stock-insights

An intelligent stock recommendation system that provides personalized investment advice based on fundamental financial metrics and user-defined thresholds. Utilizes machine learning models to analyze stock data and offer actionable insights for investors.

Overview

This project aims to create an intelligent stock recommendation system that provides personalized investment advice based on fundamental financial metrics and user-defined thresholds. The system utilizes machine learning models to analyze stock data and offer actionable insights for investors.

Project Structure

• data/: Contains raw and processed data.
• models/: Stores trained machine learning models.
• notebooks/: Jupyter notebooks for exploratory data analysis and model development.
• src/: Source code directory.
  • data_collection.py: Script for data collection.
  • data_processing.py: Script for data preprocessing.
  • model_training.py: Script for model training.
  • prediction.py: Script for making predictions.
  • utils.py: Utility functions.
• requirements.txt: Lists all required Python packages.
• README.md: Project documentation and instructions.
• LICENSE: License information.

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/stock-recommendation-system.git
   cd stock-recommendation-system