM] [Info] Start training from score 12.025515\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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     ]
    }
   ],
   "source": [
    "# re-train the model and make predictions\n",
    "\n",
    "clf = lgb.train(params, train_set=d_train, num_boost_round=best_round)\n",
    "pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c99af75a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE:\t 0.3979680431547868\n",
      "Test R2:\t 0.4196336668014382\n"
     ]
    }
   ],
   "source": [
    "print('Test RMSE:\\t', np.sqrt(mean_squared_error(y_test, pred)))\n",
    "print('Test R2:\\t', r2_score(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70362d60",
   "metadata": {},
   "source": [
    "### Part 5: Question 4:\n",
    "\n",
    "Describe the main factors impacting employee salaries. Do you think the company has been treating its employees fairly? Do you see any bias? What are the next steps you would suggest to the Head of HR?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "12635b04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# feature importance\n",
    "\n",
    "features = clf.feature_name()\n",
    "importance = clf.feature_importance()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "lgb.plot_importance(clf, ax=ax, height=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2583442",
   "metadata": {},
   "source": [
    "We can see that years of experience is the most important factor, following by degree level and department."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ced29ea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}