🚀 Crypto Price Predictor 📈

Welcome to the Crypto Price Predictor repository! This project aims to forecast cryptocurrency prices using machine learning models. 🌟

📂 Project Structure

• Dataset/ 📊: Contains the historical cryptocurrency data in CSV format.
• Models/ 🧠: Stores the trained machine learning models.
• Crypto_Price_Model.py 🤖: Script to train the models.
• Crypto_Price_Predictor.py 🔮: Script to predict future prices using the trained models.

🛠️ How to Use

1. Train the Model

First, run Crypto_Price_Model.py to train the models for all the cryptocurrencies. The trained models will be saved in the Models directory.

python Crypto_Price_Model.py

2. Predict Future Prices

Next, use Crypto_Price_Predictor.py to load a trained model and predict future prices for a specified cryptocurrency.

python Crypto_Price_Predictor.py

Enter the name of the cryptocurrency (e.g., Bitcoin, Ethereum) when prompted.

🏆 Model Performance

Here are the Mean Squared Error (MSE) values for our models:

• Aave: 2053.1783
• BinanceCoin: 64902.0719
• Bitcoin: 298114964.3461
• Cardano: 0.1006
• ChainLink: 121.0245
• Cosmos: 110.5456
• CryptocomCoin: 0.0006
• Dogecoin: 0.0188
• EOS: 0.3773
• Ethereum: 487633.3648
• Iota: 0.0150
• Litecoin: 189.7667
• Monero: 237.8501
• NEM: 0.0008
• Polkadot: 13.2997
• Solana: 152.0202
• Stellar: 0.0020
• Tether: 7.7837e-06
• Tron: 7.6098e-05
• Uniswap: 18.8259
• USDCoin: 8.8966e-06
• WrappedBitcoin: 40597001.4039
• XRP: 0.0072

🌟 Features

• Predicts future prices of various cryptocurrencies.
• Utilizes Random Forest Regressor for accurate predictions.
• Handles missing data and performs necessary preprocessing.

📦 Dependencies

Ensure you have the required libraries installed. You can install them using pip:

pip install pandas numpy scikit-learn joblib matplotlib

📫 Connect with Me

For any queries or discussions, feel free to reach out via my GitHub profile.

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Happy predicting! 🚀📈

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### Instructions for Running the Scripts

1. **Train the Model:**
   - Run `Crypto_Price_Model.py` to train the models for all the cryptocurrencies and save them to the "Models" directory.

   ```bash
   python Crypto_Price_Model.py

2. Predict Future Prices:

   • Run Crypto_Price_Predictor.py to load a trained model from the "Models" directory and predict future prices for a specified cryptocurrency.

   python Crypto_Price_Predictor.py

   • Enter the name of the cryptocurrency (e.g., Bitcoin, Ethereum) when prompted.

Dependencies

Make sure you have the required libraries installed. You can install them using pip:

pip install pandas numpy scikit-learn joblib matplotlib

These updated scripts will now save the trained models in the "Models" directory and load them from there for prediction.