Update README.md

README.md

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 ---
 license: mit
 language:
-- en
+  - en
+tags:
+  - bitcoin
+  - lstm
+  - time-series
+  - price-prediction
+  - tensorflow
+  - keras
+  - finance
 ---
 
-🔍 Model Overview
-Feature	Description
-📦 Model Type	LSTM (Long Short-Term Memory), a type of Recurrent Neural Network (RNN)
-🧠 Frameworks Used	TensorFlow (Keras API), Scikit-learn, yfinance, NumPy, Pandas
-📈 Input	Past 60 days of Bitcoin closing prices
-🎯 Output	The predicted closing price for the next day
-🧪 Goal	Short-term (1-day ahead) price prediction
-📊 Evaluation Metric	RMSE (Root Mean Squared Error)
+# 🧠 Bitcoin Price Forecasting using LSTM Neural Network
 
-🔧 What It Does
-Downloads historical BTC-USD data from Yahoo Finance
+A deep learning model based on Long Short-Term Memory (LSTM) networks to predict the next-day closing price of Bitcoin (BTC-USD) using historical data from Yahoo Finance.
 
-Normalizes the closing price data between 0 and 1 using MinMaxScaler
+---
 
-Splits the data into 80% training and 20% testing
+## 🔍 Model Overview
 
-Uses a sliding window of 60 days to create time-sequenced input for LSTM
+| Feature            | Description                                                                 |
+|--------------------|-----------------------------------------------------------------------------|
+| 📦 Model Type      | LSTM (Long Short-Term Memory), a variant of Recurrent Neural Networks (RNN) |
+| 🧠 Frameworks Used | TensorFlow (Keras API), Scikit-learn, NumPy, Pandas, yfinance                |
+| 📈 Input           | Past 60 days of Bitcoin closing prices                                       |
+| 🎯 Output          | Predicted closing price for the next day                                     |
+| 📊 Evaluation      | Root Mean Squared Error (RMSE)                                               |
+| 🧪 Goal            | Short-term (1-day ahead) BTC price forecasting                               |
 
-Builds a 2-layer LSTM network with dropout to avoid overfitting
+---
 
-Trains the model to minimize mean squared error
+## 🔧 What the Model Does
 
-Evaluates the model on test data
+- Downloads historical BTC-USD data from Yahoo Finance
+- Normalizes the data between 0 and 1 using MinMaxScaler
+- Splits into 80% training and 20% test sets
+- Creates time-sequenced inputs with a 60-day sliding window
+- Trains a 2-layer LSTM model with dropout to prevent overfitting
+- Evaluates the model using RMSE
+- Plots predicted vs actual prices
+- Makes a next-day prediction using the last 60 days of data
 
-Plots actual vs predicted prices
+---
 
-Makes a future prediction for the next day based on the last 60 days
+## 💡 Use Cases
 
-💡 What This Model Is Useful For
-Educational purposes (learning LSTM, time series preprocessing)
+- Educational: Learning time series forecasting and LSTM models
+- Research: Benchmarking for financial forecasting models
+- Visualization: Analyze model performance on real BTC data
+- Academic Support: Useful for papers or prototypes on AI-based financial systems
 
-Research and experimentation with financial prediction models
+---
 
-Visualization and evaluation of model performance
+## ⚠️ Limitations
 
-Academic paper support (as you’re already writing)
+- Uses only the closing price (no volume, indicators, or sentiment data)
+- Performs only single-step (1-day ahead) forecasting
+- Does not account for sudden market news or shocks
+- Not designed for high-frequency or live trading systems
 
-⚠️ Limitations
-It uses only closing price – no volume, sentiment, or indicators like RSI/MACD
+---
 
-It performs single-step prediction (only the next day)
+## 🚀 Potential Improvements
 
-May not adapt well to sudden market shocks
+- Include additional features: volume, RSI, MACD, etc.
+- Integrate external signals: news, social media sentiment, macro data
+- Add attention or transformer-based layers
+- Extend to multi-step forecasting (3-day, 5-day, etc.)
+- Deploy as REST API or interactive dashboard
+- Connect to Binance or other exchanges for live predictions
 
-Not suitable as-is for high-frequency trading or live trading bots
+---
 
-🚀 How to Improve It
-Include more features: trading volume, high/low prices, technical indicators
+## 📁 Files
 
-Add external data: news sentiment, macroeconomic indicators
+- `lstm_bitcoin_predictor.py`: Full code to train, evaluate, and predict using LSTM
+- `data.csv`: (optional) Cached historical BTC-USD data
+- `model.h5`: Saved trained model
 
-Use attention mechanisms or transformer architectures
+---
 
-Convert it to multi-step forecasting (predict next 3, 5, or 7 days)
+## 📜 License
 
-Wrap it in a REST API or real-time dashboard
+This project is licensed under the MIT License.
 
-Let me know if you want to:
+---
 
-Package this into a web app
+## ⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️ Disclaimer⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️
+
+> **This model is intended for educational and research purposes only.**
+>
+> It is **not** designed for financial or investment decision-making.  
+> No guarantees are made about the accuracy of the forecasts.  
+> The authors accept no responsibility for any financial losses incurred from the use of this model.  
+> **Use at your own risk.**
 
-Prepare a GitHub README for the code
 
-Extend the model for live trading (e.g., using Binance API)
 
-Or build a production-ready pipeline
----
-⚠️ ⚠️ ⚠️ ⚠️ ⚠️ ⚠️ ⚠️ ⚠️ ⚠️ ⚠️ 
-Disclaimer:
-This model is developed strictly for experimental and educational purposes. It is not intended for real-world financial forecasting or investment decisions. Users are strongly advised not to rely on the outputs of this model for any form of trading or financial transactions. No guarantees are made regarding the accuracy or reliability of the predictions, and no liability or responsibility is accepted for any losses or damages resulting from the use of this system. By using this model, you acknowledge and agree that no claims or disputes will be accepted under any circumstances.
----