Web Scraper for Market Trends

Skills, Tech Stack, and Libraries

1. Skills: Web Scraping, Data Wrangling, Automation, Data Visualization

2. Tech Stack: Python, SQL, MongoDB, Tableau

3. Libraries: BeautifulSoup, Selenium, Pandas, Matplotlib, Seaborn

Description and Approach

Objective:

I built an automated web scraper to collect and analyze market data from e-commerce platforms. The system extracted pricing, product availability, and competitor trends to help businesses make data-driven decisions about pricing strategies and inventory management.

Approach:

1. Data Collection:

   • Used BeautifulSoup for static web scraping and Selenium for handling dynamic web content.

   • Targeted multiple e-commerce platforms, scraping product names, categories, prices, reviews, and availability.

   • Scheduled automated scraping runs using Python scripts to ensure data freshness.

2. Data Cleaning and Structuring:

   • Processed the scraped data using Pandas to remove duplicates, handle missing values, and standardize formats (e.g., price fields).

   • Stored the cleaned data in a MongoDB database for scalability and flexibility in handling semi-structured data.

3. Data Analysis:

   • Performed EDA using Matplotlib and Seaborn to uncover market trends such as:

     • Price fluctuations over time.

     • Popular product categories and high-demand items.

     • Competitor pricing strategies.

Visualization and Reporting:

Designed an interactive Tableau dashboard displaying:

1. Daily and weekly price trends for key products.

2. Competitor price comparisons for similar products.

3. Inventory and availability status for trending items.

Automation:

Automated the scraping, cleaning, and loading process to ensure the dashboard refreshed with the latest market data.

Code Flow:

1. Use Selenium to navigate and scrape dynamic web pages, and BeautifulSoup for static content.

2. Preprocess and clean the data using Pandas for consistency and accuracy.

3. Store structured data in MongoDB for scalable storage and querying.

4. Load data into Tableau to visualize market trends and generate insights.

Results

The web scraping solution provided actionable insights into market trends with the following outcomes:

• Pricing Strategy Optimization: Enabled businesses to adjust prices dynamically based on competitor trends, increasing revenue by 10%.

• Improved Inventory Planning: Identified high-demand products and ensured timely restocking, reducing lost sales due to stockouts by 15%.

• Time Efficiency: Automated scraping saved significant manual effort, allowing for daily data updates without human intervention.

• Competitive Advantage: Delivered real-time insights into competitor strategies, helping businesses stay ahead in the market.

This project highlighted the power of automated web scraping and analytics in driving competitive business strategies.

Git Link

For more information and code, visit the Git link.