Abhigna Bollam

I’m an AI/ML student and Business Analyst who turns requirements into clear functional and technical specifications. In my current role at [Turing], I gather and document business requirements, translating them into practical specs stakeholders can act on.

I analyze datasets with SQL and Python to identify trends, generate insights, and track key performance indicators (KPIs). I also develop dashboards and structured reports to support data-driven decision-making, along with gap analysis and process improvement recommendations.

My projects reflect how I work end-to-end with machine learning: I built a Flood Forecasting System that preprocesses data, selects features, and compares models like SVM, Logistic Regression, KNN, and MLP—achieving the highest accuracy with MLP. I then deployed a Django-based web application for real-time flood prediction and early warning support.

I also developed a Fake Currency Detection model using Random Forest Classifier, extracting features (Variance, Skewness, Kurtosis, Entropy) from wavelet-transformed images from the UCI Banknote Authentication Dataset. I’m adaptable and a quick learner, and I’m eager to contribute to innovative teams while continuously upgrading my technical skills.