Customer Segmentation using K-Means Clustering and Parameter Tuning

The purpose of this dataset is to help us understand customer behavior and purchasing data in a supermarket mall through membership cards. The data includes basic information about customers such as their ID, age, gender, annual income, and spending score. Spending score is a parameter assigned to customers based on their purchasing behaviour. The goal of this dataset is to use unsupervised ML techniques, specifically K-Means clustering algorithm, to segment the customers into groups and identify the target customers who are likely to converge. By understanding the customers better, the mall can plan their marketing strategy accordingly and increase their revenue. This dataset is designed for learning purposes and serves as an excellent example of how to use unsupervised machine learning algorithms in practice.

The aim of this project is to segment customers of a mall. I am using K-means clustering algorithm to cluster customers based on their features such as age, gender, annual income, and spending score.

The mall wants to understand the customer segments to better target marketing efforts and improve sales. The objective of this project is to identify distinct customer segments and provide recommendations to the marketing team to develop targeted marketing strategies.

K-means clustering algorithm was used to develop the model. The optimal number of clusters was selected using the elbow method and silhouette score. The model was evaluated using within-cluster sum of squares (WCSS) and silhouette score.

The technique of splitting customers into separate groups depending on their attributes or behavior, makes this possible. Customer segmentation in machine learning can help you save money on marketing initiatives by reducing waste.

K-Means Clustering is an unsupervised learning algorithm that is used to solve the clustering problems in machine learning or data science. In this topic, we will learn what is K-means clustering algorithm, how the algorithm works, along with the Python implementation of k-means clustering.

Hyperparameters tuning is crucial as they control the overall behavior of a machine learning model. Every machine learning models will have different hyperparameters that can be set.

A hyperparameter is a parameter whose value is set before the learning process begins.

Hierarchical clustering is an unsupervised learning method for clustering data points. The algorithm builds clusters by measuring the dissimilarities between data. Unsupervised learning means that a model does not have to be trained, and we do not need a "target" variable.