Stock-Analysis

A stock market analysis and prediction web application built using Streamlit - Python. The project utilizes machine learning models (Linear Regression, Random Forest, LSTM) to predict stock prices and visualize stock trends.

Features

• Fetches real-time stock data using yfinance.
• Supports multiple prediction models: Linear Regression, Random Forest, and LSTM.
• Displays stock charts using plotly.
• Identifies top gainers and losers in Sensex and Nifty.
• Deployable on Streamlit and Flask.

Installation

Prerequisites

• Python 3.10+
• Virtual environment (venv or conda recommended)
• Required dependencies (listed in requirements.txt)

Steps

1. Clone the repository:

   git clone https://github.com/Yuvan-1166/Stock-Analysis.git
   cd stock-predictor

2. Create a virtual environment and activate it:

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Streamlit application:

   streamlit run Home.py

Usage

• Enter the stock ticker, start date, and end date.
• Choose a prediction model to forecast future stock prices.
• View stock trends, gainers, and losers in interactive charts.

Models Used

• Linear Regression: Simple and interpretable model for stock trend analysis.
• Random Forest: Provides better generalization and robustness.
• LSTM (Long Short-Term Memory): Captures sequential patterns in stock data.

Exporting & Using Trained Models

• Trained models are saved as .pkl files using joblib.
• Example:

  import joblib
  scaler = joblib.load('scaler.pkl')
  model = joblib.load('stock_predictor_model.pkl')

Contributing

• Fork the repo and create a new branch.
• Implement changes and submit a pull request.
• Ensure proper documentation and testing.

Author

Developed by Yuvan Shankar S.

Contact

For queries or collaboration, email: yuvanshankars.1166@google.com