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.