Crypto Analytics & Web Scraping Engine

A data-driven project focused on scraping, cleaning, and analyzing historical prices for Bitcoin, Ethereum, and Ripple using Python and Object-Oriented Programming (OOP).

📊 Project Overview

This project automates the extraction of financial data from the web and performs deep statistical analysis to find market trends and correlations between major cryptocurrencies.

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🛠️ Key Features

1. Automated Web Scraping

Utilizes BeautifulSoup and requests to pull historical daily prices for BTC, ETH, and XRP.

2. OOP-Based Architecture (Step 2 & 9)

Developed a robust CryptoData class implementing:

• Encapsulation: Protecting data attributes.
• Polymorphism/Inheritance: For specialized data handling.

3. Data Cleaning & Transformation (Step 3 & 4)

• Handling missing values via mean-filling and interpolation.
• Conversion of raw prices to Logarithmic Returns using NumPy for better statistical analysis.

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📈 Visualizations & Results

Historical Price Trends

Line charts showing the price volatility of Bitcoin, Ethereum, and Ripple over time.

Mean Return Comparison

Bar charts comparing the average daily returns across the three assets.

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🧪 Analysis Highlights

• Statistical Summary: Calculation of Mean, Median, and Standard Deviation.
• Volatility Analysis: Identifying dates with peak returns.
• Correlation Matrix: Understanding how Bitcoin's movement affects Ethereum and Ripple.

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🚀 How to Run

1. Clone the repo: git clone https://github.com/ahmedrana24/Crypto-Analytics-Engine.git
2. Install dependencies: pip install -r requirements.txt
3. Run the analysis: python main.py

👤 Author

Rana Ahmed FAST NUCES, BS Artificial Intelligence