Exploratory analysis is of the telecom segment. The company has three sets of customer and service data, with a variable that determines whether the customer has endorsed (churn) or not the telecom company..
The purpose is that with these data sets and based on some hypotheses that we will formulate and that will be answered by the EDA, can obtain some initial insights for the construction of artificial intelligence that can "predict" the abandonment of still active customers.
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churn_customers.csv = Client data
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churn_contracts.csv = Contracts data
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churn_services.csv = Services data
Univariate analysis is a fundamental statistical technique in the field of Artificial Intelligence (AI), used to examine and understand the behavior of a single variable in a dataset. In other words, it focuses on the analysis of one feature or variable at a time, without considering relationships with other variables.
- Client age group has a strong association with Churn
- A client who com less than 6 months of active contract is more prone to Churn
- Customer with monthly contracts are prone to Churn
Bivariate Analysis is a statistical approach that focuses on the relationship between two variables in a dataset. Unlike Univariate Analysis, which explores individual features, Bivariate Analysis seeks to understand how two variables are interconnected and whether there is any significant relationship between them.