InvestKuy: Optimal Investment Recommendation System

InvestKuy is a smart solution designed to optimize your investment decisions using the Knapsack Problem approach. The app provides tailored investment recommendations based on your risk profile and investment duration.

Table of Contents

• Features
• Installation
• Usage
• Dataset
• Algorithms
• Contributing
• License

Features

• Risk Profile Selection: Choose between Conservative, Moderate, or Aggressive risk profiles.
• Investment Duration: Short, Mid, or Long-term options available.
• Algorithm Selection: Supports multiple algorithms for investment optimization:
  • 0/1 Knapsack with Dynamic Programming
  • 0/1 Knapsack with Brute Force
  • 0/1 Knapsack with Greedy
  • Unbounded Knapsack with Dynamic Programming
  • Unbounded Knapsack with Greedy
• User-Friendly Interface: Built with Streamlit for a seamless user experience.

Installation

Follow these steps to set up the project locally:

1. Clone the repository:

   git clone https://github.com/yourusername/investkuy.git

2. Navigate to the project directory:

   cd investkuy

3. Install the required Python packages:

   pip install -r requirements.txt

Usage

1. Run the Streamlit app:

   streamlit run app.py

2. Open the app in your browser at http://localhost:8501.
3. Follow the on-screen instructions to:
   • Select your risk profile.
   • Choose your investment duration.
   • Set your initial investment capital.
   • Get optimized investment recommendations.

Dataset

The app uses a dataset containing mutual fund information, including:

• Fund names
• Prices per lot
• Risk levels (low, medium, high)
• Historical returns over different durations (1M, 1Y, 3Y)

The dataset should be placed in the root directory and named Invest Kuy_Clean Dataset - Investkuy_Dataset - Mutual_Funds.csv.

Algorithms

InvestKuy implements the following algorithms to solve the Knapsack Problem:

• 0/1 Knapsack:

  • Brute Force: Examines all possible subsets to find the optimal solution.
  • Dynamic Programming: Uses a table to optimize the selection process.
  • Greedy: Selects items based on the highest value-to-weight ratio.

• Unbounded Knapsack:

  • Dynamic Programming: Optimizes for unlimited quantities of each item.
  • Greedy: Selects as many of the highest ratio items as possible.

Contributing

We welcome contributions! To contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-name

3. Make your changes and commit them:

   git commit -m "Add new feature"

4. Push your changes:

   git push origin feature-name

5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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Happy Investing! 🚀