Predictive ML Model for Movie Success
Budget: ₹12,500 – ₹37,500 INR
# Build a machine learning solution to predict a movie’s success category (Hit, Average, or Flop) before release by analyzing historical movie data. The project should involve collecting and preprocessing movie datasets containing features such as budget, revenue, cast popularity, user and critic reviews, genres, release year, and social media engagement. IMDB scores should be transformed into categorical classes to frame the problem as a multi-class classification task.
# Perform exploratory data analysis (EDA) to identify key factors influencing movie success and engineer meaningful features (e.g., profit, engagement metrics, log-transformed financial variables). Train and evaluate multiple machine learning models such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and XGBoost. Compare model performance using metrics like accuracy, precision, recall, and ROC–AUC, and handle class imbalance if present.
# Finally, visualize model results using ROC curves and build an interactive dashboard (Streamlit or Power BI) to present predictions and insights that can support decision-making for producers and marketing teams.
Expected outcome:
A robust predictive model with clear insights into the factors driving movie success, along with visual dashboards that communicate results effectively to non-technical stakeholders.
# Perform exploratory data analysis (EDA) to identify key factors influencing movie success and engineer meaningful features (e.g., profit, engagement metrics, log-transformed financial variables). Train and evaluate multiple machine learning models such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and XGBoost. Compare model performance using metrics like accuracy, precision, recall, and ROC–AUC, and handle class imbalance if present.
# Finally, visualize model results using ROC curves and build an interactive dashboard (Streamlit or Power BI) to present predictions and insights that can support decision-making for producers and marketing teams.
Expected outcome:
A robust predictive model with clear insights into the factors driving movie success, along with visual dashboards that communicate results effectively to non-technical stakeholders.