Movie_Revenue_Predictor

🎬 About the Project – Movie Revenue Predictor 🚀 Inspiration The entertainment industry is driven by both creativity and business. While making a great film is art, understanding its financial potential is a science. We were inspired to bridge this gap — to empower creators, producers, and investors with predictive insights before a movie hits the screens. This tool helps answer a crucial question: “Will this movie make money?”

🧠 What We Learned Throughout this project, we deepened our understanding of:

Data preprocessing & ML modeling for real-world predictions

The importance of feature engineering in domains like media

Frontend-backend integration for real-time AI applications

Creating smooth user experiences with React, TailwindCSS, and Vite

🛠️ How We Built It The Movie Revenue Predictor is powered by:

Python & scikit-learn for machine learning (Random Forest/Logistic Regression)

Node.js + Express to handle API requests and run the ML scripts via child processes

MongoDB for storing user prediction data

React + Tailwind CSS + Vite for the responsive frontend UI

Hosted on Vercel (frontend) and Render (backend)

User inputs like genre, budget, screen count, franchise/remake status, etc., are sent to the backend, where the model predicts the expected box office revenue. The prediction is displayed and also logged in the database for analytics.

⚠️ Challenges We Faced Data cleaning: Budget and revenue fields had inconsistencies which required thorough pre-processing.

Dynamic ML Execution: Running Python scripts from Node.js and handling stdout correctly.

Deployment hiccups: Vite’s proxy didn't work with Vercel in production, so we had to rely on direct backend URLs.

Model accuracy: Choosing the right features that affect revenue and avoiding overfitting on skewed data.

💡 Final Thought This project helped us blend data science with software engineering to solve a real-world problem. It’s a stepping stone toward AI-enabled filmmaking decisions, and we're excited to keep evolving it with better models and richer datasets.

Built With

• express.js
• ml
• mongodb
• node.js
• numpy
• python
• react/vite