Employee Retention Insights for Salifort Motors' HR

Description

Capstone Project for the Google Advanced Data Analytics Certification course.

Scenario:

• The Human Resources (HR) department of a large consulting firm, Salifort Motors, wants to take some initiatives to improve employee satisfaction levels since finding, interviewing, and hiring new employees is so time-consuming and expensive.

Problem Question:

• What is likely to make the employee leave the company?

Task:

• Predict whether or not an employee will leave the company and provide data-driven suggestions for HR on improving employee retention.

Solution:

• Analyze the data collected by the HR department, build a predictive model to identify employees likely to quit, and identify factors that contribute to their leaving if possible.
• Models: Logistic Regression, Decision Tree, Random Forest, XGBoost, K-Means

Deliverables:

1. Executive summary that you would present to external stakeholders as the data professional in Salifort Motors; includes model evaluation, interpretation, data visualizations, ethical considerations, and the resources you used to troubleshoot and find answers or solutions.
2. Completed python scripts.

Dataset:

Raw: Kaggle HR Dataset: 14,999 rows (employees), 10 columns

After Cleaning:

COL_NAME	TYPE	DESC
SATISFACTION	float64	The employee’s self-reported satisfaction level [0-1]
LAST_EVAL	float64	Score of employee's last performance review [0-1]
PROJ_NUM	int64	Number of projects employee contributes to
AVG_HRS_PER_MONTH	int64	Average number of hours employee worked per month
TENURE	int64	How long the employee has been with the company (years)
WORK_ACCIDENT	int64	Whether or not the employee experienced an accident while at work; 0=No, 1=Yes
LEFT	int64	Whether or not the employee left the company; 0=No (Stayed), 1=Yes (Left)
PROMOTION_LAST_5YEARS	int64	Whether or not the employee was promoted in the last 5 years; 0=No, 1=Yes
DEPARTMENT	object	The employee's department
SALARY	object	The employee's salary (low, medium, high)

Installation

pip install ipykernel pycaret[full] sweetviz numpy pandas matplotlib seaborn sklearn

Imports

# Data manipulation
import numpy as np
import pandas as pd

# Saving models
import pickle

# Data visualization
import sweetviz as sv
import matplotlib.pyplot as plt
import seaborn as sns

# LightGBM using PyCaret
from pycaret.classification import *

# Data Modeling - LogReg & Metrics
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report

Code of Conduct

Code of Conduct