Aftab Ahmed
Python Web Scraping & Automation Expert
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š¤ WEB SCRAPING LEAD GENERATION BOT
A Python automation bot that extracts product data from e-commerce websites and converts raw HTML into structured CSV leads.
āļø HOW IT WORKS (Step-by-Step):
š¹ SCRAPE LAYER:
requests.get() sends HTTP request to target URL
BeautifulSoup parses HTML using CSS selectors
Extracts: Product Name, Price, Availability
Data stored in pandas DataFrame
š¹ CLEAN LAYER:
drop_duplicates() removes duplicate entries
Regex pattern r"[^\d.]" removes currency symbols (Ā£, $)
pd.to (http://pd.to)_numeric() converts price string to float
Handles missing values automatically
š¹ STORAGE LAYER:
raw_leads.csv (original scraped data for audit)
clean_leads.csv (processed, analysis-ready data)
š¹ AUTOMATION LAYER:
schedule.every().day.at (http://day.at)("14:51").do(run_bot)
Continuous while loop with schedule.run (http://schedule.run)_pending()
Bot runs daily without manual intervention
š COMPLETE DATA PIPELINE:
Website ā HTTP Request ā HTML Parse ā Extract ā Clean ā Export CSV ā Schedule Repeat
šÆ REAL-WORLD USE CASE:
Competitor price monitoring
Automated lead generation for sales teams
Market research data collection
š» TECH STACK:
Python, Requests, BeautifulSoup4, Pandas, Schedule
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This project is a Python-based Ecommerce Sales Analysis tool that works automatically:
š„ INPUT: books_data.csv (contains Product, Category, Price, Quantity, Date)
āļø PROCESSING:
Pandas loads and cleans the data
Creates new column 'Total_Sales' = Price Ć Quantity
Groups data by Category for analysis
Calculates statistics: average price, total revenue, top products
š OUTPUT (Auto-generated):
Revenue by category (bar chart)
Order distribution (pie chart)
Price vs quantity (scatter plot)
Daily sales trend (line chart)
CSV file with category analysis
Text report with summary
šÆ RESULT: A complete sales dashboard ready in seconds ā no manual calculation needed.
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Price Tracker Bot running successfully on terminal.
This screenshot shows the automation script fetching live product price, comparing with previous value, and logging everything locally via Python (requests + BeautifulSoup).
The bot is scheduled to run daily and sends an email only when a price drop is detected ā no manual intervention required.
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