This project uses Python-based data analysis using the Google Trends API (PyTrends) to investigate regional and worldwide search trends.
TrendMining: Google Search PyTrends-Based Analytics
This project uncovers global and regional search interest using Google Trends data through the unofficial PyTrends API. It enables users to explore what the world is searching for, how trends evolve over time, and how search behavior varies across countries and Google platforms like YouTube, News, and Web Search.
By leveraging Python, data visualization, and geospatial mapping, this project brings search trend data to life in an interpretable and visually compelling format.
To mine, analyze, and visualize search patterns for selected keywords across the globe, helping decode audience intent and interest using publicly available Google Trends data.
- 🐍 Python
- 🧪 Pandas, NumPy – for data processing
- 📊 Seaborn, Matplotlib, Plotly – for visualizations
- 🌐 PyTrends – Google Trends data extraction
- 🌍 Plotly Choropleth Maps – for geospatial search mapping
- Extracted real-time and historical trend data for one or multiple keywords.
- Created dynamic visualizations to compare search interest across countries.
- Segmented data by Google property (e.g., Web, YouTube, News).
- Time-series plots showing rising and falling interest over months or years.
- Regional analysis using interactive maps and bar charts.
- Which countries show the highest interest for a topic.
- How platform-based trends (e.g., YouTube vs Web Search) vary.
- Trend evolution across time using line graphs.
- Potential seasonal patterns or spikes in public interest.
