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https://github.com/armanx200/crypto_price_predictor
🚀 Crypto Price Predictor: Machine learning models to forecast future cryptocurrency prices! 📉💹
https://github.com/armanx200/crypto_price_predictor
arman-kianian bitcoin crypto-analysis cryptocurrency data-science data-visualization deep-learning ethereum finance forecasting machine-learning python trading
Last synced: 13 days ago
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🚀 Crypto Price Predictor: Machine learning models to forecast future cryptocurrency prices! 📉💹
- Host: GitHub
- URL: https://github.com/armanx200/crypto_price_predictor
- Owner: Armanx200
- Created: 2024-06-06T08:38:49.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-06-06T09:59:07.000Z (8 months ago)
- Last Synced: 2024-11-24T09:16:33.275Z (2 months ago)
- Topics: arman-kianian, bitcoin, crypto-analysis, cryptocurrency, data-science, data-visualization, deep-learning, ethereum, finance, forecasting, machine-learning, python, trading
- Language: Python
- Homepage: https://github.com/Armanx200
- Size: 62.6 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚀 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.
```bash
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.
```bash
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:
```bash
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](https://github.com/Armanx200).
---
Happy predicting! 🚀📈
---
```
### 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.
```bash
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:
```bash
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.