📊 AI Methodology: Employee Performance Analysis

This repository showcases an end-to-end Machine Learning project focused on analyzing and predicting Employee Performance using various AI and Data Science methodologies. It integrates modern MLOps practices, model interpretability techniques, and comprehensive exploratory data analysis (EDA).

🚀 Architecture

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🎯 Project Objective

• Build robust Machine Learning models for predicting employee performance.
• Industrialize the model through structured data preprocessing, training, inference, and deployment.
• Integrate explainability techniques (SHAP) for transparent and interpretable ML outcomes.

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📁 Project Structure

AI_Methodology/
├── data/
│   └── External/   # Downloaded datasets or data retrieved from Kaggle
├── notebooks/
│   ├── EDA.ipynb
│   ├── MLFLOW.ipynb
│   └── Model_Explainability.ipynb
├── scripts/
│   ├── inference.py
│   ├── main.py
│   ├── preprocess.py
│   └── train_model.py
├── Requirements.txt
└── README.md

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🚀 Getting Started

1. Clone the Repository

git clone git@github.com:Anand-puthiyapurayil/AI_Methodology.git
cd AI_Methodology

2. Set Up the Environment

Create a virtual environment with Python 3.10:

conda create --name "envname" python=3.10
conda activate envname

Install required dependencies:

pip install -r Requirements.txt

3. Data Setup

Download the dataset from Kaggle and place it under:

data/External/

4. Run the Project

A. Exploratory Data Analysis (EDA)

Start Jupyter Notebook:

jupyter notebook

Open and run notebooks/EDA.ipynb for detailed exploratory analysis.

B. Model Training & Inference

Run scripts in the following order:

python scripts/preprocess.py
python scripts/train_model.py
python scripts/inference.py
python scripts/main.py

C. MLFLOW Integration

To track experiments and manage model versions, run:

jupyter notebook

Open notebooks/MLFLOW.ipynb to log experiments, track parameters, metrics, and manage model lifecycle.

5. Model Explainability

Use SHAP for interpretability and explainability:

jupyter notebook

Run notebooks/Model_Explainability.ipynb to analyze model predictions and understand feature impacts clearly.

6. Deactivate Environment

After completion:

conda deactivate

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🛠️ Tech Stack

• Programming Language: Python
• Libraries: Pandas, NumPy, Scikit-learn, SHAP, Matplotlib
• MLOps Tools: MLflow
• Environments: Jupyter Notebook, Conda

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🌟 Key Features

• Comprehensive EDA: Gain insights from data before modeling.
• End-to-End Pipeline: Automates preprocessing, training, inference.
• MLflow Integration: Robust tracking, logging, and model management.
• Explainability: Transparent ML model decisions through SHAP.

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🤝 Contributions

Feel free to contribute:

• Fork this repository.
• Create your branch (git checkout -b feature/my-feature).
• Commit changes (git commit -m 'Added new feature').
• Push your changes (git push origin feature/my-feature).
• Create a pull request.

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📞 Contact

• Email: anand.nelliot@gmail.com
• LinkedIn: Anand Puthiyapurayil

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✨ Happy Modeling! ✨