Python Automation Portfolio

Showcasing my freelancing and automation projects

Projects Gallery

Browse through my projects to see how I've solved various automation challenges using Python.

Automated Data Migration Tool

Completed
Python Pandas SQLAlchemy Scrapy

The completed tool reduced manual migration time from estimated 4 weeks to 6 hours runtime. It included comprehensive logging and generated a detailed quality report highlighting data issues that required manual review. The client was able to onboard new data sources easily by extending the mapping configuration

Client needed to migrate 50+ Excel files containing customer data from different legacy systems into a unified MySQL database with consistent formatting and validation

  1. Analyzed source file structures and identified common patterns
  2. Created a mapping schema for data transformation
  3. Built a CLI tool with progress tracking and error reporting
  4. Implemented batch processing to handle memory constraints
  5. Added data validation with automatic correction where possible
  • Inconsistent date formats across different source files
  • Missing or duplicate records in some files
  • Some files contained 100K+ records causing memory issues
  • Client requested runtime optimization midway through project
  • Implemented a date parser that could detect and normalize different formats
  • Created deduplication logic with customizable matching criteria
  • Used pandas chunksize parameter for memory-efficient processing
  • Refactored code to use multiprocessing where file operations were independent

E-commerce Inventory Scraper

Ongoing
Python Scrapy Selenium PostgreSQL

The system currently monitors 5 platforms with 100% uptime. The client used the data to adjust pricing strategies, resulting in 15% increased conversions. The modular design allowed easy addition of 3 more platforms during development with minimal code changes required.

Client required a system to regularly monitor competitor product listings across 5 major e-commerce platforms, tracking price changes, stock availability, and promotional campaigns.

  1. Analyzed target sites to identify scraping approaches
  2. Built modular spiders for each platform with common interface
  3. Implemented proxy rotation and request throttling
  4. Created a scheduler system to run scrapes daily
  5. Designed a database schema for historical tracking
  6. Set up email alerts for significant price drops
  • Two platforms used heavy JavaScript rendering
  • Frequent anti-bot countermeasures by target sites
  • Data format inconsistencies between similar product listings
  • Client needed to add 3 more platforms during development
  • Integrated Selenium WebDriver for JS-heavy sites
  • Developed adaptive delays to mimic human behavior
  • Created a data normalization layer to handle different formats
  • Implemented a plugin architecture for easy platform integration
  • Set up comprehensive monitoring for scraper health checks

Automated Report Generator

Completed
Python Jinja2 Matplotlib Tableau

The automated system reduced manual report generation time from 8 hours weekly to fully automated. It now supports 3 different report variants tailored to executives, marketing team, and sales staff. The solution has been extended to provide additional analytics beyond the original requirements.

Marketing team needed weekly performance reports generated automatically from Google Analytics, social media APIs, and CRM data, with customized sections for different stakeholders.

  1. Connected to all data sources via their APIs
  2. Created data transformation pipelines to clean and structure the data
  3. Developed template system for different report variants
  4. Built visualization generation with Matplotlib and Tableau
  5. Automated PDF generation and email distribution
  6. Added Slack notifications for report completion
  • Data schema changes from some API providers
  • Complex conditional formatting requirements
  • Need to support multiple output formats (PDF
  • Excel
  • Dashboard)
  • Last-minute requests for additional metrics
  • Implemented schema adaptation layer to handle API changes
  • Created a YAML-based configuration system for formatting rules
  • Built modular output generators sharing common data processing
  • Developed a quick-add system for new metrics with auto-validation
  • Added automated testing for data quality checks
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