- Businesses employing customer segmentation are better able to target specific groups of consumers with messages that those consumers would find relevant and lead them to buy something.
- Malls or shopping complexes are often indulged in the race to increase their customers and hence making huge profits.
- Customer segmentation model allows for the effective allocation of marketing resources and the maximization of cross- and up-selling opportunities.
- In this project, we will be building a Machine Learning modelfor Customer Segmentation using Unsupervised Learning.
- We will use K-Means Clustering algorithm for Customer Segmentation.
- We had used Mall Customer Dataset from Kaggle for Customer Segmentation.
- K-Means Clustering is an unsupervised learning algorithm that is used to solve the clustering problems in machine learning or data science.
- It allows us to cluster the data into different groups and a convenient way to discover the categories of groups in the unlabeled dataset on its own.
- It is a centroid-based algorithm, where each cluster is associated with a centroid. The main aim of this algorithm is to minimize the sum of distances between the data point and their corresponding clusters.
- Before training, we have done preprocessing on the dataset such as detecting and replacing NULL values and outliers by it mean and median values respectively and also extracted only useful variable for model training.
- For determining appropriate number of clusters, we have use Elbow method.
- Then we trained the model using K-means clustering algorithm and divided the customers into 5 groups based on their spending activity and annual income.