The data of customers is analyzed by dividing them into clusters based on their age, annual income and spending score. Classification is also done based on the genders and comparison of all three parmeters is also provided
This is a model which uses K-means clustering Algorithm.
The algorithm captures the insight that each point in a cluster should be near to the center of that cluster. First k is chosen i.e. the number of clusters to be found in the data using elbow method. For the analysis specifically Python Libraries like Numpy, Scikit Learn, Pandas and Seaborn is used
The target customers are those in green and cyan coloured clusters. Those with high spending scores are priority customers.
The number of female customers are greater and their count in the range 40-65 of spending score observed is more.
The target customers are those having high annual income. The ones with high spending scores are priority customers. The company may send more advertisments to those with high income and low expenditure.
This model can be useful to analyze and advertize the products to the right set of people.