🎯 Employee Promotion Prediction System

📄 Project Overview

This project focuses on building a Machine Learning Model to predict whether employees are eligible for a promotion based on their performance, achievements, and other attributes. The solution is designed to help HR departments make data-driven decisions while ensuring fairness and efficiency.

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🔍 Objectives

• Develop a robust ML model to predict employee promotions.
• Handle class imbalance to ensure accurate predictions for underrepresented groups.
• Provide a detailed EDA (Exploratory Data Analysis) to understand key patterns and relationships in the data.
• Train models with optimized features for better interpretability and performance.

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🛠️ Technologies and Tools Used

• Programming Language: Python 🐍
• Libraries:
  • Data Processing: pandas, numpy
  • Visualization: matplotlib, seaborn
  • Machine Learning: scikit-learn, xgboost
  • Oversampling: imbalanced-learn
  • Model Persistence: joblib

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📂 Project Structure

EDA-and-predictor-for-Employee-Eligible-for-Promotion/
├── data/                     # Contains datasets
│   ├── HRData.csv            # Raw dataset
│   ├── HRData_cleaned.csv    # Cleaned and preprocessed dataset
│   ├── hrdatatest.csv        # Test dataset
│   └── predictions_hrdatatest.csv # Predictions output
├── models/                   # Trained models and scalers
│   ├── model.py
│   ├── xgboost_model_smote.pkl
│   └── scaler.pkl
├── scripts/                  # Scripts for each step of the pipeline
│   ├── data_preprocessing.py # Data preprocessing and cleaning
│   ├── Exploratory_Data_Analysis.py  # Exploratory Data Analysis
│   ├── predict.py            # Predictions on new data
├── README.md                 # Project documentation
└── requirements.txt          # Python dependencies

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🚀 Key Features

🔹 1. Exploratory Data Analysis (EDA)

• Visualizations included:
  • Distribution of promotions.
  • Correlation matrix.
  • Boxplots (e.g., age vs promotion).
  • Histograms (e.g., training scores).
  • Promotion breakdown by department.
• Insights:
  • avg_training_score, KPIs_met >80%, and previous_year_rating are highly correlated with promotions.

🔹 2. Data Preprocessing

• Handled missing values using SimpleImputer.
• Converted categorical variables to numerical using LabelEncoder.
• Standardized features using StandardScaler.

🔹 3. Model Training

• Used XGBoost for classification.
• Addressed class imbalance with SMOTE.
• Trained the model with the full feature set and improved its precision and recall by balancing classes using SMOTE:

🔹 4. Predictions

• Deployed a script to predict promotions on new datasets.
• Outputs predictions in predictions_hrdatatest.csv.

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📊 Model Metrics

Model with Full Features

• Accuracy: 97%.
• Class 1 (Promoted):
  • Recall: 94%.
  • Precision: 99%.
  • F1-Score: 96%.

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⚙️ How to Run the Project

1. Set up the environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: .\venv\Scripts\activate
   pip install -r requirements.txt

2. Preprocess the data:

   python scripts/data_preprocessing.py

3. Perform EDA:

   python scripts/Exploratory_Data_Analysis.py

4. Train the model:

   python models/model_py

5. Make predictions:

   python scripts/predict.py

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📦 Dependencies

Install all required libraries with:

pip install -r requirements.txt

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📥 Outputs

• Predictions are saved in the data/predictions_hrdatatest.csv file.
• Trained models and scalers are saved in the models/ directory.

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🤝 Contributing

Contributions are welcome! Feel free to submit a pull request or raise an issue. Let's make this project even better!

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📧 Contact

For questions or feedback, please reach out to me at - LinkedIn Profile.

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✨ Thank you for exploring this project! 😊