With the increase in business activities in the world, it is important for businesses to apply optimized marketing strategies to help them stay in the competition and make more profit.
It is important to know the needs and understand what the customer desires. Customer segmentation involves the process of grouping customers into groups based on common characteristics so that the management or the company can focus on each group efficiently according to their needs. Here machine learning approach of clustering technique is used to group the customers of a mall.
During the model development, different machine-learning libraries were used. The model showed that;
- Female customers had a higher spending score than Males of 56% and 44% respectively.
- By Annual Income, the least Income of customers is around 20 US Dollars, and an average earning of around 50–75 US Dollars.
- Ages, most regular customers are aged around 30–35 years.
- People at Age of 32 are the highest visiting customers.
Finally, regarding the distribution, we may conclude that most of the Customers have a Spending Score in the range of 35–59. There are customers who also have a high frequency around 73 and 75 spending scores. This approach is implemented using k-means which is an unsupervised clustering machine-learning algorithm.
- K-means
- Python
- Google Colab
- Data understanding
- Clustering technique
- Elbow joint
In machine learning clusteriing often groups samples in other to find and understand similarities. This data does not require any early knowledge or labels. This strengh of not requiring labels makes clustering to be knowned as an unsupervised machine learning technique.