Description

I design, build, evaluate, and deploy machine learning solutions that solve real-world problems and integrate seamlessly into production systems. My approach focuses on data quality, model performance, and measurable outcomes, ensuring that every model delivers reliable and explainable results.

Key Features

• End-to-End ML Pipelines: Data collection, preprocessing, feature engineering, model training, validation, and deployment.

• Custom Model Development: Classification, regression, clustering, time-series forecasting, and computer vision models.

• Model Optimization: Hyperparameter tuning, cross-validation, and performance benchmarking.

• Evaluation Metrics & Validation:

  • Classification: Accuracy, Precision, Recall, F1-score, ROC-AUC

  • Regression: MAE, MSE, RMSE, R²

  • Computer Vision: Confusion Matrix, IoU, Dice Coefficient

• Explainability & Insights: Model interpretability using feature importance and visual diagnostics.

• Production Deployment: Models packaged as APIs or integrated into web and mobile applications.

• Monitoring & Retraining: Tracking model drift and maintaining long-term performance.

Technologies Used

• Languages: Python

• ML Frameworks: TensorFlow, Keras, Scikit-learn

• Data Processing: Pandas, NumPy

• Visualization & Analysis: Matplotlib, Seaborn

• Model Evaluation Metrics:
  Accuracy, Precision, Recall, F1-score, ROC-AUC, MAE, MSE, RMSE, R², Confusion Matrix

• Deployment: Flask, FastAPI, Docker

• Cloud Platforms: AWS, Google Cloud Platform (GCP)

Design Highlights

• Metrics-driven model evaluation for objective performance assessment

• Reproducible experiments and clean ML pipelines

• Production-ready models designed for scalability and reliability