Algorithm Selector is a web application that uses Reinforcement Learning to select the best machine learning algorithm for a given dataset. The project leverages Deep Q Networks and Q Learning to evaluate and choose among several algorithms: Support Vector Machine (SVM), Decision Tree, k-Nearest Neighbors (kNN), and Random Forest. The web interface is built using Flask, allowing users to upload datasets and receive recommendations on the best algorithm to use.

1. Features
2. Technologies Used
3. Setup and Installation
4. How to Run
5. Usage
6. Advantages

• Algorithm Selection: Utilizes Deep Q Networks and Q Learning to determine the most suitable machine learning algorithm for your dataset.
• Multiple Algorithms Supported: Compares SVM, Decision Tree, kNN, and Random Forest.
• User-friendly Web Interface: Easy-to-use interface for uploading datasets and receiving algorithm recommendations.
• Automatic Learning: The system improves its recommendations over time through reinforcement learning.

• Flask: Web framework used for creating the web interface.
• Scikit-Learn: Machine learning library for implementing the algorithms.
• Pandas: Data manipulation and analysis library for handling datasets.
• NumPy: Library for numerical computations.
• Reinforcement Learning: Deep Q Networks and Q Learning for algorithm selection.

• Python 3.6 or higher
• Pip (Python package installer)

1. Clone the repository:

   git clone https://github.com/your-username/algorithm-selector.git
   cd algorithm-selector

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate   # On Windows, use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Install the required packages:

   pip install -r requirements.txt

1. Start the Flask application:

   python app.py

2. pen your web browser and navigate to:

   http://127.0.0.1:5000

1. Upload a CSV file:

   • Ensure your CSV file has a proper format and the target variable column.
   • The target variable is the column you want the algorithms to predict.

2. Select the target variable:

   • Enter the name of the target variable in the provided form.

3. Get the recommendation:

   • The application will process the dataset and recommend the best algorithm based on its learning.

1. Automated Selection: Saves time by automatically determining the best algorithm for your dataset.
2. Improves Over Time: The reinforcement learning model improves its accuracy with more data and usage.
3. User-Friendly: Simple and intuitive web interface for ease of use.
4. Versatile: Supports multiple machine learning algorithms, making it suitable for various types of datasets.