Revenue Prediction Model Development

Assessed relationships between revenue and features like budget, runtime, popularity, and language. Budget showed the highest correlation (~0.75) and was the strongest individual predictor. Visualized insights using scatter plots and correlation matrices.

Engineered numerical features from JSON fields, such as number of male cast members and production companies. Built a multiple linear regression model using scikit-learn. Budget and popularity were the most significant predictors. The final model achieved an R² score of ~0.38 and was validated through residual analysis.

Evaluated the model’s assumptions by analyzing the distribution of residuals. A histogram revealed a roughly symmetric shape centered around zero, supporting the validity of the linear model for this dataset.