Bitcoin Stock Price Prediction using LSTM

Project Overview

This project is a course assignment for the Data Mining course (COMP7103C) at HKU. The goal is to predict Bitcoin stock prices using Long Short-Term Memory (LSTM) neural networks. The project involves data collection, cleaning, exploratory data analysis, feature engineering, and model building.

Team: Yuxi CHEN (Dylan)

Source Code: bitcoin_price_prediction-lstm.ipynb

Dataset: BTC-USD.csv

Dataset Description

The Bitcoin price dataset includes historical data points representing the price of Bitcoin. The dataset features include:

• Date: The date of the recorded Bitcoin price.
• Open: The price at the beginning of the trading day.
• Close: The price at the end of the trading day.
• Adj. Close: The adjusted close price accounting for corporate actions.
• High: The highest price during the trading day.
• Low: The lowest price during the trading day.

These features are used for financial analysis, prediction, and decision-making. By analyzing historical data, patterns and trends can be identified to predict future prices.

Instructions to Run the Code

If you encounter any issues, you can view the results in bitcoin_price_prediction-lstm.html or contact us

1. Clone the Project

   git clone https://github.com/Dylan-CS/COMP7103_Bitcoin_Price_Prediction_LSTM.git

2. Open the Jupyter Notebook

   Open bitcoin_price_prediction-lstm.ipynb in JupyterLab.

3. Install Required Libraries

   Ensure you have the necessary libraries installed. Run the following commands in your command line:

   pip install pandas numpy matplotlib seaborn plotly scikit-learn tensorflow

4. Run the Jupyter Notebook

   Execute the notebook to see the entire process:

   1. Import Libraries Needed for the data mining project
   2. Data Collection, Cleaning, and Preparation
   3. Exploratory Data Analysis & Feature Engineering
   4. Splitting the Time-series Data
   5. Scaling Data using Min-Max scaler
   6. Model Building
   7. Prediction & Analysis

Additional resource

jupyter nbconvert --to script bitcoin_price_prediction-lstm.ipynb; pipreqs .