Predict Star Rating from Movie Review- Natural Language Processing (Oct 2021)

This machine learning project involved building a model to predict Amazon movie review star ratings using natural language processing techniques. Working with a dataset of 1.7 million reviews, I performed text preprocessing, feature extraction, and sentiment weighting using NLTK and TextBlob, transforming raw unstructured text into meaningful input for modeling.

This project objective was to predict review ratings as accurately as possible while experimenting with different ML approaches. With 179 total entrants, my submission ranked 32nd out of 150+ active competitors, a milestone that validated my early understanding of real-world ML workflow and experimentation.

To evaluate performance, I tested and compared several models (including Random Forest, Decision Tree, and k-Nearest Neighbors (KNN) ) achieving an RMSE of 0.8. I applied K-fold cross-validation and confusion matrix evaluation to verify the model’s generalizability and reliability, gaining hands-on experience in data handling, feature engineering, tuning, and model interpretation.