Bitcoin Price Prediction
Project start: 2025-02-04
Project description
This project focuses on developing a predictive model for Bitcoin price movements using deep learning techniques. The approach integrates comprehensive Exploratory Data Analysis (EDA) with advanced time series forecasting methods, specifically Long Short-Term Memory (LSTM) neural networks. By leveraging historical Bitcoin pricing data (from Kaggle), technical indicators, and market sentiment metrics, the model aims to forecast future price trends with meaningful accuracy. The implementation utilizes TensorFlow and Keras frameworks to build, train, and evaluate the LSTM architecture, which is particularly suited for capturing temporal dependencies in financial time series data. This project demonstrates the application of deep learning to cryptocurrency market analysis and provides insights into the factors influencing Bitcoin price volatility.
Main functionalities
- Comprehensive Exploratory Data Analysis (EDA) of historical Bitcoin price data and related market metrics
- Data preprocessing including normalization techniques to optimize model performance
- Implementation of feature engineering to extract meaningful predictors from raw market data
- Development of LSTM (Long Short-Term Memory) neural network architecture for time series forecasting
- Integration of TensorFlow and Keras frameworks for model building, training, and evaluation
- Hyperparameter tuning to optimize model accuracy and generalization capabilities
- Visualization of predicted vs. actual price movements to assess model performance
- Analysis of prediction errors to identify market conditions affecting forecast accuracy
Skills
- Python
- Pandas
- EDA
- Numpy
- Sklearn
- Tensorflow
- Keras
- LSTM
- Matplotlib
- LaTeX
Project Report
You can download and review the complete project report with detailed methodology and results here: Bitcoin Price Prediction Report
Sample photos
