AI/ML · 2025
Movie Genre Classifier (93% Accuracy)
Machine learning classifier that predicts movie genres from text data with 93% accuracy and a Streamlit demo.
Case study
The story
A supervised learning project that classifies movie genres using textual metadata and TF-IDF features.
The problem
Manual genre classification is slow and inconsistent when datasets grow. The goal was to automate labeling with strong accuracy.
The build
Built a preprocessing pipeline and trained a Random Forest model on TF-IDF vectors, then packaged results in a Streamlit demo for easy exploration.
What it changed
- Achieved 93% accuracy on 5,000+ records.
- Delivered a working Streamlit demo for stakeholders.
- Led the final project presentation and results walkthrough.
My part
- Performed data cleaning, preprocessing, and feature extraction.
- Trained and tuned a Random Forest classifier with TF-IDF vectors.
- Led the final presentation and delivered model insights.
What it does
- TF-IDF based text vectorization
- Random Forest classification pipeline
- Streamlit demo for interactive predictions
Hard parts
- Balancing precision and recall across multi-genre categories.
- Communicating model performance to non-technical audiences.
Stack
Python · Pandas · NumPy · Scikit-learn · NLP (TF-IDF) · Random Forest · Streamlit
Screens
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