Assignment 1: Movie Revenue Prediction Goal: Predict movie revenue and classify success. Insight: Strong correlation between budget and revenue (r = 0.74). Models: Linear Regression: R² = 0.62. Logistic Regression: Accuracy = 82%, AUC = 0.87. Key Feature: Budget.

Assignment 2: Movie Classification with KNN & Random Forest KNN: Best at K = 9 (Accuracy ≈ 89%). Random Forest: Accuracy = 97%, but poor recall for successful movies. Issue: Imbalanced data (only 4% are successful movies). Suggestions: Use oversampling and better feature engineering.

Assignment 3: Breast Cancer Classification Dataset: From sklearn, no missing values. Classes: 357 benign, 212 malignant. Preprocessing: Train-test split and scaling. Note: Results for models are not yet included (Parts 2 & 3 still in progress).