K-Means Clustering on Iris Dataset

This repository contains a Python project that performs K-Means clustering on the Iris dataset. The project involves finding the optimal number of clusters and visualizing the results.

Project Overview

The objective of this project is to apply the K-Means clustering algorithm to the Iris dataset to identify distinct clusters within the data. Key steps include:

1. Loading the Dataset: The Iris dataset is loaded from a CSV file.
2. Determining Optimal Clusters:
   • Elbow Method: Helps to find the optimal number of clusters by plotting inertia.
   • Silhouette Score: Evaluates the quality of clustering for different numbers of clusters.
3. Fitting the Model: The K-Means model is fitted with the optimal number of clusters.
4. Visualizing Clusters: Clusters are visualized to interpret the results.

Project Structure

• iris.csv: The dataset used for clustering.
• predict.py: The main script for performing clustering, determining optimal clusters, and visualizing results.

Installation

To run this project, you need to have Python installed. You also need to install the following packages:

pip install numpy pandas matplotlib scikit-learn