README.md

Employee Attrition Analysis

Overview

This project focuses on analyzing employee attrition data using data analysis, visualization, and machine learning. The goal is to understand patterns in employee turnover and predict attrition based on features like tenure, performance, and demographics.

Key skills demonstrated:

• Data Analysis & Visualization
• Machine Learning Model Building
• Feature Engineering & Preprocessing

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Features

• Exploratory Data Analysis: Visualize attrition patterns, correlations, and feature distributions.
• Machine Learning: Train models to predict employee attrition with performance evaluation.
• Saved Visualizations: PNG files of plots for reporting or presentations.

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Technologies Used

• Python, Pandas, NumPy
• Scikit-learn for ML
• Matplotlib & Seaborn for visualization

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Installation

1. Clone the repository:

git clone https://github.com/yourusername/Employee-Attrition-Analysis.git
cd Employee-Attrition-Analysis

2. Install dependencies:

pip install -r requirements.txt

3. Run model training:

python src/model_training.py

4. Open the EDA notebook for visualization:

"C:\Users\ridhi\Downloads\Git.ipynb"

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Screenshots of EDA

Attrition Count:

Correlation Heatmap:

Pairplot:

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