Data Analysis project in which we analyze a Teleom Dataset and infer on whether a client will churn (change provider) or not.
This dataset contains information about US Telecom customers. Each row represents a customer. The dataset has the following variables on the columns:
● state: code of the US state of customer residence
● account length: number of months the customer has been with the current telco provider
● area code
● international plan: the customer has an international plan (Yes=1)
● voice mail plan: the customer has a voice-mail plan (Yes=1)
● number vmail messages: number of voice-mail messages
● total day minutes: total minutes of calls during the day
● total day calls: total number of calls during the day
● total day charge: total charge of day charge
● total eve minutes: total minutes of calls during the evening
● total eve calls: total number of calls during the evening
● total eve charge: total charge of evening charge
● total night minutes: total minutes of calls during the night
● total night calls: total number of calls during the night
● total night charge: total charge of night charge
● total intl minutes: total minutes of international calls
● total intl calls: total number of international calls
● total intl charge: total charge of international charge
● number customer service calls: number of calls to customer service
● churn: customer changed telco provider (Yes=1)
- Build a classification model to help Telecom in targeting the willing-to-churn customers before it is too late.
- Cluster customers according to their behavior.