YouTube-K-means-Clustering-Analysis

YouTube Channels Clustering Analysis 🎥📊

This repository contains a comprehensive analysis of YouTube channels based on their “video views” and “uploads”. We aim to group similar channels together using the k-means clustering algorithm.

🎯 Objective

Our main objective is to understand the pattern among YouTube channels based on their engagement (views) and content production (uploads) patterns.

🗃 Data Source

The dataset is named Global YouTube Statistics.csv and contains details about YouTube channels, including their views, uploads, subscribers, and more.

💼 Technologies Used

• Python
• Pandas
• Matplotlib
• Scikit-learn

📋 Workflow

1. Data Preprocessing: Extracted relevant columns and scaled the data.
2. Elbow Test: Determined the optimal number of clusters.
3. K-means Clustering: Applied clustering to the data.
4. Visualization: Visualized the clusters using scatter plots.

📉 Visualizations

Elbow Method For Optimal K

Clusters of YouTube Channels based on Views and Uploads

🔍 Conclusion

The analysis allowed us to categorize YouTube channels into 4 distinct groups based on their views and uploads. This can be helpful for advertisers, marketers, and content creators to understand the different content production and engagement patterns on YouTube.

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