🌟 Project Overview

In real-world data science, raw data is messy. It often comes from multiple unstructured sources like websites, APIs, and CSVs. This project demonstrates how to:

1. Scrape data from websites using BeautifulSoup

2. Clean & structure datasets with Pandas

3. Store processed data into an SQL database

4. Prepare clean data for analysis & dashboards

✅ Recruiter Signal: “This candidate can automate data collection, clean datasets, and deliver ready-to-analyze insights.”

🛠️ Tech Stack

• Python – automation & scripting

• BeautifulSoup – scraping web data

• Pandas – data wrangling & cleaning

• SQLite/MySQL – structured database storage

• SQLAlchemy – database connection in Python

💡 Features

• 🌐 Automated web scraping from multiple pages

• 🧹 Data cleaning pipeline: handle missing values, duplicates, and formatting

• 📂 Save structured datasets into SQL for long-term use

• 📊 Dashboard-ready outputs in CSV/Excel/SQL

• ⚡ 80% faster than manual collection

📂 Project Structure

web_scraping_pipeline/
│── app.py                # Main pipeline script
│── scraper.py            # Web scraping functions
│── cleaner.py            # Data cleaning functions
│── database.py           # SQL storage logic
│── requirements.txt      # Dependencies
│── README.md             # Documentation
│── data/                 # Raw + Cleaned data (sample CSVs)

🚀 How to Run Locally

1. Clone the repo:

    

   git clone https://github.com/yourusername/web_scraping_pipeline.git
   cd web_scraping_pipeline
   

2. Install dependencies:

    

   pip install -r requirements.txt 

3. Run the scraper:

    

   python app.py 

📝 Example Workflow

1. Scraping with BeautifulSoup

import requests
from bs4 import BeautifulSoup

url = "https://example.com/products"
html = requests.get(url).text
soup = BeautifulSoup(html, "html.parser")

products = []
for item in soup.find_all("div", class_="product"):
   title = item.find("h2").text
   price = item.find("span", class_="price").text
   products.append({"title": title, "price": price})

2. Data Cleaning with Pandas

import pandas as pd

df = pd.DataFrame(products)

# Clean price column
df["price"] = df["price"].str.replace("$", "").astype(float)

# Drop duplicates
df = df.drop_duplicates()

3. Storing Data in SQL

import sqlite3

conn = sqlite3.connect("scraped_data.db")
df.to_sql("products", conn, if_exists="replace", index=False)

📊 Example Output

📌 Why This Project Matters

• Shows automation skills (scraping + pipelines)

• Highlights data wrangling expertise with Pandas

• Demonstrates SQL knowledge for structured storage

• Fits perfectly into real-world data analyst workflows

🌐 Recruiter-Friendly Signal

This project proves you can collect, clean, and organize messy web data into structured insights, saving time and enabling faster analysis — exactly what recruiters and hiring managers want.

🔗 Links

View Code on GitHub
View Live Demo on Streamlit