Helpful Review Predictor

• A supervised machine learning project that classifies the helpfulness of product reviews using a Random Forest model.
• Includes data preprocessing, feature engineering, model tuning (using GridSearchCV), model comparison (logistic regression v.s. random forest), and performance evaluation (based on accuracy, precision, recall, confusion matrices, roc, and auc).

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• Trained on the bookReviewsData.csv data set which contains two columns:
  • Review: the review text
  • Helpful Review: binary label (1 = helpful, 0 = not helpful)
• Additional engineered features:
  • Number of exclamation marks
  • Number of question marks
  • Number of periods
  • Review length
• The dataset was cleaned and balanced before modeling.