XGBoost_film_analysis

Simple python script for using XGBoost decision tree ensemble to predict profit factor for film titles. Features include: rating, category, replacement cost, total revenue, film copy count, number of times rented, and inventory cost.

XGBoost Film Analysis

Welcome to the XGBoost Film Analysis repository, a Python script for leveraging XGBoost, a decision tree ensemble, to predict the profit factor for film titles. This script provides a practical tool for data analysis and predictive modeling in the context of the film industry.

Table of Contents

• Introduction
• Features
• Getting Started
• Usage
• Results
• Contributing
• License

Introduction

In the dynamic world of the film industry, understanding the profitability of film titles is a crucial aspect of decision-making. The XGBoost Film Analysis script aims to streamline this process by predicting the profit factor, a key metric that represents the relationship between costs and revenue.

Features

• Film Title Characteristics: The script considers various film characteristics, including rating, category, replacement cost, total revenue, film copy count, number of times rented, and inventory cost. These features are essential for a comprehensive analysis of a film's financial performance.

• XGBoost Modeling: XGBoost, a powerful machine learning algorithm, is employed to build an accurate predictive model. The ensemble of decision trees helps capture intricate patterns within the data.

• SHAP Values: To gain insights into feature importance, the script utilizes SHAP (SHapley Additive exPlanations) values. SHAP values provide a clear understanding of how each feature contributes to the model's predictions.

Getting Started

Before you can use this script, make sure you have the required libraries installed.

Usage

To use the XGBoost Film Analysis script, follow these steps:

1. Prepare your film data in a suitable format, ensuring it contains the required features.
2. Run the script by executing the following command:
3. The script will load the data, perform XGBoost modeling, predict the profit factor, and provide insights into feature importance using SHAP values.
4. Review the results and analysis to make informed decisions regarding film profitability.

Results

The XGBoost Film Analysis script provides valuable insights into film profitability. You can expect:

• Accurate Predictions: The XGBoost model delivers accurate profit factor predictions based on film characteristics.
• Feature Importance: SHAP values reveal the significance of each feature, aiding in understanding which factors influence a film's financial success.

Contributing

If you have suggestions for improvements or would like to contribute to this project, please open an issue or pull request. Your input is highly appreciated.