📈 Stock Market Analysis & Forecasting App

An interactive web app to visualize and forecast stock market trends using time-series machine learning models. Built with Python, Streamlit, and TensorFlow, this project helps users analyze historical stock prices and predict future movements for smarter investment decisions.

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🚀 Features

• 📊 Visualize stock trends using historical data
• 📉 Forecast future prices using LSTM model
• 🔍 Compare trends of multiple companies
• 🧠 ML-based time-series forecasting (90% accuracy)
• 💡 User-friendly interface with interactive charts

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🛠 Tech Stack

• Frontend: Streamlit
• Backend/Data: Python, Pandas, NumPy, yfinance
• ML Models: TensorFlow (LSTM), Scikit-learn
• Visualization: Matplotlib, Seaborn, Plotly

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📂 Folder Structure

📁 stock-market-analysis/ ├── app.py # Streamlit web app ├── model.py # ML model building and forecasting ├── data_loader.py # Stock data fetching and processing ├── utils.py # Helper functions and visualization ├── requirements.txt # Python dependencies └── README.md # Project overview

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📈 How It Works

1. User Input
   User enters the stock ticker symbol (e.g., AAPL, TSLA, INFY).

2. Data Fetching
   Historical stock data is retrieved using the yfinance API.

3. Visualization
   App plots interactive charts (price, volume, moving averages).

4. Forecasting
   LSTM model predicts future price trends based on past data.

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🔧 Setup Instructions

1. Clone the Repository

git clone https://github.com/your-username/stock-market-analysis.git
cd stock-market-analysis
2. Install Requirements
bash
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pip install -r requirements.txt
3. Run the Streamlit App
bash
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streamlit run app.py
📊 Example Stock Tickers to Try
AAPL (Apple)

TSLA (Tesla)

MSFT (Microsoft)

INFY (Infosys)

GOOGL (Google)

📌 To-Do (Future Improvements)
Add more forecasting models (e.g., ARIMA, Prophet)

Enable user-uploaded datasets

Store forecasts in a database

Add live news sentiment analysis integration