MovieIQ — Predictive Analytics on Film Success
MovieIQ is an end-to-end Data Science and Machine Learning web application built using Python and Streamlit. It analyzes historical film data to uncover success drivers and uses a Random Forest classifier to predict whether a planned movie will be commercially Successful or a Non-Success (Financial Loss).
Live Streamlit App: https://movieiq-analyzer.streamlit.app
Features
- Exploratory Data Analysis (EDA): Interactive visual insights on Budget vs. Revenue, Success Rates by Genre, and Popularity distributions.
- Statistical Testing: Validated key correlations using T-Tests (
$p < 0.05$ ) and Chi-Square tests ($p < 0.05$ ). - Success & Risk Predictor: Enter custom movie parameters (Budget, Popularity, Runtime, Ratings, Genre) to get real-time machine learning predictions with confidence scores.
- Custom Genre Parsing: Built-in JSON/dict parsing to handle complex nested metadata across 15+ genres.
Tech Stack & Dependencies
- Language: Python 3.10+
- Data Manipulation:
pandas,numpy - Machine Learning:
scikit-learn,joblib - Visualization:
matplotlib,seaborn - Web App & Deployment:
streamlit, GitHub, Streamlit Community Cloud
Local Installation & Running
- Clone the repository:
git clone [https://github.com/Aadi394210/MovieIQ-.git](https://github.com/Aadi394210/MovieIQ-.git) cd MovieIQ-