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'Unhappy hard-workers' and 'unhappy champions': how to make them happy?

📖 Background

You work for the human capital department of a large corporation. The Board is worried about the relatively high turnover, and your team must look into ways to reduce the number of employees leaving the company.

The team needs to understand better the situation, which employees are more likely to leave, and why. Once it is clear what variables impact employee churn, you can present your findings along with your ideas on how to attack the problem.

💾 The data

The department has assembled data on almost 10,000 employees. The team used information from exit interviews, performance reviews, and employee records.

  • "department" - the department the employee belongs to.
  • "promoted" - 1 if the employee was promoted in the previous 24 months, 0 otherwise.
  • "review" - the composite score the employee received in their last evaluation.
  • "projects" - how many projects the employee is involved in.
  • "salary" - for confidentiality reasons, salary comes in three tiers: low, medium, high.
  • "tenure" - how many years the employee has been at the company.
  • "satisfaction" - a measure of employee satisfaction from surveys.
  • "avg_hrs_month" - the average hours the employee worked in a month.
  • "left" - "yes" if the employee ended up leaving, "no" otherwise.
import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.model_selection import train_test_split
from sklearn.model_selection import RandomizedSearchCV

from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.cluster import KMeans

from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import recall_score
from sklearn.metrics import confusion_matrix

from scipy.stats import boxcox
from sklearn.preprocessing import MinMaxScaler

from sklearn import tree 
import graphviz
from mpl_toolkits.mplot3d import Axes3D

EDA

Let' start with preprocessing and exploratory data analysis.

df = pd.read_csv('./data/employee_churn_data.csv')
df.head()
df.info()

Which department has the highest employee turnover? Which one has the lowest?

df.department.value_counts()
round(pd.crosstab(df.department, df.left, normalize = 0)*100, 1).sort_values(by = 'yes', ascending = False).reset_index()

As we can see, there is the highest employee turnover (higher than 30%) in IT, logistics, retail and marketing departments, and the lowest (around 27%) is in the finance department.

Taking into account the sizes of the departments, the turnover is especially alarming in IT and logistics.

df['salary'] = df['salary'].replace({'low': 1, 'medium': 2, 'high': 3})
df['left'] = df['left'].replace({'no': 0, 'yes': 1})
sns.heatmap(df.corr());

There is a strong linear correlation between the 'tenure' and 'avg_hrs_month' features, as well as a significant correlation of the 'review' feature with the target variable.

'Review' may be an important predictor.

sns.scatterplot(x='tenure', y='avg_hrs_month', hue='left', data=df);
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