Feature Selection: By finding our high correlation fields and dropping some of them altogetherĢ. Dimension reduction or modification can make the predictions more accurate if data has multiple fields with high correlation. If there are multiple fields in the numerical data that are highly correlated, it makes sense to remove some of those fields from data or find modify the components in the way that they are aligned in the direction of data variation. When dealing with high-dimension data (a large number of columns containing data about various fields and aspects related to the data), it is important to reduce the dimensions to make the model simpler, faster, and with lower chances of overfitting. Note: Data for this analysis is available at And it can then be used to understand how much a customer is being charged based on the services he is getting and the options he has chosen and other data available. Using this data predictive model for customer churn can be created. The data ranges from demographic information to types of services being provided. One of the benefits of customer characteristics analysis is an improvement in business processes and practices which keep the company popular choice among the customer base and create customer loyalty in the long run.ĭata used for this analysis was obtained from Kaggle. Once these factors are understood, businesses can implement necessary changes and enhanced processes with the overall objective of preventing customers from leaving the company. Given the importance of keeping customers satisfied and minimizing the churn, it is important to understand the fundamental factors which lead customers to switch service providers. This poses a serious challenge to business owners. Also, the cost of acquiring a new customer is 10 times more than the cost to retain an existing customer. However, in the real world, the customer churn can be as high as 25% annually in the telecommunication industry. Businesses try to keep the customers satisfied, to retain them as long as possible. When a customer chooses to move away from the current provider to a new provider, it results in loss of business and revenue to the current provider. They can choose any service provider and may move away from the current provider. When choosing a telecommunication service provider, customers usually have many choices. Create New Dimensions along the Data Variability
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