IT student specializing in Artificial Intelligence. Completed multiple projects utilizing machine learning and web technologies. Seeking opportunities to apply programming skills in Python, Java, and web development.
Built a complete end-to-end pipeline for cleaning, transforming, and analyzing a large movies dataset. Engineered features such as genre counts, ROI, rating strength, and movie age to improve predictive power. Implemented multiple models (Linear Regression, Logistic Regression, Random Forest) and compared performance using accuracy, precision, recall, and F1-score. Achieved over 80% accuracy in classifying movies as high-rated (≥7/10). Designed clear visualizations (rating distributions, genre trends, popularity vs. rating) to support insights. Tools & Technologies: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn.
Developed a responsive, multi-page movie discovery platform with sections for trending films, top-rated movies, actor profiles, news, and contact. Built with HTML and CSS, featuring a clean cinematic UI, integrated search bar, smooth page navigation, and consistent branding across the site.