I am a 2nd year BE AI & ML student at Francis Xavier Engineering College, focused on building real-world Machine Learning systems and preparing for ML internships.
Currently, I am learning:
- Machine Learning
- Deep Learning
- Data Structures & Algorithms
- FastAPI Deployment
- End-to-End ML Projects
- Built an end-to-end customer churn prediction pipeline using Logistic Regression, Random Forest, XGBoost, FastAPI, and threshold optimization.
- Focused on minimizing business cost instead of only maximizing accuracy.
- Public API deployed with FastAPI.
Repository: https://github.com/Raja-ML-22/cost-optimized-churn-ml-system
- Built and deployed a sentiment analysis API using PyTorch, TF-IDF, FastAPI, and IMDb movie reviews.
- Trained on 40K reviews and evaluated on 10K reviews.
- Achieved 82.29% accuracy and 81.86% F1-score.
Repository: https://github.com/Raja-ML-22/sentiment-analysis-api
- Built a fraud detection model on highly imbalanced transaction data.
- Used Logistic Regression, Random Forest, class balancing, and PR-AUC evaluation.
- Focused on maximizing fraud recall while controlling false positives.
Repository: https://github.com/Raja-ML-22/credit-card-fraud-detection
- Analyzed the relationship between Fear & Greed market sentiment and trader performance.
- Built visualizations and extracted insights from historical trading behavior.
Repository: https://github.com/Raja-ML-22/trader_analysis
Languages:
- Python
- SQL
Machine Learning:
- Scikit-learn
- Pandas
- NumPy
- XGBoost
- PyTorch
Backend & Deployment:
- FastAPI
- Uvicorn
- Render
- GitHub
Currently Practicing:
- DSA
- LeetCode
- ML Interview Preparation
- Secure an ML internship at a product-based company
- Continue building deployable ML projects
- Become an AI/ML Engineer
- Email: p.esakkiraja22@gmail.com
- GitHub: https://github.com/Raja-ML-22
- LinkedIn: https://www.linkedin.com/in/esakki-raja-4b9980327/