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README.md

@@ -1,3 +1,113 @@
+# 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.
+
 ---
-license: mit
+
+## 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/)

lstm_model.pth

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+oid sha256:1fd1167249182f33bc6c092b3bc5dae23b392775e21068d44e0e4443d407acee
+size 206069

requirements.txt

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+pandas==1.5.3
+numpy==1.24.3
+matplotlib==3.7.1
+seaborn==0.12.2
+torch==2.0.1
+scikit-learn==1.2.2
+pycoingecko==3.1.0
+jupyter==1.0.0