This project predicts the liquidity ratio of cryptocurrencies using machine learning techniques.
CryptoLiquidityPrediction/
├── data/ # Contains raw/cleaned cryptocurrency CSV data
├── docs/ # Design documents: HLD, LLD, Architecture, Final Report
├── models/ # Trained machine learning model (best\_model.joblib)
├── notebooks/ # Jupyter Notebook with EDA, preprocessing, model training
├── src/ # Flask application (app.py for API deployment)
This project aims to forecast the liquidity ratio of cryptocurrencies using supervised machine learning. It includes:
- Data preprocessing
- Model selection using
GridSearchCV - Performance evaluation
- Deployment of a prediction API using Flask
- Python 3
- Pandas, NumPy
- Scikit-learn
- Matplotlib, Seaborn
- Joblib
- Flask
- Jupyter Notebook
- Model Used: Random Forest Regressor with GridSearchCV
- Target Variable: Liquidity Ratio
- Evaluation Metrics: R² Score, RMSE, MAE
The trained model is saved here: models/best_model.joblib
cd notebooks
jupyter notebook Cryptocurrency_ML_Project.ipynbcd src
python app.pyThen open your browser and go to: 👉 http://127.0.0.1:5000
You can find detailed documentation inside the docs/ folder:
HLD.md– High-Level DesignLLD.md– Low-Level DesignPipeline_Architecture.md– Pipeline & FlowFinal_Report.md– Final summary & analysis
An interactive Power BI dashboard was created to visualize cryptocurrency trends, including price fluctuations, liquidity predictions, and trading volume insights.
📁 Location: dashboards/Cryptocurrency_Project.pbix
📄 Dashboard PDF Preview: https://github.com/Progati00/CryptoLiquidityPrediction/blob/main/dashboards/Cryptocurrency_Project.pdf Cryptocurrency Project Dashboard (PDF)
This PDF provides a static view of the Power BI dashboard for easier access and preview, especially for users who do not have Power BI installed.
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