Automated Classification Model Selector

This project implements an automated pipeline that allows users to upload their datasets and automatically identifies the classification algorithm that produces the highest accuracy. The pipeline evaluates multiple machine learning models to find the best-performing one for the given dataset, providing users with an easy way to determine the optimal classification model..

Features

• Automated Model Selection: Automatically selects the classification model with the highest accuracy for the user's dataset.
• Multiple Algorithms: Compares the performance of various classification algorithms.
• User-friendly Interface: Simple interface for dataset upload and automatic performance comparison.

Run Locally

Clone the project

  git clone git clone https://github.com/codexkunal/Automated-Classification-Model-Selector

Go to the project directory

  cd Automated-Classification-Model-Selector

Install library

  pip install -r requirements.txt

Usage

• Upload Dataset: Upload your dataset in CSV format through the interface or directly to the system.
• Model Evaluation: The system will evaluate various classification algorithms and provide the one with the highest accuracy.
• View Results: The model with the best performance will be displayed along with its accuracy score.