Update README.md

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)
-
----
-
-## 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.
-
+title: Stock
+emoji: 🏢
+colorFrom: gray
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.26.0
+app_file: app.py
+pinned: false
+license: mit
 ---
 
-**Author**: NARESH KUMAR LAHAJAL  
-**License**: MIT 
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference