A Study on Unsupervised K-means Algorithm
Objective
The Machine Learning objective of this project is to study and use unsupervised K-means clustering algorithm for the purpose of customer segmentation.
Abstract
In this project, we will study and implement K-Means algorithm. The k-means algorithm is normally the most known and used clustering method. It is an unsupervised clustering method as it doesn’t require any labels for the data to be clustered in homogeneous groups. We will study all the procedures that are required to develop a k-means clustering model. Elbow method is used to determine the optimal number of clusters. Here, we will use customer data in order to segment or cluster them into multiple homogeneous groups using k-means algorithm.
KEYWORDS: Unsupervised Learning, K-Means, Clustering, Elbow method.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
SOFTWARE FRONT END REQUIREMENTS
H/W CONFIGURATION:
Processor- I3/Intel Processor
Hard Disk- 160GB
Key Board- Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - ANY
RAM- 8GB
S/W CONFIGURATION:
Operating System: Windows 7/8/10
Server side Script: HTML, CSS, Bootstrap & JS
Programming Language: Python
Libraries: Flask, Pandas, Mysql.connector, Os, Smtplib, Numpy
IDE/Workbench: PyCharm
Technology: Python 3.6+
Server Deployment: Xampp Server
Related Projects
Paper Publishing
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