Akilesh Madrimath

I’m a B.Tech Computer Science graduate (2025) building with strong foundations in Python, OOP, and SQL, with a clear bias toward data-driven problem solving. I’m excited to apply hands-on ML and data engineering skills in collaborative, fast-paced teams.

In my recent AI & Machine Learning internship, I designed and evaluated 4 production-grade ML classifiers—Logistic Regression, Random Forest, XGBoost, and SVM—reaching up to 88% accuracy. Using an OOP-based pipeline architecture with 5-fold cross-validation and ROC-AUC benchmarking, I improved F1-score by 12% (0.74 to 0.83) through feature engineering such as polynomial features, log transforms, and target encoding.

I also automated end-to-end ML pipelines from preprocessing to evaluation, cutting per-experiment turnaround from 4 hours to 45 minutes. By documenting pipelines for maintainability and working with senior engineers to translate business requirements into modular Python code, I practiced agile, cross-functional delivery.

Earlier, as a Data Science Intern, I cleaned and standardized 3 structured datasets using Python (Pandas/SQL), delivering analysis-ready tables 2 days ahead of schedule. I combined EDA with statistical hypothesis testing to surface 6 actionable business insights and packaged results into stakeholder-ready reports—while building projects like a content-based recommendation engine and a Fashion MNIST CNN for measurable performance.