Churn.docx - Introduction Customer Churn is a burning problem for Telecommunication companies In this project we simulate one such case of customer

Churn.docx - Introduction Customer Churn is a burning...

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IntroductionCustomer Churn is a burning problem for Telecommunication companies. In this project, we simulateone such case of customer churn where we work on a data of postpaid customers with a contract. The data has information about the customer usage behaviour, contract details and the payment details. The data also indicates which were the customers who cancelled their service. Based on this past data, we need to build a model which can predict whether a customer will cancel their service inthe future or not. For 3333 Postpaid customers, 10 features are being considered. Out of which Churn is our target variable.Summary of Problem statement for Business Stakeholders:We would be analyzing a data-set “Cellphone” and performing techniques like logistic regression to find service prediction for the customers in dataset. A telecommunications company requires a predictive model to choose which customers will leave their plan. The results will inform the Marketing and Customer Retention teams about which customer are likelyto leave their plan so that resources can be directed to these customers.Data Dictionary:VariablesChurn1 if customer cancelled service, 0 if notAccountWeeksnumber of weeks customer has had activeaccountContractRenewal1 if customer recently renewed contract, 0 ifnotDataPlan1 if customer has data plan, 0 if notDataUsagegigabytes of monthly data usageCustServCallsnumber of calls into customer serviceDayMinsaverage daytime minutes per monthDayCallsaverage number of daytime callsMonthlyChargeaverage monthly billOverageFeelargest overage fee in last 12 monthsRoamMinsaverage number of roaming minutesExploratory Data Analysis:Note: To better analyse the data in R, we renamed the dataset as ‘cell’Structure:
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R code:str(cell)Classes ‘tbl_df’, ‘tbl’ and 'data.frame':3333 obs. of 11 variables:$ Churn : num 0 0 0 0 0 0 0 0 0 0 ...$ AccountWeeks : num 128 107 137 84 75 118 121 147 117 141 ...$ ContractRenewal: num 1 1 1 0 0 0 1 0 1 0 ...$ DataPlan : num 1 1 0 0 0 0 1 0 0 1 ...$ DataUsage : num 2.7 3.7 0 0 0 0 2.03 0 0.19 3.02 ...$ CustServCalls : num 1 1 0 2 3 0 3 0 1 0 ...$ DayMins : num 265 162 243 299 167 ...$ DayCalls : num 110 123 114 71 113 98 88 79 97 84 ...$ MonthlyCharge : num 89 82 52 57 41 57 87.3 36 63.9 93.2 ...$ OverageFee : num 9.87 9.78 6.06 3.1 7.42 ...$ RoamMins : num 10 13.7 12.2 6.6 10.1 6.3 7.5 7.1 8.7 11.2 ...The number of observation in the dataset is 3333.There are 11 Variables in dataset. Target variable is “Churn” (1 if customer cancelled service, 0 if not).Summary:R code:summary(cell)Churn AccountWeeks ContractRenewal DataPlan DataUsage CustServCalls DayMins DayCalls Min. :0.0000 Min. : 1.0 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000 Min. : 0.0 Min. : 0.0 1st Qu.:0.0000 1st Qu.: 74.0 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.:143.7 1st Qu.: 87.0 Median :0.0000 Median :101.0 Median :1.0000 Median :0.0000 Median :0.0000 Median :1.000 Median :179.4 Median :101.0 Mean :0.1449 Mean :101.1 Mean :0.9031 Mean :0.2766 Mean :0.8165 Mean :1.563 Mean :179.8 Mean :100.4 3rd Qu.:0.0000 3rd Qu.:127.0 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.7800 3rd Qu.:2.000 3rd Qu.:216.4 3rd Qu.:114.0 Max. :1.0000 Max. :243.0 Max. :1.0000 Max. :1.0000 Max. :5.4000 Max. :9.000 Max. :350.8 Max. :165.0 MonthlyCharge OverageFee RoamMins Min. : 14.00 Min. : 0.00 Min. : 0.00 1st Qu.: 45.00 1st Qu.: 8.33 1st Qu.: 8.50 Median : 53.50 Median :10.07 Median :10.30 Mean : 56.31 Mean :10.05 Mean :10.24 3rd Qu.: 66.20 3rd Qu.:11.77 3rd Qu.:12.10 Max. :111.30 Max. :18.19 Max. :20.00
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