📊 Cryptocurrency Market Analysis using CoinGecko API

📌 Project Overview

This project performs an end-to-end analysis of the top 100 cryptocurrencies using live market data from the CoinGecko REST API.

The project demonstrates a complete data analysis workflow, including API data extraction, data cleaning, feature engineering, exploratory data analysis (EDA), and data visualization using Python.

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🎯 Project Objectives

• Extract live cryptocurrency market data using the CoinGecko API.
• Clean and preprocess the dataset for analysis.
• Engineer new business-focused features.
• Perform exploratory data analysis to identify market trends.
• Build visualizations to communicate insights effectively.
• Generate meaningful business insights from the cryptocurrency market.

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🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Requests
• CoinGecko REST API

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📂 Project Structure

Crypto-Market-Analysis/
│
├── data/
│   ├── crypto_raw.csv
│   └── crypto_cleaned.csv
│
├── notebook/
│   └── Cryptocurrency_Market_Analysis.ipynb
│
├── images/
│   ├── dashboard.png
│   ├── top_market_cap.png
│   ├── top_10_trading_volume.png
│   ├── top_10_gainers.png
│   ├── top_10_losers.png
│   ├── market_cap_distribution.png
│   ├── supply_distribution.png
│   ├── price_distribution.png
│   ├── correlation_heatmap.png
│   ├── market_cap_vs_trading_volume.png
│   └── days_since_ath_distribution.png
│
├── README.md
├── requirements.txt
└── LICENSE

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🔄 Project Workflow

CoinGecko API
        │
        ▼
Data Extraction
        │
        ▼
Data Understanding
        │
        ▼
Data Cleaning
        │
        ▼
Feature Engineering
        │
        ▼
Exploratory Data Analysis
        │
        ▼
Data Visualization
        │
        ▼
Business Insights

📊 Dashboard Preview

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🔗 Interactive Dashboard

View the live Google Looker Studio Dashboard: https://datastudio.google.com/reporting/0c979b24-54cb-4a63-9394-5fa436a35f1a

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🧹 Data Cleaning

• Removed unnecessary columns.
• Investigated missing values.
• Removed rows with critical missing values.
• Converted date columns into datetime format.
• Saved a cleaned dataset for analysis.

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⚙️ Feature Engineering

The following business features were created:

• Supply Status

  • Fixed Supply
  • Unlimited Supply

• Price Movement

  • Gainer
  • Loser

• Market Cap Category

  • Large Cap
  • Mid Cap
  • Small Cap

• Days Since ATH

  • Number of days since each cryptocurrency reached its All-Time High.

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📊 Exploratory Data Analysis (EDA)

The analysis answers several business questions:

• Which cryptocurrencies dominate the market by capitalization?
• Which cryptocurrencies have the highest trading volume?
• Which cryptocurrencies experienced the highest gains and losses?
• How are cryptocurrencies distributed across market-cap categories?
• How many cryptocurrencies have fixed vs unlimited supply?
• How long has it been since each cryptocurrency reached its All-Time High?
• What relationships exist among market cap, trading volume, and price?

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📈 Visualizations

The project includes the following visualizations:

• Top 10 Cryptocurrencies by Market Cap
• Top 10 Cryptocurrencies by Trading Volume
• Top 10 Gainers
• Top 10 Losers
• Market Cap Distribution
• Supply Status Distribution
• Current Price Distribution
• Correlation Heatmap
• Market Cap vs Trading Volume
• Days Since ATH Distribution

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💡 Business Insights

• Bitcoin holds the largest market capitalization among the top cryptocurrencies.
• Large-cap cryptocurrencies contribute the majority of the overall market value.
• Trading volume generally increases with market capitalization.
• Most cryptocurrencies are trading below their historical All-Time High (ATH).
• Unlimited-supply cryptocurrencies represent a significant portion of the dataset.
• Market capitalization is highly concentrated among a small number of leading cryptocurrencies.
• Mid-cap cryptocurrencies form the largest category within the analyzed dataset.
• Daily price movements vary significantly across cryptocurrencies.

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🚀 How to Run

1. Clone the repository

git clone https://github.com/arvindd333/Crypto-Market-Analysis.git

2. Install the required libraries

pip install -r requirements.txt

3. Open the notebook

notebook/Cryptocurrency_Market_Analysis.ipynb

4. Run all cells.

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🔮 Future Improvements

• Automate daily data collection using scheduled API requests.
• Build an interactive dashboard using Power BI or Tableau.
• Store historical cryptocurrency data in SQL.
• Perform time-series forecasting on cryptocurrency prices.
• Develop machine learning models for price prediction.

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👨‍💻 Author

Arvind Anand Dyavanapelli

• GitHub: https://github.com/arvindd333
• LinkedIn: https://www.linkedin.com/in/arvind-dyavanapelli-8159b7194

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