🧠 Employee Retention Forecasting Using Machine Learning

This project presents a machine learning-based solution to forecast employee attrition within organizations. By analyzing HR data, it aims to provide actionable insights that help improve employee engagement and retention strategies.

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📌 Overview

• Project Title: Employee Retention Forecasting Using ML Algorithms
• Domain: Human Resources / People Analytics
• Objective: Predict whether an employee is likely to leave the organization
• Tools & Libraries: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Google Colab
• ML Models Used: Logistic Regression, Decision Tree, Random Forest
• Evaluation Metrics: Accuracy, Confusion Matrix, Precision, Recall, F1-Score

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🔍 Key Features

• ✅ Cleaned and preprocessed HR dataset
• 📊 Performed detailed Exploratory Data Analysis (EDA)
• 📉 Visualized employee behavior and attrition trends
• 🤖 Trained multiple machine learning models
• 🧪 Evaluated and compared model performance
• 📌 Extracted key insights to inform HR policy

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📊 Visual Insights

• Heatmap of feature correlations
• Distribution plots of key features (e.g., satisfaction level, last evaluation)
• Attrition comparison by number of projects, average monthly hours, promotions, etc.
• Accuracy comparison of ML models

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🚀 How to Run

1. Clone the repository

   git clone https://github.com/your-username/employee-retention-forecasting.git
   cd employee-retention-forecasting