Developed and trained based on a Comprehensive Film Statistics Dataset, this Worldwide Gross Revenue ($) Predictive Model for Movies served as my final submission for CAI2C08 Machine Learning. With an approximately 67% Adjusted-R² Value, my extensive analysis and training catered quite well towards the complexity, depth, and imbalance of this dataset. Hosted via Streamlit, my application of this model can be accessed here.
Inclusive of my well-researched Report Analysis of the dataset, I gained valuable insights into the film industry by approaching it from a more analytical and data-driven perspective through the recognition of inherent challenges and unpredictability of box office performances.
The Box Office Analyser Repository includes several key features designed to provide insights into movie performance and revenue predictions:
- Predictive Model: Utilises my well-trained advanced machine learning algorithms to predict worldwide gross revenue based on various input features.
- Data Visualisation: Interactive charts and graphs to visualise relationships between impactful features.
- Feature Importance Analysis: Identifies which features most significantly impact revenue predictions, helping users understand the driving factors behind box office success.
- User-Friendly Interface: A Streamlit-based application that enables users to input movie data and receive instant predictions and insights.
- Exploratory Data Analysis (EDA): Provides visualisations and statistics to explore the dataset, helping users identify trends and patterns in movie performance.
| Actual Gross Revenue ($) | Predicted Gross Revenue ($) | Percentage Difference (%) |
|---|---|---|
| 17, 475, 475 | 22, 152, 180.67 | 26.8 |
| 53, 191, 101 | 58, 329, 482.08 | 9.6 |
pandas: For data manipulation and analysis.numpy: For numerical operations and handling arrays.seaborn: For data visualisation and statistical graphics.matplotlib: For creating static, animated, and interactive visualisations.scikit-learn: For implementing machine learning algorithms and model evaluation.optuna: For hyperparameter optimisation.joblib: For saving and loading models.catboost,xgboost,lightgbm: For gradient boosting algorithms tailored for regression tasks, all inclusive of my tree-based algorithm choices.
pip install -r requirements.txt
streamlit run application.py