JPX Tokyo Stock Exchange Prediction AWS Pipeline

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Project Overview

This project aims to build an end-to-end machine learning pipeline to predict stock prices using data from the JPX Tokyo Stock Exchange. The project involves data collection, preprocessing, model training, evaluation, and deployment using AWS services.

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Table of Contents

1. Project Structure
2. Dataset
3. AWS Setup
4. Installation
5. Usage
6. Model Training
7. Evaluation
8. Deployment
9. Contributing
10. License

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Project Structure

jpx-stock-prediction/
├── data/
│   ├── raw/
│   ├── processed/
├── notebooks/
├── src/
│   ├── data_preprocessing.py
│   ├── feature_engineering.py
│   ├── model_training.py
│   ├── evaluation.py
│   ├── deployment.py
├── requirements.txt
├── README.md
└── .gitignore

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Dataset

The dataset used for this project is obtained from the JPX Tokyo Stock Exchange Prediction competition on Kaggle. It contains historical stock prices and other relevant financial data.

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AWS Setup

Services Used

1. S3: For storing raw and processed data, as well as the trained model.
2. EC2: For running data preprocessing and model training scripts.
3. SageMaker: For managing Jupyter notebooks and training models.
4. Lambda: For deploying the model as a serverless function.
5. API Gateway: For creating a RESTful API to interact with the deployed model.

Setup Instructions

1. Create an S3 Bucket:

   • Go to the S3 service in the AWS console.
   • Create a new bucket (e.g., jpx-stock-data).
   • Upload your dataset to this bucket.

2. Launch an EC2 Instance:

   • Choose an appropriate instance type (e.g., t2.medium).
   • Set up security groups to allow SSH access.
   • Install necessary packages and clone the repository.

3. Set Up SageMaker:

   • Create a new notebook instance.
   • Attach the appropriate IAM role with S3 access.
   • Open the notebook and clone the repository.

4. Lambda Function and API Gateway:

   • Write a Lambda function to load the model from S3 and handle inference requests.
   • Create a new API Gateway and integrate it with the Lambda function.

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Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/jpx-stock-prediction.git
   cd jpx-stock-prediction

2. Install the required packages:

   pip install -r requirements.txt

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Usage

1. Data Preprocessing: Run the data preprocessing script to clean and prepare the data:

   python src/data_preprocessing.py

2. Feature Engineering: Generate features for the model:

   python src/feature_engineering.py

3. Model Training: Train the model using the processed data:

   python src/model_training.py

4. Evaluation: Evaluate the trained model:

   python src/evaluation.py

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Model Training

The model training script includes the following steps:

1. Loading the processed data
2. Splitting the data into training and validation sets
3. Training a linear regression model (or any other chosen model)
4. Saving the trained model to S3

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Evaluation

The evaluation script calculates performance metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) to assess the model's accuracy.

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Deployment

To deploy the model using AWS services, follow these steps:

1. Upload the Trained Model to S3:

   • Save the trained model to your local system.
   • Upload the model to your S3 bucket (e.g., s3://jpx-stock-data/model/).

2. Set Up AWS Lambda:

   • Create a new Lambda function.
   • Write the function code to load the model from S3 and handle inference requests.
   • Configure the function's execution role to allow S3 access.

3. Create an API Gateway:

   • Create a new RESTful API.
   • Integrate the API with the Lambda function.
   • Deploy the API to make it accessible.

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Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Commit your changes (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature-branch)
5. Open a Pull Request

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License

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