Naga Phanindra

I've built end-to-end machine learning, data processing, and computer vision solutions, from requirements analysis and data cleaning through testing and documentation.

For a mental health prediction system, I benchmarked Logistic Regression, SVM, Gradient Boosting, AdaBoost, and HistGradientBoosting models, selecting HistGradientBoosting after it achieved 99% accuracy. The solution supports early-detection and clinical decision-support use cases.

I've also developed a CNN-Transformer hybrid lane-detection application for ADAS conditions including rain, fog, and low light, and optimized it for real-time performance. In Python and OpenCV, I created a low-light image enhancement pipeline using CLAHE, gamma correction, and filtering for biometric verification on low-power embedded platforms.

I'm completing a B.Tech in Computer Science Engineering and bring hands-on skills in Python, SQL, Snowflake, dbt, Power BI, Git/GitHub, and software development fundamentals. I'm ready to contribute analytical thinking, coding fundamentals, and a continuous-learning mindset to quality software delivery.