Objective: Classified user reviews into Positive, Neutral, and Negative categories.
Key Features: Analyzed app names, cleaned reviews, sentiment labels, polarity, and subjectivity.
Methods: Utilized TF-IDF for feature extraction, built Random Forest and Logistic Regression models, and handled class imbalance using Random UnderSampler.
Outcome: Developed a function to predict sentiment from new review inputs.