Purchase-Data-Analysis-and-Web-Scraping-Project

Overview

This project performs data analysis on a purchase dataset, scrapes product details from Walmart, and integrates the scraped data with existing datasets. Additionally, it offers interactive features for filtering, searching, and summarizing data using a command-line interface.

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Features

📊 Purchase Data Analysis:

• Detects missing values and ensures data consistency.
• Analyzes trends, popular product categories, and average customer spending.
• Visualizes data using line charts, bar charts, and pie charts.

🌐 Web Scraping:

• Scrapes product data (title, price, rating) from Walmart for multiple categories.
• Combines scraped data with existing datasets for enriched analysis.

🔍 Interactive Analysis Tools:

• Searches customer purchase history.
• Filters purchases based on date range and amount.
• Calculates total revenue by product category.
• Generates and exports a summary report highlighting top-performing products.

📁 Exporting Results:

• Saves processed datasets and summary reports as CSV files.

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🛠 Installation

1. Clone the repository or download the project files.
2. Install dependencies:

   pip install pandas matplotlib beautifulsoup4 requests

3. If using Google Colab, upload the .py file and mount Google Drive for file operations.

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🚀 Usage

Prepare Datasets:

• Ensure the purchase data file (purchase_data.csv) is available.
• Place it in the specified directory or update the script’s file path.

Run the Script:

• Execute the script in Python:

  python project.py

• Follow the menu prompts for interactive analysis.

Web Scraping:

• The script scrapes product data for categories such as electronics, toys, books, and more.
• Ensure the target URLs are accessible and update HEADERS with a valid User-Agent.

Outputs:

• Processed and matched datasets are saved as CSV files in the specified location.
• Summary reports are also exported as CSV files for further analysis.

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📂 Files

• project.py: Main script containing all functionality.
• purchase_data.csv: Input purchase dataset (ensure this file exists in your drive).
• Output Files:
  • combined_walmart_data.csv: Combined dataset of scraped and existing data.
  • matched_dataset_combined.csv: Dataset with matched purchase and scraped data.
  • summaryreport.csv: Generated summary report highlighting top-performing products.

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