About

This project predicts momentum spikes in trending topics by analyzing data from Google Trends and Hacker News. The workflow:

Data Collection: Loads trending topics from Google Trends and Hacker News
Data Normalization: Unifies topics across both sources
Feature Engineering: Builds time-series features to capture momentum patterns
Labeling: Identifies topic "explosions" (sudden spikes in interest)
Model Training: Trains a momentum classifier to predict these explosive growth moments
Evaluation: Tests the classifier on held-out data
This is useful for trend prediction — identifying which emerging topics are about to blow up before they peak. Perfect for content creators, investors, or anyone wanting to stay ahead of viral trends!