update readme

README.md

@@ -1,113 +1,125 @@
-# Bitcoin Price Prediction with LSTM
-
-## Project Overview
-This project aims to predict Bitcoin (BTC) prices for the next 60 days using a Long Short-Term Memory (LSTM) neural network. The dataset used contains historical BTC/USD prices from 2014 to early 2024. The project leverages PyTorch for deep learning and includes data preprocessing, feature engineering, and model evaluation.
-
----
-
-## Table of Contents
-1. [Introduction](#introduction)
-2. [Dataset Description](#dataset-description)
-3. [Project Workflow](#project-workflow)
-4. [Model Architecture](#model-architecture)
-5. [Results](#results)
-6. [How to Run](#how-to-run)
-7. [Future Work](#future-work)
-8. [References](#references)
-
----
-
-## Introduction
-Bitcoin is a highly volatile cryptocurrency, making price prediction a challenging task. This project uses sequential data modeling with LSTM to capture patterns in historical BTC prices and provide reliable predictions.
-
----
-
-## Dataset Description
-- **Source**: Kaggle
-- **File**: `Dataset/BTC-USD.csv`
-- **Columns**: `Date`, `Open`, `High`, `Low`, `Close`, `Adj Close`, `Volume`
-- **Timeframe**: 2014 to early 2024
-- **Frequency**: Minute-level data aggregated to daily prices.
-
----
-
-## Project Workflow
-### 1. Data Preparation
-- Import libraries and load the dataset.
-- Perform initial exploration to understand the data structure.
-
-### 2. Data Cleaning
-- Handle missing values and duplicates.
-- Normalize and standardize the data for better model performance.
-
-### 3. Exploratory Data Analysis (EDA)
-- Visualize trends in BTC prices and trading volume.
-- Analyze correlations between features.
-
-### 4. Feature Engineering
-- Create sequences of 30 days as input features.
-- Scale features using `MinMaxScaler`.
-
-### 5. Modeling
-- Build LSTM and GRU models using PyTorch.
-- Train the models with Mean Squared Error (MSE) loss and Adam optimizer.
-
-### 6. Evaluation
-- Evaluate the model using Root Mean Squared Error (RMSE).
-- Visualize predictions against actual prices.
-
-### 7. Prediction
-- Predict BTC prices for the next 60 days.
-- Compare predictions with actual future prices.
-
----
-
-## Model Architecture
-The LSTM model consists of:
-- **Input Layer**: Sequence of 30 days of closing prices.
-- **Hidden Layers**: 2 LSTM layers with 64 hidden units.
-- **Output Layer**: Single neuron for predicting the next day's price.
-
----
-
-## Results
-- **LSTM Test RMSE**: ~1,118 USD
-- **GRU Test RMSE**: ~21,445 USD
-- The LSTM model outperformed the GRU model, demonstrating its ability to capture sequential patterns in BTC prices.
-
-![Bitcoin Price Prediction](output_prediction.png)
-
----
-
-## How to Run
-1. Clone the repository:
-   ```bash
-   git clone <repository-url>
-   cd Bitcoin-Prediction
-   ```
-
-2. Install dependencies:
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the Jupyter Notebook:
-   ```bash
-   jupyter notebook Notebook.ipynb
-   ```
-
-4. Follow the steps in the notebook to train the model and visualize predictions.
-
----
-
-## Future Work
-- Add additional features such as macroeconomic indicators, Moving Average, RSI or sentiment analysis.
-- Perform hyperparameter tuning to further improve model performance.
-- Deploy the model as a web application for real-time predictions.
-
----
-
-## References
-- Kaggle Dataset: [BTC-USD Historical Data](https://www.kaggle.com/)
-- PyTorch Documentation: [https://pytorch.org/](https://pytorch.org/)
+---
+license: mit
+language:
+- en
+metrics:
+- mse
+tags:
+- LSTM
+- PyTorch
+- RNN
+- DeepLearning
+---
+# Bitcoin Price Prediction with LSTM
+
+## Project Overview
+This project aims to predict Bitcoin (BTC) prices for the next 60 days using a Long Short-Term Memory (LSTM) neural network. The dataset used contains historical BTC/USD prices from 2014 to early 2024. The project leverages PyTorch for deep learning and includes data preprocessing, feature engineering, and model evaluation.
+
+---
+
+## Table of Contents
+1. [Introduction](#introduction)
+2. [Dataset Description](#dataset-description)
+3. [Project Workflow](#project-workflow)
+4. [Model Architecture](#model-architecture)
+5. [Results](#results)
+6. [How to Run](#how-to-run)
+7. [Future Work](#future-work)
+8. [References](#references)
+
+---
+
+## Introduction
+Bitcoin is a highly volatile cryptocurrency, making price prediction a challenging task. This project uses sequential data modeling with LSTM to capture patterns in historical BTC prices and provide reliable predictions.
+
+---
+
+## Dataset Description
+- **Source**: Kaggle
+- **File**: `Dataset/BTC-USD.csv`
+- **Columns**: `Date`, `Open`, `High`, `Low`, `Close`, `Adj Close`, `Volume`
+- **Timeframe**: 2014 to early 2024
+- **Frequency**: Minute-level data aggregated to daily prices.
+
+---
+
+## Project Workflow
+### 1. Data Preparation
+- Import libraries and load the dataset.
+- Perform initial exploration to understand the data structure.
+
+### 2. Data Cleaning
+- Handle missing values and duplicates.
+- Normalize and standardize the data for better model performance.
+
+### 3. Exploratory Data Analysis (EDA)
+- Visualize trends in BTC prices and trading volume.
+- Analyze correlations between features.
+
+### 4. Feature Engineering
+- Create sequences of 30 days as input features.
+- Scale features using `MinMaxScaler`.
+
+### 5. Modeling
+- Build LSTM and GRU models using PyTorch.
+- Train the models with Mean Squared Error (MSE) loss and Adam optimizer.
+
+### 6. Evaluation
+- Evaluate the model using Root Mean Squared Error (RMSE).
+- Visualize predictions against actual prices.
+
+### 7. Prediction
+- Predict BTC prices for the next 60 days.
+- Compare predictions with actual future prices.
+
+---
+
+## Model Architecture
+The LSTM model consists of:
+- **Input Layer**: Sequence of 30 days of closing prices.
+- **Hidden Layers**: 2 LSTM layers with 64 hidden units.
+- **Output Layer**: Single neuron for predicting the next day's price.
+
+---
+
+## Results
+- **LSTM Test RMSE**: ~1,118 USD
+- **GRU Test RMSE**: ~21,445 USD
+- The LSTM model outperformed the GRU model, demonstrating its ability to capture sequential patterns in BTC prices.
+
+![Bitcoin Price Prediction](output_prediction.png)
+
+---
+
+## How to Run
+1. Clone the repository:
+   ```bash
+   git clone <repository-url>
+   cd Bitcoin-Prediction
+   ```
+
+2. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Run the Jupyter Notebook:
+   ```bash
+   jupyter notebook Notebook.ipynb
+   ```
+
+4. Follow the steps in the notebook to train the model and visualize predictions.
+
+---
+
+## Future Work
+- Add additional features such as macroeconomic indicators, Moving Average, RSI or sentiment analysis.
+- Perform hyperparameter tuning to further improve model performance.
+- Deploy the model as a web application for real-time predictions.
+
+---
+
+## References
+- Kaggle Dataset: [BTC-USD Historical Data](https://www.kaggle.com/)
+- PyTorch Documentation: [https://pytorch.org/](https://pytorch.org/)
 - CoinGecko API: [https://www.coingecko.com/](https://www.coingecko.com/)