文件列表:

BIT-USD.csv
Code.ipynb
Research_Report.pdf
Result.png

# Crypto-Currency-Analysis-Prediction Description: This project focuses on analyzing Bitcoin, a prominent cryptocurrency, and predicting its price movements using machine learning techniques. By leveraging historical data and various features, the goal is to provide insights into Bitcoin's behavior and potential future trends. Features: Data Collection: Historical Bitcoin price data is collected from reliable sources such as cryptocurrency exchanges or financial APIs. Exploratory Data Analysis (EDA): Initial exploration of the data to understand patterns, trends, and relationships between different variables. Feature Engineering: Creation of additional features derived from the raw data, such as moving averages, technical indicators, and sentiment analysis of news or social media. Machine Learning Models: Utilization of machine learning algorithms such as regression, time series analysis, and deep learning to predict Bitcoin prices. Model Evaluation: Assessment of model performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and accuracy of direction prediction. Prediction: Use trained models to make predictions on future Bitcoin prices. Dependencies: Python 3.x, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras (for machine learning and deep learning), Cryptocurrency exchange API (if collecting live data), Jupyter Notebook (for interactive data analysis)

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