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.

Data Collection
Data Preparation
Model Training
Flask API Setup
Deployment
Testing
Maintenance

1. Data Collection

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

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

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

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

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

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

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

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.

Author: NARESH KUMAR LAHAJAL
License: MIT

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