New Age Solar sells and installs solar panels for residential homes. The company’s sales representatives contact and pay a personal visit to potential customers to present the benefits of installing solar panels. This high-touch approach works well as the customers feel that they receive personal services that meet their individual needs, but it is more expensive than other, mass-marketing approaches. The company wants to be very strategic about visiting potential customers who are more likely to install solar panels. The company has compiled a data set of past home visits by sales reps. The data include the age and annual income (in $1,000s) of the potential customer and whether or not the customer purchases the solar panels (Install: Y/N). A portion of the Solar_Data worksheet is shown in the accompanying table.

a. Perform KNN analysis on the Solar_Data worksheet to determine whether or not a potential customer is likely to purchase solar panels. Score the records of the 11 new potential customers in the Solar_Score worksheet. What is the optimal k? What is the predicted outcome for the first new potential customer? 

b. What is the misclassification rate for the optimal k? 

c. Report the accuracy, specificity, sensitivity, and precision rates for the test data set (for Analytic Solver) or validation data set (for R). 

d. Display the cumulative lift chart, decile-wise chart, and ROC curve. 

e. Based on your answers in part c and d, is KNN an effective way to classify potential customers?

Transcribed Image Text:

Income 115 68 E 73 Age 45 31 1 34 Install N Y ⠀ N