Bitcoin Market Analysis

## Objective

This project explores Bitcoin market trends, price volatility, and sentiment analysis to build insights and forecasting models.

## Key Features

- Cleaned and merged historical Bitcoin data with sentiment data.

- Performed trend analysis, volatility studies, and correlation checks.

- Built interactive dashboards using Power BI/Tableau.

- Modeled time series forecasts using Prophet and ARIMA.

## Tools

- Python, Pandas, Seaborn, VADER

- Power BI, Tableau

- APIs: CoinGecko, Twitter, Glassnode

## Outcomes

- Identified peak volatility zones and sentiment shifts.

- Built a simple trading signal model based on sentiment and moving averages.

- Suggested portfolio allocation strategy backed by data.

## Folder Guide

See the `/notebooks` and `/dashboard` directories for insights and visualizations.

bitcoin-analysis/

│

├── data/

│   ├── raw/                   # Raw downloaded data (CSV/JSON from APIs)

│   ├── processed/             # Cleaned & transformed data

│

├── notebooks/

│   ├── 01_data_collection.ipynb       # Collect from CoinGecko, Twitter API, etc.

│   ├── 02_data_cleaning.ipynb         # Handling missing values, formatting

│   ├── 03_eda_visuals.ipynb           # Price trends, volume, correlations

│   ├── 04_sentiment_analysis.ipynb    # VADER/TextBlob analysis on tweets or Reddit

│   ├── 05_modeling_forecasting.ipynb  # Prophet/ARIMA or ML model

│

├── dashboard/

│   ├── PowerBI/                # PBIX files for interactive dashboards

│   ├── Tableau/                # TWBX or TDS files

│

├── reports/

│   ├── presentation.pdf        # Slide deck summarizing insights

│   ├── final_report.pdf        # Detailed project report

│

├── scripts/

│   ├── get_data.py             # Python script for scheduled data collection

│   ├── clean_data.py

│

├── requirements.txt            # Python dependencies

├── README.md                   # Project overview

🔍 Project Title: Bitcoin Market Analysis and Trend Forecasting

🎯 Objective:

To analyze Bitcoin price trends, transaction volumes, and market sentiment to identify key patterns and insights that can guide investment strategies or platform development.

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👤 Role: Data Analyst

✅ Responsibilities:

1. Data Collection & Integration

• Collected historical Bitcoin price and transaction data from APIs like:

  • CoinGecko, CoinMarketCap, or Yahoo Finance

  • On-chain metrics from Glassnode or Blockchain.com

• Integrated social sentiment data (Reddit, Twitter) using web scraping or APIs.

2. Data Cleaning & Preparation

• Processed time-series data using Python (pandas, NumPy).

• Handled missing values, duplicates, and anomalies in transaction data.

• Converted timestamps, standardized currencies, and ensured consistency across sources.

3. Exploratory Data Analysis (EDA)

• Analyzed key metrics:

  • Price trends, volatility, volume, market cap

  • Correlation with macro indicators (e.g., inflation, interest rates)

• Visualized trends using Power BI / Tableau / Matplotlib / Seaborn.

  • Time series plots

  • Heatmaps of correlation

  • Candlestick charts

4. Feature Engineering

• Created rolling averages (7-day, 30-day)

• Built volatility indicators (e.g., Bollinger Bands)

• Calculated moving averages and RSI (Relative Strength Index)

5. Sentiment Analysis

• Performed sentiment analysis on social media or Reddit comments using NLP (TextBlob or VADER).

• Merged sentiment scores with price data to test correlation.

6. Predictive Modeling (optional but powerful)

• Built regression or time-series models (ARIMA, Prophet) to forecast price trends.

• Used machine learning models (Random Forest, XGBoost) for price movement classification (up/down).

7. Dashboarding & Reporting

• Built an interactive dashboard in Power BI or Tableau to present:

  • Real-time price and volume changes

  • Sentiment vs. price correlation

  • Predictive signals and historical patterns

• Generated insights and shared actionable reports with stakeholders or team members.

8. Business Impact

• Identified ideal buy/sell signals.

• Helped model future market behavior under various economic conditions.

• Provided data-driven insights for investor awareness or exchange platform optimization.

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🧰 Tools & Technologies Used

Category	Tools/Technologies
Data Collection	Python, APIs, Web Scraping
Data Wrangling	Python (pandas, NumPy)
Visualization	Power BI, Tableau, Matplotlib, Seaborn
Modeling	Scikit-learn, Prophet, ARIMA
Sentiment Analysis	TextBlob, VADER, NLTK
Version Control	Git, GitHub

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📊 Deliverables

• Cleaned and structured dataset

• Insightful EDA visuals

• Time series model forecasts

• Power BI or Tableau dashboard

• Project documentation/report (Jupiter Notebook or PDF)