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README.md

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+# 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)
+
 ---
-license: mit
+
+## 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