Artificial Intelligence and Machine Learning Student
Artificial Intelligence and Machine Learning student with hands-on internship and project experience in machine learning, NLP, computer vision, data analysis, and predictive modeling.
IBM Developer Skills Network
Jul 2025 - Aug 2025 · Remote Virtual
Built a CNN image classifier achieving 92% validation accuracy using data augmentation and hyperparameter tuning. Developed salary prediction using Linear Regression and Random Forest, improving accuracy by 18% over baseline. Implemented Amazon review sentiment analysis using TF-IDF and Naive Bayes, achieving 88% accuracy with improved F1-score. Technologies used included Python, NLP, and CNN.
Bachelor of Technology, Artificial Intelligence and Machine Learning
Jul 2023 - Jul 2027
CGPA: 7.38 / 10
Developed a machine learning model to estimate used bike resale prices based on brand, model year, mileage, fuel type, and engine capacity. Performed data cleaning, feature engineering, and Random Forest regression modeling, and deployed a user-interactive interface for real-time predictions using Python, Pandas, Scikit-learn, Flask, HTML/CSS, and Git.
Analyzed 17 IPL seasons from 2008 to 2025, covering more than 1,000 matches and 800 players, to identify performance, venue, and win-pattern trends. Built an interactive dashboard with more than 10 performance metrics and automated visual reports using Python, Pandas, NumPy, and Matplotlib.
Built a hybrid movie recommender using TF-IDF and cosine similarity across more than 5,000 movies, improving relevance by 20% over genre-based filtering. Applied VADER sentiment analysis to more than 10,000 IMDb reviews, achieving 88% classification accuracy for audience mood detection. Used Python, Flask, Scikit-learn, and NLTK.