This project is about predicting whether a customer will change telecommunications provider, something known as "churning".

The training dataset contains $3998$ samples. Each sample contains $19$ features and $1$ categorical variable "churn" which indicates the class of the sample. The $19$ input features and $1$ output variable are:

"state", string. $2-$letter code of the US state of customer residence

"account$\_$length", numerical. Number of months the customer has been with the current telco provider

"area$\_$code", string$=$"area$\_$code$\_$AAA" where AAA $=3$ digit area code.

"international$\_$plan", $($yes$/$no$).$ The customer has international plan.

"voice$\_$mail$\_$plan", $($yes$/$no$).$ The customer has voice mail plan.

"number$\_$vmail$\_$messages", numerical. Number of voice$-$mail messages.

"total$\_$day$\_$minutes", numerical. Total minutes of day calls.

"total$\_$day$\_$calls", numerical. Total minutes of day calls.

"total$\_$day$\_$charge", numerical. Total charge of day calls.

"total$\_$eve$\_$minutes", numerical. Total minutes of evening calls.

"total$\_$eve$\_$calls", numerical. Total number of evening calls.

"total$\_$eve$\_$charge", numerical. Total charge of evening calls.

"total$\_$night$\_$minutes", numerical. Total minutes of night calls.

"total$\_$night$\_$calls", numerical. Total number of night calls.

"total$\_$night$\_$charge", numerical. Total charge of night calls.

"total$\_$intl$\_$minutes", numerical. Total minutes of international calls.

"total$\_$intl$\_$calls", numerical. Total number of international calls.

"total$\_$intl$\_$charge", numerical. Total charge of international calls

"number$\_$customer$\_$service$\_$calls", numerical. Number of calls to customer service

"churn", $($yes$/$no$).$ Customer churn $-$ target variable.