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Can you help reduce employee turnover?

📖 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.
  • "bonus" - 1 if the employee received a bonus in the previous 24 months, 0 otherwise.
  • "avg_hrs_month" - the average hours the employee worked in a month.
  • "left" - "yes" if the employee ended up leaving, "no" otherwise.

💪 Competition challenge

Create a report that covers the following:

  1. Which department has the highest employee turnover? Which one has the lowest?
  2. Investigate which variables seem to be better predictors of employee departure.
  3. What recommendations would you make regarding ways to reduce employee turnover?

Imports

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

import statsmodels.api as sm
from statsmodels.stats.outliers_influence import variance_inflation_factor

sns.set_style('whitegrid')
def check_multicollinearity(X):
    # Check for multicollinearity using VIF
    vif_data = pd.DataFrame()
    vif_data["feature"] = X.columns
    vif_data["VIF"] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
    vif_data.sort_values(by = "VIF", ascending = False)
    return vif_data
churn = pd.read_csv('./data/employee_churn_data.csv')
churn.head()

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

plt.figure(figsize = (16, 6))
df = churn.copy()
df = pd.crosstab(df['department'], df['left']).reset_index()
df = df.melt(id_vars = ['department'])
df.columns = ['department', 'left', 'count']
df = df.sort_values(by = 'count', ascending = False)

sns.barplot(data = df, y = 'department', x = 'count', hue = 'left')
plt.xticks(rotation = 90)
plt.tight_layout()
plt.figure(figsize = (16, 6))

df = churn.copy()
df = pd.crosstab(df['department'], df['left'], normalize='index').sort_values(by = 'yes', ascending = False)['yes']
df = df.reset_index()
df.columns = ['department', 'pct']
sns.barplot(data = df, y = 'department', x = 'pct')
plt.title("Percentage of employee turn by department")
plt.tight_layout()

Investigate which variables seem to be better predictors of employee departure.

Logistic Regression Model Building

Initial Model

mapping = {
    'yes': 1,
    'no': 0
}
df = churn.copy()
df['left'] = df['left'].map(mapping)
df = pd.get_dummies(df, drop_first=True)
df
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