StockVision

📌 Overview

The Stock Market Prediction App is a data-driven project that leverages machine learning and real-time financial data to predict stock prices and trends. It integrates APIs for fetching live stock market data and utilizes visualization tools to help users analyze stock performance.

🔍 Features

• Live Stock Data Fetching: Integrated API to retrieve real-time stock market data.
• Historical Data Analysis: Used past stock trends to derive insights.
• Machine Learning Predictions: Applied ML models to predict future stock prices.
• Data Visualization: Interactive charts for stock trend analysis.
• Technical Indicators: Implemented key indicators like Moving Averages, RSI, MACD, etc.
• User Input Support: Allows users to enter stock symbols and get predictions.

📊 Technologies Used

• Python (for data analysis and ML modeling)
• Pandas, NumPy (for data preprocessing)
• Scikit-Learn (for machine learning models)
• Matplotlib, Seaborn, Plotly (for visualizations)
• Streamlit (for an interactive web app)
• Yahoo Finance API / Alpha Vantage API (for fetching real-time stock data)
• Flask / FastAPI (optional backend for API handling)

📂 Dataset

The app retrieves historical stock data using APIs.

Preprocessed data includes date, open price, close price, high, low, volume, and technical indicators.

🚀 How to Run the Project

Clone the repository:

git clone https://github.com/your-username/stock-prediction-app.git

Navigate to the project directory:

cd stock-prediction-app

Install dependencies:

pip install -r requirements.txt

Run the app:

streamlit run app.py

OR, if using Flask:

python app.py

📈 Insights Gained

• Trend Analysis – Identified historical patterns that influence stock prices
• Prediction Accuracy – Evaluated different ML models like Linear Regression, LSTM, and ARIMA
• Impact of Market Events – Studied how news and financial events affect stock movements
• Volatility Measurement – Used historical data to assess market risks

📜 Future Improvements

• Implement deep learning models (LSTM, RNNs) for better time-series forecasting
• Add sentiment analysis based on financial news headlines
• Enhance the UI with real-time interactive dashboards
• Provide stock recommendations based on risk assessment and trends.

🏆 Contributions

Feel free to fork this repository and contribute to improve this project! If you have any suggestions, open an issue or create a pull request.

Author

Priya Chanchal :)