Customer Segmentation

Overview of the project

The following project is based on identifying potential customers for a particular product. This project will be implemented using R programming language. For machine learning techniques we will use K-means clustering. The algorithm is used for the project is very essential. Segmentation is the process of dividing customers into various groups for targeted selling. This data analytics project can help sellers a lot in many ways. The sellers can know about the customer’s mentality hence increasing the market for the sellers. 

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Procedure of the project

As the project is basically based on data manipulation, we will use the database programming language R. The huge data is based on demographics, geography, economic status, etc. The huge dataset can be downloaded from the internet for free. The first step involves importing all the necessary datasets from the internet and then going through it. Head () function is used to display the data rows and then use summary () to print the summary.

As the customers are of various age group and gender group. We need to divide that first to identify the customers. To display the gender distribution, pie charts can be used to portray the message accurately. For this barplot () function is used. Bar graphs can be used to display the varied age group that will make it easier for the seller for targeted selling. Then there will be a need for analysis of the customer’s annual income.

The annual income gives the idea of identifying the customer’s ability to buy expensive products. Hence, the seller can sell expensive products to those particular customers.  It is also important to know about the spending score of the customers. It will help in knowing about the customers who spent a lot on buying products. Histogram is used to predict the spending score of the customers.

K-means algorithm is used to process the whole data precisely. The algorithm select objects for initialization of the project, these objects are known as cluster centroids. To optimize the algorithm there are three methods popularly known such as Elbow method, Silhouette method, and Gap statistic method. You can get the arguments to execute them from the internet easily. The cluster would show like dots on a map. The concentration of dots shows the increase in potential customers in that area.

With the help of clusters showing the variation in customers, the seller can understand it easily. The seller can easily manipulate the concentration of dots and hence the print the data. Customer segmentation project will also result in better pattern reading and machine learning through data analytics. The targeted customers will result in less risk market for the sellers.

Conclusion

This project is easily implemented and is widely used by new startups. The new startups can easily be benefitted by this project. The R programming language makes it easier for the developer to implement it. The developer should have good knowledge about R programming language and data analytics to build this project accurately.

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• Machine Learning
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Customer Segmentation