Movie Revenue Prediction

arXiv Paper | Academia Paper

Introduction

In the contemporary film industry, accurately predicting a movie's earnings is paramount for maximizing profitability. This project aims to develop a sophisticated machine learning model to forecast movie earnings based on a comprehensive set of input features, including the movie name, MPAA rating, genre, year of release, IMDb rating, votes by the watchers, director, writer, leading cast, country of production, budget, production company, and runtime.

Numerous factors influence a movie's earnings, and the optimal combination of these factors remains elusive. Our machine learning model seeks to uncover the most significant factors for box office success by analyzing real data from a diverse array of movies produced globally.

We hypothesize that certain parameters, such as the director's track record and the genre of the film, hold more significance in predicting movie revenue than others. Observations suggest that despite lower IMDb ratings, action-oriented films often perform well at the box office, while genres such as comedy or emotional dramas, despite potentially higher IMDb ratings, may not achieve comparable revenue outcomes. These insights highlight the complex interplay between film attributes and audience preferences.

By leveraging these diverse attributes, our goal is to construct a robust predictive model that can offer valuable insights and aid decision-making in the film industry. Ultimately, this will help filmmakers optimize their movie production strategies for maximum profit and popularity.

Directory Structure

Movie-Revenue-Prediction/
│
├── fig
│   └─ intro.png
│
├── Helper Files
│   ├── Best Features
│   │   ├── feature_scores.py
│   │   ├── feature_scores.txt
│   │   ├── significant_features.py
│   │   └── significant_features.txt
│   ├── budgetxgross.py
│   ├── data_visualization.py
│   ├── gross_histogram.py
│   ├── null_values_check.py
│   └── pie_chart.py
│
├── Misc
│   └─ initial_try.py
│
├── models
│   ├── accuracies.txt
│   ├── decision_tree_bagging.py
│   ├── decision_tree.py
│   ├── gradient_boost.py
│   ├── linear_regression_pca.py
│   ├── linear_regression.py
│   ├── random_forest.py
│   ├── tracking_XGBoost.py
│   └── XGBoost.py
│
├── old datasets
│   ├── finalised dataset
│   │   ├── dataset_modified.py
│   │   ├── masti.csv
│   │   ├── new_updated_less-than-1b-dataset.csv
│   │   ├── new_updated_less-than-350m-dataset.csv
│   │   ├── old_data.csv
│   │   └── updated_masti.csv
│   ├── initial
│   │   ├── initial_dataset.csv
│   │   └── initial_merge.csv
│   ├── Intermediate
│   │   ├── intermediate_dataset.csv
│   │   └── intermediate_merge.csv
│   │   └── intermediate1_dataset.csv
│   ├── Kaggle
│   │   ├── IMDb 5000+.csv
│   │   ├── movie_data_imdb.csv
│   │   ├── movie_metadata.csv
│   │   └── top_500_movies.csv
│   │
│   ├── data_builder_check.py
│   ├── dataset.csv
│   ├── dataset2.csv
│   ├── final_dataset.csv
│   ├── final_merge.csv
│   └── README.md
│
├── Reports
│   ├── Proposal
│   │   ├── proposal.pdf
│   │   └── proposal.tex
│   ├── 1st Project Report
│   │   ├── 1st_Project_Report.pdf
│   │   ├── 1st_Project_Report.tex
│   │   └── pics
│   │       ├── gross_histogram.png
│   │       ├── k_best.png
│   │       ├── model_accuracy_plot.png
│   │       ├── null_values.png
│   └── Final Report
│       ├── Final_Report.pdf
│       ├── Final_Report.tex
│       └── pics
│           ├── gross_histogram.png
│           ├── k_best.png
│           ├── model_accuracy_plot.png
│           ├── null_values.png
│           ├── pie_chart.png
│           └── R2_score_tracking.png
│
├── revised datasets
│   ├── movies.csv
│   └── output.csv
│
├── .gitignore
├── LICENSE
├── main.py
├── README.md
└── requirements.txt

Getting Started

All our code was tested on Python 3.6.8 with scikit-learn 1.3.2. Ideally, our scripts require access to a single GPU (uses .cuda() for inference). Inference can also be done on CPUs with minimal changes to the scripts.

Setting up the Environment

We recommend setting up a Python virtual environment and installing all the requirements. Please follow these steps to set up the project folder correctly:

git clone https://github.com/Vikranth3140/Movie-Revenue-Prediction.git
cd Movie-Revenue-Prediction

python3 -m venv ./env
source env/bin/activate

pip install -r requirements.txt

Setting up Datasets

The datasets have been taken from Movie Industry dataset.
Detailed instructions on how to set up our datasets are provided in old datasets\README.md.

Running the Models

You can run the models using:

python <model_name>.py

The model_name parameter can be one of [linear_regression, decision_tree, random_forest, decision_tree_bagging, gradient_boost, XGBoost].

Data Preprocessing

We provide scripts for data preprocessing, including handling missing values, encoding categorical variables, and feature selection.

Handling Missing Values

Missing values are handled using the data_preprocessing.py script:

python data_preprocessing.py

Encoding Categorical Variables

Categorical variables are encoded using Label Encoding. This is implemented in the data_preprocessing.py script.

Feature Selection

We use SelectKBest for feature selection, as implemented in the data_preprocessing.py script.

Model Improvement

We employ several strategies for model improvement, including standardizing data, applying logarithmic transformations, and hyperparameter tuning using GridSearchCV.

Standardizing Data

To ensure consistent scaling across features, we use Standard Scaler.

Logarithmic Transformations

Logarithmic transformations are applied to skewed data (e.g., budget and gross revenue).

Hyperparameter Tuning

Hyperparameter tuning is performed using GridSearchCV to optimize model parameters.

Command Line Interface (CLI)

A CLI is developed to allow users to input movie features and get revenue predictions. Users can select different models for prediction.

Running the CLI

1. Navigate to the project directory.
2. Run the CLI:

   python main.py

3. Follow the prompts to input the movie features and choose the prediction model.

Streamlit Web Interface

Additionally a web interface is also developed using Streamlit to allow users to input movie features and get revenue predictions.

Running the Web Interface

1. Navigate to the project directory.
2. Run the Web Interface:

   streamlit run streamlit_app.py

3. Follow the prompts to input the movie features and choose the prediction model.

Results

The Gradient Boosting model achieved the best performance with:

• Training Accuracy: 91.58%
• Testing Accuracy: 82.42%

The model evaluation results for all models are as follows:

Model	Training R²	Training MAPE	Testing R²	Testing MAPE
Linear Regression	0.6553	35.23%	0.6706	18.49%
Decision Tree	0.8664	13.00%	0.6947	4.60%
Bagging	0.8583	13.32%	0.7719	5.67%
Gradient Boosting	0.9158	10.57%	0.8242	5.69%
XGBoosting	0.9079	9.70%	0.8102	5.53%
Random Forest	0.8728	14.29%	0.7786	5.33%

Conclusion

The developed Gradient Boosting model demonstrates promising accuracy and generalization capabilities, facilitating informed decision-making in the film industry to maximize profits.

Citation

If you found this work useful, please consider citing it as:

@misc{udandarao2024movie,
      title={Movie Revenue Prediction using Machine Learning Models}, 
      author={Vikranth Udandarao and Pratyush Gupta},
      year={2024},
      eprint={2405.11651},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Contact

Please feel free to open an issue or email me at vikranth22570@iiitd.ac.in.

License

This project is licensed under the MIT License.