Project Overview

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Cryptocurrency Price Prediction Using Deep Learning

This project implements Long Short-Term Memory (LSTM) neural networks to predict the closing prices of three cryptocurrencies: Bitcoin (BTC), Solana (SOL), and XRP. The models are built using Python and the TensorFlow/Keras library, leveraging historical data to forecast future prices.

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Files in the Repository:

1. btc_codes.ipynb:

   • Contains the LSTM model for predicting the closing price of Bitcoin (BTC).
   • Includes data preprocessing, model training, and evaluation steps.

2. sol_codes.ipynb:

   • Implements an LSTM model for forecasting the closing price of Solana (SOL).
   • Follows a similar approach to the Bitcoin model.

3. xrp_codes.ipynb:

   • Develops an LSTM model to predict XRP closing price.
   • The process involves data cleaning, feature scaling, and model evaluation.

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Features

• Data Source: Historical OHLC (Open, High, Low, Close) data is fetched from reliable APIs or sources like Yahoo Finance.
• Preprocessing: The notebooks include steps for:
  • Data cleaning and handling missing values.
  • Feature scaling (e.g., MinMaxScaler) for better model performance.
• Model:
  • LSTM neural networks are used for time series forecasting.
  • Optimized hyperparameters such as learning rate, number of neurons, and epochs.
• Visualization: Plots of actual vs. predicted prices are provided to visualize model performance.

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How to Use

1. Clone this repository: bash git clone https://github.com/yourusername/cryptocurrency-price-prediction.git