Project Overview
A comprehensive data science project analyzing 5,000+ ramen reviews through advanced web scraping, data cleaning, and analytical techniques. Built a robust ETL pipeline with 6 processing stages across 174 web pages.
Data Science Methodology
Environment Setup & Data Exploration
Established a structured development environment with proper project organization, version control, and dependency management. Conducted comprehensive exploratory data analysis to understand data structure and identify quality issues.
- Python virtual environment setup and requirements management
- Git version control implementation for reproducible research
- Statistical profiling and distribution analysis
- Missing value pattern identification and data type validation
Data Cleaning & Standardization
Applied systematic data cleaning procedures including standardization, deduplication, and format normalization while maintaining data integrity throughout the process.
- Column naming standardization using snake_case convention
- Data type conversion and validation procedures
- Duplicate detection and intelligent removal strategies
- Whitespace normalization and encoding issue resolution
Web Scraping Architecture
Designed and implemented ethical web scraping solutions with proper throttling, error handling, and respectful data collection practices to supplement temporal data gaps.
- Rate limiting and throttling mechanisms (1-second delays)
- Robust error handling and retry logic implementation
- Structured data extraction and intelligent parsing
- Automated date parsing and temporal data integration
Data Integration & Documentation
Merged multiple data sources using sophisticated join operations while resolving conflicts and ensuring referential integrity. Created comprehensive documentation for reproducible workflows.
- Multi-source data alignment with intelligent matching algorithms
- Conflict resolution strategies and comprehensive logging
- Data quality validation post-merge procedures
- Reproducible analysis notebooks and deliverable packaging
Technical Stack & Tools
Programming Languages
Data Processing
Web Scraping
Development Tools
Key Challenges Solved
Data Quality Issues
Raw dataset contained inconsistent formatting, missing values, encoding issues, and duplicate entries requiring systematic cleaning procedures.
Solution:
Implemented comprehensive validation pipeline with standardized cleaning procedures and intelligent duplicate detection.
Temporal Data Gap
Primary dataset lacked critical timestamp information needed for temporal analysis, requiring additional data collection from web sources.
Solution:
Developed ethical web scraping solution with rate limiting and automated date parsing to supplement existing data.
Data Integration Complexity
Merging datasets from different sources with potential ID mismatches required sophisticated conflict resolution strategies.
Solution:
Created intelligent matching algorithms with fuzzy logic and comprehensive logging of merge decisions.
Reproducibility Requirements
Ensuring analysis reproducibility across different environments while maintaining clear documentation of all processing steps.
Solution:
Implemented containerized development environment with dependency management and comprehensive logging.
Project Results & Deliverables
Key Outcomes
Complete Data Pipeline
End-to-end workflow from raw sources to analysis-ready datasets
Enhanced Data Quality
Significant improvement in consistency and completeness
Temporal Analysis Capability
Time-series analysis and trend identification across review patterns
Scalable Architecture
Modular, maintainable code structure for future extensions
Deliverables
Interested in exploring the methodology, technical implementation, or discussing data science best practices?