🧠 Employee Performance Prediction using Machine Learning

A machine learning based web application that predicts the productivity of employees based on various work-related factors like overtime, idle time, department, team structure, and more.

📊 Built using Linear Regression, Random Forest, and XGBoost
🌐 Frontend: Flask-based web app for real-time predictions

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📁 Project Folder Structure

employee_performance_ml/
├── model/               # Trained model & feature columns
├── templates/           # Flask HTML templates
├── static/              # (Optional) CSS or images
├── dataset/             # CSV input dataset
├── assets/              # Screenshots used in README
├── main.py              # Flask app entry point
├── preprocessing.py     # Data cleaning + transformation
├── visualization.py     # Exploratory Data Analysis
├── model_building.py    # Model training + saving
├── test_predict.py      # Manual testing script
├── requirements.txt     # Dependency list
└── README.md            # You're here 

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🧠 Problem Statement

This system predicts employee productivity based on historical workplace data.
Helps businesses to:

• 📉 Identify underperformance trends
• 🧑‍💼 Support management in training/resource allocation
• 🔁 Retain top talent using early performance signals

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📦 Dataset Info

• 📍 Source: Kaggle - Garment Employee Productivity
• 👥 1197 Rows × 15 Columns
• 📄 Format: .csv (CSV)

📸 Sample Preview:

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🔍 Exploratory Data Analysis

Performed:

• ✅ Null check + Imputation (wip column)
• 🏷️ Label encoding for categorical fields (Quarter, Department)
• 📊 Correlation + stats visualizations

📸 Correlation Matrix:

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🧪 Model Building

Used 3 ML algorithms:

• 📉 Linear Regression
• 🌲 Random Forest ✅ (Best Performing)
• 🚀 XGBoost

📊 R² Score Comparison:

🏆 Best Model: Random Forest (R² ≈ 0.46)

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🌐 Web Application (Flask)

🔧 Features:

• 📝 Input employee data via web form
• 🔮 Predict productivity instantly
• 🧠 Uses trained Random Forest model behind the scenes

📸 Web Interface:

Homepage:

Prediction Output:

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▶️ How to Run Locally

# 1️⃣ Clone Repo
git clone https://github.com/vanstyagi/employee_performance_ml.git
cd employee_performance_ml

# 2️⃣ Install Dependencies
pip install -r requirements.txt

# 3️⃣ Run the App
python main.py

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🤝 Contributors

• 🔥 Vansh — ML Dev, EDA, Flask Integration

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📌 Learnings

• 🧠 Complete ML pipeline: Preprocessing → Model → Deploy
• 📊 Real-world dataset + exploratory data analysis
• 🌐 Building & connecting backend ML to frontend Flask UI
• ⚙️ Hands-on end-to-end deployment experience

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