Question 2.1
My project for a utility company is to identify if a particular customer is stealing electricity by tampering with the
meter and bypassing the device that records the amount of kWh the premise is consuming. Customers have been
known to use several different methodologies, including installing jumper cables on the meter, to achieve this goal
and essentially receive free power for their home. Some predictors I use to feed a model in identifying patterns in
such customers include:
1. number of of tamper events - the smart meters register multiple types of events, such as a “removal
tamper” event when the meter is pulled out of the base and a “reconnection” event when it is plugged back
in
2. kWh - usage recorded on the meter, ex: looking for a period of zero usage following a removal tamper
event
3. latitude/longitude - the relative location of previous theft cases compared to new cases showed a strong
correlation as it seems customers who have a similar method of stealing can be duplicated with
surrounding neighborhoods
4. property type - residential vs. commercial vs. mobile home vs. vacation home etc.
5. account delinquent nonpayment credit score - customer account level information that is compiled from
the billing system to narrow down the behavior of customers
Question 2.2
# load libraries
library(kernlab)
library(kknn)
# load and visualize data
data <- read.table("credit_card_data.txt", header=FALSE, strip.white=TRUE)
head(data)
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