Flipkart Data Web Scraping & Analysis

🚀 Project Overview

Welcome to our Hackathon Project! This repository showcases our exploration of Flipkart data through web scraping and data analysis. By leveraging the power of Python, we extracted valuable insights from Flipkart's product listings, aiming to uncover trends, patterns, and actionable insights in the e-commerce domain.

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💥 Team Name : Code Commandos

👥 Team Members

• Naisha Raval
• Yashvardhan Jani
• Ayushi Sapariya
• Krishna Pachani
• Prince Valand

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🎯 Objectives

1. Data Extraction: Scrape product details, reviews, and ratings from Flipkart's website.
2. Data Cleaning: Process the raw data to ensure consistency and accuracy.
3. Data Visualization: Present insights through meaningful visualizations.
4. Trend Analysis: Identify patterns in product pricing, ratings, and reviews.

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🛠️ Tech Stack

• Programming Language: Python

• Libraries:

  • BeautifulSoup for web scraping
  • NumPy for numerical operations
  • Pandas for data manipulation
  • Matplotlib and Seaborn for visualization

• Tools:

  • Google Colab Notebook for interactive coding

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📂 Repository Structure

📦 ECommerce-Flipkart-WebScrapping-DataAnalysis
├── README.md                      # Project documentation
├── requirements.txt               # required Libraries
├── ECom_Data-Analysis.ipynb       # Main Jupyter Notebook
├── ECom_Data-Analysis.html        # Main Jupyter Notebook in form of .html
├── Flipkart_WebScraping_Code.py   # Flipkart Web-Scraping code file
└── Flipkart_Mobiles_Data.csv      # Flipkart Mobiles Data-set

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📊 Key Insights

• Pricing Trends: Analysis of price variations across categories.
• Ratings Distribution: Trends in customer ratings and satisfaction.
• Review Sentiment: Sentiment analysis of product reviews.

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📖 How to Use

1. Clone the repository:

   git clone https://github.com/YashvardhanJani/ECommerce-Flipkart-WebScrapping-DataAnalysis.git

2. Navigate to the project directory:

   cd ECommerce-Flipkart-WebScrapping-DataAnalysis

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Jupyter Notebook:

   jupyter notebook ECom_Data-Analysis.ipynb

OR

• 📒Google Colab File

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🏆 Challenges & Learnings

• Challenge: Handling dynamic web content during scraping.
• Solution: Implemented appropriate delay mechanisms and optimized parsing methods.
• Learning: Gained hands-on experience with web scraping techniques and data visualization.

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📩 Contact

Feel free to reach out to us for collaboration or queries:

• Email: [email protected]
• GitHub: YashvardhanJani

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We hope you find this project insightful and inspiring! 😊