Salifort Motors: Employee Turnover Analysis

This repository contains the Capstone Project for the Google Advanced Data Analytics Certificate, focusing on employee turnover analysis at Salifort Motors. The goal is to identify factors contributing to employee attrition and to build predictive models that can help HR teams make data-driven decisions to retain talent.

Project Overview

In this project, we:

• Explored and analyzed the Salifort Motors HR dataset.
• Conducted feature engineering (e.g., ordinal encoding of salary).
• Built and evaluated classification models (Random Forest and XGBoost).
• Analyzed model performance using metrics like accuracy, ROC-AUC, precision, recall, and F1 score.
• Visualized confusion matrices for clear interpretation.

Project Structure

salifort-motors-employee-turnover/
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├── images/
│
├── hr_data.csv # Raw HR dataset
│
├── analysis.ipynb # Jupyter Notebook with analysis
│
├── hr_rf1.pickle # Trained Random Forest model for Dataset 1
├── hr_rf2.pickle # Trained Random Forest model for Dataset 2
│
├── hr_xgb_model1.json # Trained XGBoost model for Dataset 1
├── hr_xgb_model1_metadata.pkl # Metadata for XGBoost model 1
│
├── hr_xgb_model2.json # Trained XGBoost model for Dataset 2
├── hr_xgb_model2_metadata.pkl # Metadata for XGBoost model 2
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├── extract_images_from_ipynb.sh # bash script to extract all images
├── ipynb_to_html.sh # ipynb to html while keeping nice format
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|── html_visual_SB_Activity_Salifort_Motors_project_lab.html # added html file for better visualisation
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└── README.md # This file

Confusion Matrix

Plot confusion matrices using ConfusionMatrixDisplay from sklearn.metrics.

Key Libraries

• Python 3.12+

• pandas

• numpy

• scikit-learn

• xgboost

• matplotlib

• seaborn