A comprehensive data analysis project that explores Google search trends using Python and various visualization libraries. This project analyzes search interest patterns, geographical distributions, and temporal trends for specific keywords.
This project demonstrates how to:
- Extract Google search trend data using the
pytrendslibrary - Analyze regional search patterns and interests
- Visualize temporal trends over time
- Compare multiple keywords' search patterns
- Create interactive visualizations with Plotly
- Regional Analysis: Identify top countries searching for specific keywords
- Temporal Analysis: Track search trends over time periods
- Multi-keyword Comparison: Compare search patterns across different terms
- Interactive Visualizations: Create engaging charts and maps
- Data Export: Save analysis results for further processing
Project-1/
├── google_searching_trend_analysis.ipynb # Main Jupyter notebook
├── data-export (1).csv # Exported Google Analytics data
└── README.md # This file
- Python 3.x
- pytrends: Google Trends data extraction
- pandas: Data manipulation and analysis
- matplotlib: Static plotting and visualization
- seaborn: Statistical data visualization
- plotly: Interactive visualizations
- Jupyter Notebook: Interactive development environment
Before running this project, ensure you have the following installed:
pip install pandas matplotlib seaborn plotly pytrends jupyter- Extracts search interest by region/country
- Identifies top 10 countries with highest search interest
- Creates bar charts showing geographical distribution
- Tracks search interest over time (numbers of 'n'-month periods)
- Generates line plots showing trend evolution
- Supports custom time frame analysis
- Compares search patterns across multiple keywords
- Analyzes trending topics in data science and AI fields
- Creates comparative line charts
- Generates choropleth maps for geographical analysis
- Creates interactive plots using Plotly
- Supports VS Code and Jupyter notebook rendering
The project includes analysis of keywords such as:
- "computer science" (example search term)
- "data science"
- "machine learning"
- "AI"
- "deep learning"
- Clone or download this repository
- Install dependencies using pip
- Open
google_searching_trend_analysis.ipynbin Jupyter - Run cells sequentially to execute the analysis
- Modify keywords in the notebook to analyze different search terms
- View results including charts, maps, and data exports
- Google Trends API: Primary data source via pytrends
- Google Analytics: Supplementary data from
data-export (1).csv - Real-time data: Updated search trend information
- Bar Charts: Country-wise search interest
- Line Plots: Time-series trend analysis
- Choropleth Maps: Interactive world maps
- Comparative Charts: Multi-keyword analysis
- Custom Styling: Professional visualization themes
The notebook supports various configuration options:
- Time frames: 1 month, 12 months, custom periods
- Geographical scope: Global, country-specific, or regional
- Language settings: English (US) with Bangladesh timezone
- Visualization themes: Customizable color schemes and styles
- The project includes some experimental analysis with various keywords
- Data collection respects Google's rate limiting policies
- Results may vary based on current search trends
- Some visualization warnings are normal and don't affect functionality
Feel free to:
- Add new analysis features
- Improve visualization quality
- Add more keyword categories
- Enhance documentation
- Fix any issues or bugs
This project is for educational and research purposes. Please respect Google's Terms of Service when using the Google Trends API.