An overview of a comprehensive project aimed at improving employee retention at Salifort Motors. The project utilizes data exploration, analysis, and predictive machine learning models, all implemented using Python.
Data Analysis Using Python
The initial phase of the project involves data exploration and analysis using Python. This process allows us to understand the underlying patterns and trends in our data, which is crucial for determining subsequent steps. We utilize Python libraries such as Seaborn and Matplotlib to create preliminary visualizations, which provide valuable insights into the data.
Project Resources on GitHub
The project repository on GitHub contains four key files:
- A Jupyter Notebook (.ipynb) file containing Python code, complete with comments and annotations for clarity.
- An updated Jupyter Notebook file, featuring revised machine learning models compatible with Python 3.12.
- An Executive Summary in PDF format, providing a high-level overview of the project and its findings.
- A link to a comprehensive presentation hosted at Tableau Public, offering a visual representation of the project's results.
This project demonstrates the power of data analysis and machine learning in addressing real-world challenges. By leveraging these tools, we can gain valuable insights and make informed decisions to improve employee retention at Salifort Motors.
Heatmap of Logistic Regression Dataset:
Confusion matrix for random forest machine learning model:
Feature importance for random forest model:
