Marketing is a crucial for the growth of any business. One of the key for marketers is to know their customers and identify their needs.
The goal of this project is to apply customer segmentation on the dataset for a Bank in New York City. Visualized and explored data set to check that the assumptions K-means makes are fulfilled. In order to segment customers, K-means clustering algorithm, elbow method and Autoencoders are used.
K-means works by grouping some data points together (clustering) in an unsupervised fashion. Histograms of various clusters:
The elbow method is a heuristic method of interpretation and validation of consistency within cluster analysis designed to help find the appropriate number of clusters in a dataset. If the line chart looks like an arm, then the “elbow” on the arm is the value of k that is the best.
Performed dimensionality reduction using AutoEncoders. Autoencoders are type of Artificial Neural networks that are used to perform a task of data encoding.
