📈 Bitcoin Price Prediction

📖 Project Overview

This project demonstrates a Bitcoin price prediction model using Python and Jupyter Notebook.
The goal was to analyze historical price data and build a model capable of predicting future Bitcoin prices using machine learning techniques.

It combines data preprocessing, feature engineering, and model training into a coherent notebook-based workflow.
Key focus areas include trend analysis, model selection, and prediction accuracy.

📂 Repository Structure

• bitcoin-prediction.ipynb – Main notebook with all code, analysis, and results

⚙️ Technologies Used

• Python 3.x
• Jupyter Notebook
• XGBoost
• Pandas / NumPy – Data handling and preprocessing
• Matplotlib / Seaborn – Visualization
• scikit-learn – Model building and evaluation
• Datetime tools – Time-series alignment and transformation

🧠 Model Strategy

The notebook walks through the following steps:

1. Load and clean historical Bitcoin price data
2. Visualize trends, volatility, and correlations
3. Engineer relevant features (e.g., moving averages, lags)
4. Train a regression model (e.g., Linear Regression, Random Forest, etc.)
5. Evaluate prediction accuracy on test data
6. Plot actual vs. predicted prices

🚀 How to Run

Requirements

Install required Python packages via pip:

pip install pandas numpy matplotlib scikit-learn jupyter

Run the Notebook

1. Launch Jupyter:

   jupyter notebook

2. Open bitcoin-prediction.ipynb

3. Run the notebook cells in order

✅ Project Status

⚠️ Finished, but not functional
The implementation is complete, but the prediction output does not work as intended.
It can be extended with more advanced models (e.g., LSTM, Prophet) or real-time data integration.

📄 License

This project is licensed under the MIT License – see the LICENSE file for details.