Objective:

To build a web-based platform that uses historical stock market data and machine learning models to forecast future stock prices and provide visual insights, helping users make data-driven investment decisions.

Technologies Used:

• Frontend: HTML, CSS, Bootstrap, JavaScript (Chart.js / D3.js for graphs)

• Backend: Node.js / PHP / Java

• Machine Learning: Python (Scikit-learn, LSTM with Keras/TensorFlow)

• Database: MySQL / MongoDB

• APIs/Data: Yahoo Finance API, Alpha Vantage API, or NSE/BSE APIs

• Optional: Flask/Express.js API layer to integrate ML model into backend

Key Features:

1. Stock Data Visualization:

   • Display historical data with interactive graphs.

   • Show Open, High, Low, Close, and Volume over time.

2. ML-Based Price Prediction:

   • Train models (Linear Regression, LSTM, ARIMA) on historical data.

   • Forecast short-term (1–7 days) or long-term trends.

3. Search & Compare Companies:

   • Allow users to search for multiple stocks.

   • Compare performance and predictions side by side.

4. Dashboard for Investors:

   • Personalized view for users with favorite stocks, watchlist, alerts.

   • Show predicted trend graphs with confidence intervals.

5. Technical Indicators:

   • Display MACD, RSI, Moving Averages, and Bollinger Bands.

6. Risk Alert System:

   • Notify users of potential risks based on market volatility or sudden dips.

7. User Authentication (Optional):

   • Secure login, portfolio management, and history tracking.