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# Stock Price Prediction System
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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.
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## Table of Contents
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1. [Data Collection](#data-collection)
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2. [Data Preparation](#data-preparation)
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3. [Model Training](#model-training)
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4. [Flask API Setup](#flask-api-setup)
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5. [Deployment](#deployment)
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6. [Testing](#testing)
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7. [Maintenance](#maintenance)
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---
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## 1. Data Collection <a name="data-collection"></a>
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- **Objective**: Collect data for the stock you want to predict. This includes the stock's historical prices and relevant market factors.
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- **Tools/Platforms**: Yahoo Finance, Quandl, Alpha Vantage, etc.
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- **Steps**:
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1. Choose a reliable data source.
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2. Gather historical stock prices.
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3. Collect relevant market factors (e.g., trading volume, market indices).
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## 2. Data Preparation <a name="data-preparation"></a>
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- **Objective**: Ensure that the data is clean, free of anomalies, and prepared for modeling.
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- **Tools**: Pandas, NumPy
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- **Steps**:
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1. Remove any missing or erroneous data points.
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2. Normalize or scale data if necessary.
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3. Split data into training and test sets.
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## 4. Flask API Setup <a name="flask-api-setup"></a>
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- **Objective**: Set up a Flask API that will expose the trained model for prediction requests.
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- **Tools**: Flask
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- **Steps**:
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1. Initialize a Flask app.
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2. Create API endpoints to receive user inputs (stock symbol, date range) and return predictions.
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3. Integrate the trained model into the Flask app.
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## 5. Deployment <a name="deployment"></a>
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- **Objective**: Make the Flask API available for users by deploying it.
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- **Tools**: PythonAnywhere
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- **Steps**:
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1. Register on PythonAnywhere.
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2. Create a new web app.
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3. Upload all necessary code and dependencies.
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4. Configure the web app to launch the Flask API.
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## 6. Testing <a name="testing"></a>
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- **Objective**: Ensure that the deployed Flask API is functioning correctly.
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- **Tools**: Postman, cURL
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- **Steps**:
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1. Send prediction requests to the Flask API endpoints.
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2. Verify the responses against expected outcomes.
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## 7. Maintenance <a name="maintenance"></a>
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- **Objective**: Ensure the prediction model remains accurate over time.
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- **Steps**:
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1. Monitor model performance metrics regularly.
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2. Retrain the model with fresh data if performance drops.
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3. Update the model or features if necessary.
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## Feedback & Contribution
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We welcome feedback and contributions to improve this system. Please raise an issue or submit a pull request if you have suggestions or improvements.
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---
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**License**: MIT
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---
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title: Stock
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emoji: 🏢
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colorFrom: gray
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colorTo: indigo
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sdk: streamlit
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sdk_version: 1.26.0
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app_file: app.py
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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