Clustering Model

Overview

This repository contains an experimental machine learning clustering model developed using the K-Means algorithm. The model partitions data into clusters based on feature similarity, aiming to identify inherent groupings within the dataset. Currently, the model is in an unstable state and has not been optimized for accuracy. It is intended for research purposes and is not recommended for production use.

Pre-trained Model

A pre-trained version of the clustering model is available in the model.pkl file. This file can be used to apply the clustering algorithm to new datasets. Please note that due to the model's current instability, results may vary, and further optimization is required for improved accuracy.

Requirements

To run the model, ensure you have the following dependencies installed:

• Python 3.10 or higher
• Required Python packages (listed in requirements.txt)

Setup Instructions

1. Fork the Repository:

   To use this repository, you first need to fork it into your own GitHub account. You can then clone it locally to make changes.

   git clone <repo>
   

2. Set Up the Environment:

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

3. Install Dependencies:

   pip install -r requirements.txt
   
   

Important note:

• This repository comes with a pre-trained K-Means model with the dataset. If you want more accuracy, please re-train the model with a cleaner dataset and perform feature engineering on the model.