Palak Gupta👋
Turning data into insights with my Strategic Data Analysis
Portfolio Project 1:
Google Trend Analysis
Overview
The Google Trend Analysis project focused on leveraging real-time search data to understand public interest patterns and emerging topics. By using Google Trends and dynamically pulling live data, the project aimed to analyze trending topics, visualize data shifts, and derive actionable insights from user behavior across regions and timeframes.
Research: Initial research focused on identifying key topics across domains (e.g., tech, health, politics) and how user interest changes over time and geography. The goal was to uncover seasonal patterns, spikes in search behavior, and regional differences.
Information Architecture: Data was categorized based on keywords, timeframes (daily, weekly, monthly), and geographical locations to create a structured approach for analysis. The data was also tagged by industry and topic category.
Wireframing and Prototyping: A dashboard prototype was designed to display trending topics using charts and maps, showing real-time fluctuations and comparisons between regions or search terms.
Challenges
Real-Time Data Handling:
- Challenge: Pulling, processing, and visualizing real-time data efficiently without lag.
- Solution:Used Google Trends API via pytrends for scheduled data fetching and caching strategies to reduce API load.
Data Noise and Relevance:
- Challenge: Filtering irrelevant or misleading spikes in data caused by bot activity or viral anomalies.
- Solution:Applied smoothing techniques and cross-referenced search terms to ensure trend validity.
Regional Discrepancies:
- Challenge:Same term showing varied meaning or interest across different regions.
- Solution: Geo-tagged trend data and included language or context filtering based on location.
Visualization Complexity:
- Challenge:Designing an interface that cleanly displays fluctuating real-time data.
- Solution:Used dynamic visualizations (line graphs, heatmaps) built with Plotly and Streamlit for interactive exploration.
Results/Conclusion:
The real-time Google Trend Analysis dashboard provided deep insights into what people are searching for globally and locally. The system enabled timely detection of viral trends and patterns, proving valuable for content planning, market research, and consumer behavior analysis. The interactive interface, combined with live data updates, made it a compelling and practical tool for data-driven decision-making. Future iterations may include sentiment analysis and integration with social media data for even deeper context.
