E-Commerce-Price-Tracker-Analytics-Dashboard

An automated system that tracks product prices across e-commerce websites by web-scraping and stores the data into MySQL database and provides real-time analytics through an interactive dashboard by PowerBI.

Step 1: Creation of Project folder

mkdir book_scraper_project cd book_scraper_project python -m venv venv source venv/Scripts/activate
pip install requests beautifulsoup4 pandas

Step 2: Create file scraper.py

Use the file above updated scraper.py

Step 3: Runing of scraping function

python scrape_books.py output:- Scraping page 1: https://books.toscrape.com/catalogue/page-1.html ... ✅ Scraped 200 books successfully!

Step 4: Store Scraped data into MySQL database

Install MySQL

If not installed: Download MySQL Community Edition

During setup, remember:

Root password Port (default 3306)

Install Python connector

pip install mysql-connector-python sqlalchemy pymysql

Create database

CREATE DATABASE book_scraper_db; USE book_scraper_db;

CREATE TABLE books ( id INT AUTO_INCREMENT PRIMARY KEY, title VARCHAR(255), price FLOAT, rating VARCHAR(10), availability VARCHAR(50) );

Run scraper.py

Scraping page 1: https://books.toscrape.com/catalogue/page-1.html ... ✅ Scraped 200 books successfully and saved to MySQL!

Step 5: Analytics Ready: Connect MySQL Data to Power BI

Using PowerBI

Open Power BI Desktop → Home → Get Data → More… Select MySQL database → Connect Enter your database details: Server: localhost (if local) Database: book_scraper_db Username / Password: your MySQL login

Click Connect Select books table → Load

Now you can build visuals: Average price per rating Price distribution histogram Top 10 most expensive books

Using Python for ML

Code snepet is in Analytics.py

ETL Automation: Append New Pages / Data Automatically

ETL = Extract → Transform → Load.

Windows Task Scheduler

Open Task Scheduler → Create Task Name: Book Scraper Daily Trigger: Daily, set time Action: Start a program Program: python.exe Arguments: C:\path\to\scrape_books_mysql.py Save → It will run every day automatically.