K-Means Clustering Project 1

This is the first project in our K-Means Clustering course. In this project, we will delve into the basics of K-Means clustering and apply it to a real-world dataset.

Project Overview

• Objective: Apply K-Means clustering to a given dataset and interpret the results.
• Dataset: Dataset Link
• Tools: Python, scikit-learn

Steps to Complete the Project

1. Data Exploration: Understand the dataset and its features.
2. Data Preprocessing: Clean and preprocess the data if necessary.
3. K-Means Clustering: Apply K-Means clustering to the dataset.
4. Result Interpretation: Analyze the clusters formed and interpret the results.
5. Visualization: Visualize the clusters using plots.

Resources

• K-Means Clustering Tutorial
• Scikit-learn Documentation on K-Means

Example

Here's an example of how K-Means clustering can be applied to a dataset:

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

# Load dataset
data = load_dataset()

# Apply K-Means clustering
kmeans = KMeans(n_clusters=3)
kmeans.fit(data)

# Plotting the clusters
plt.scatter(data[:, 0], data[:, 1], c=kmeans.labels_)
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.title('K-Means Clustering')
plt.show()

Conclusion

K-Means clustering is a powerful tool for unsupervised learning. By following the steps outlined above, you will be able to apply K-Means clustering to your own datasets and gain valuable insights.

K-Means Clustering Visualization