| # Stock Price Prediction System | |
| Welcome to the Stock Price Prediction System. This system is designed to predict stock prices using a linear regression model and exposes the model via a Flask API. The guide below will walk you through the steps to set up and deploy the prediction system. | |
| ## Table of Contents | |
| 1. [Data Collection](#data-collection) | |
| 2. [Data Preparation](#data-preparation) | |
| 3. [Model Training](#model-training) | |
| 4. [Flask API Setup](#flask-api-setup) | |
| 5. [Deployment](#deployment) | |
| 6. [Testing](#testing) | |
| 7. [Maintenance](#maintenance) | |
| --- | |
| ## 1. Data Collection <a name="data-collection"></a> | |
| - **Objective**: Collect data for the stock you want to predict. This includes the stock's historical prices and relevant market factors. | |
| - **Tools/Platforms**: Yahoo Finance, Quandl, Alpha Vantage, etc. | |
| - **Steps**: | |
| 1. Choose a reliable data source. | |
| 2. Gather historical stock prices. | |
| 3. Collect relevant market factors (e.g., trading volume, market indices). | |
| --- | |
| ## 2. Data Preparation <a name="data-preparation"></a> | |
| - **Objective**: Ensure that the data is clean, free of anomalies, and prepared for modeling. | |
| - **Tools**: Pandas, NumPy | |
| - **Steps**: | |
| 1. Remove any missing or erroneous data points. | |
| 2. Normalize or scale data if necessary. | |
| 3. Split data into training and test sets. | |
| --- | |
| ## 3. Model Training <a name="model-training"></a> | |
| - **Objective**: Train a linear regression model using the prepared data. | |
| - **Tools**: scikit-learn | |
| - **Steps**: | |
| 1. Initialize a linear regression model. | |
| 2. Train the model using the training dataset. | |
| 3. Evaluate model performance using metrics like mean squared error or R-squared. | |
| --- | |
| ## 4. Flask API Setup <a name="flask-api-setup"></a> | |
| - **Objective**: Set up a Flask API that will expose the trained model for prediction requests. | |
| - **Tools**: Flask | |
| - **Steps**: | |
| 1. Initialize a Flask app. | |
| 2. Create API endpoints to receive user inputs (stock symbol, date range) and return predictions. | |
| 3. Integrate the trained model into the Flask app. | |
| --- | |
| ## 5. Deployment <a name="deployment"></a> | |
| - **Objective**: Make the Flask API available for users by deploying it. | |
| - **Tools**: PythonAnywhere | |
| - **Steps**: | |
| 1. Register on PythonAnywhere. | |
| 2. Create a new web app. | |
| 3. Upload all necessary code and dependencies. | |
| 4. Configure the web app to launch the Flask API. | |
| --- | |
| ## 6. Testing <a name="testing"></a> | |
| - **Objective**: Ensure that the deployed Flask API is functioning correctly. | |
| - **Tools**: Postman, cURL | |
| - **Steps**: | |
| 1. Send prediction requests to the Flask API endpoints. | |
| 2. Verify the responses against expected outcomes. | |
| --- | |
| ## 7. Maintenance <a name="maintenance"></a> | |
| - **Objective**: Ensure the prediction model remains accurate over time. | |
| - **Steps**: | |
| 1. Monitor model performance metrics regularly. | |
| 2. Retrain the model with fresh data if performance drops. | |
| 3. Update the model or features if necessary. | |
| --- | |
| ## Feedback & Contribution | |
| We welcome feedback and contributions to improve this system. Please raise an issue or submit a pull request if you have suggestions or improvements. | |
| --- | |
| ## Streamlit Web App <a name="streamlit-web-app"></a> | |
| We have successfully developed a web app using Streamlit that provides a user-friendly interface for our Stock Price Prediction System. The web app allows users to easily input their stock data and get predictions in real-time without any technical know-how. | |
| Furthermore, we've hosted our Streamlit app on Hugging Face, allowing for seamless access and scalable user interactions. You can access the Streamlit app [here](https://huggingface.co/spaces/NEXAS/stock). | |
| --- | |
| **Author**: NARESH KUMAR LAHAJAL | |
| **License**: MIT | |