Instagram Reels Virality Predictor
A classification model exploring whether Instagram Reels metadata (duration, hook strength, niche, music type) can predict viral performance. Built as an end-to-end ML learning project, from exploratory data analysis through feature engineering, dimensionality reduction, model training, and deployment.
✓ Retains full feature interpretability
↓ 6.25% accuracy drop
The signal under the noise.
Niche and music type emerged as the strongest categorical predictors, content category and audio choice matter more than caption or hashtag strategies.
Video duration and hook strength showed moderate predictive power, the first 3 seconds and optimal length are worth testing systematically.
Baseline model outperformed PCA, keeping full feature interpretability proved more valuable than dimensionality reduction for this dataset.
Based on logistic regression coefficients
- Exploratory Data Analysis
- Feature Engineering
- PCA Dimensionality Reduction
- Logistic Regression Modeling
- Model Evaluation & Comparison
- Streamlit App Deployment
Key takeaway
This project demonstrates an end-to-end ML workflow, from data exploration through deployment. While accuracy is modest (51%), the interpretable baseline model reveals actionable insights: content niche and music type are stronger predictors of virality than video length or hook strength alone.
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