Insurance companies invest a lot of time and money into optimizing their pricing and accurately estimating the likelihood that customers will make a claim. In many countries insurance it is a legal requirement to have car insurance in order to drive a vehicle on public roads, so the market is very large!
Knowing all of this, On the Road car insurance have requested your services in building a model to predict whether a customer will make a claim on their insurance during the policy period. As they have very little expertise and infrastructure for deploying and monitoring machine learning models, they've asked you to use simple Logistic Regression, identifying the single feature that results in the best performing model, as measured by accuracy.
They have supplied you with their customer data as a csv file called car_insurance.csv, along with a table detailing the column names and descriptions below.
The dataset
| Column | Description |
|---|---|
id | Unique client identifier |
age | Client's age:
|
gender | Client's gender:
|
driving_experience | Years the client has been driving:
|
education | Client's level of education:
|
income | Client's income level:
|
credit_score | Client's credit score (between zero and one) |
vehicle_ownership | Client's vehicle ownership status:
|
vehcile_year | Year of vehicle registration:
|
married | Client's marital status:
|
children | Client's number of children |
postal_code | Client's postal code |
annual_mileage | Number of miles driven by the client each year |
vehicle_type | Type of car:
|
speeding_violations | Total number of speeding violations received by the client |
duis | Number of times the client has been caught driving under the influence of alcohol |
past_accidents | Total number of previous accidents the client has been involved in |
outcome | Whether the client made a claim on their car insurance (response variable):
|
# Import required libraries and suppress messages
suppressMessages(library(dplyr))
suppressMessages(library(readr))
suppressMessages(library(glue))
suppressMessages(library(yardstick))
library(readr)
library(dplyr)
library(glue)
library(yardstick)
# Start coding!
# Read in dataset
cars = read_csv('car_insurance.csv')
# View data types
str(cars)
# Missing values per column
colSums(is.na(cars))
# Distribution of credit_score
summary(cars$credit_score)
# Distribution of annual_mileage
summary(cars$annual_mileage)
# Fill missing values with the mean
cars$credit_score[is.na(cars$credit_score)] <- mean(cars$credit_score, na.rm = TRUE)
cars$annual_mileage[is.na(cars$annual_mileage)] <- mean(cars$annual_mileage, na.rm = TRUE)
# Feature columns
features <- names(subset(cars, select = -c(id,outcome)))
# Empty vector to store accuracies
accuracies <- c()
# Loop through features
for (col in features) {
# Create a model
model <- glm(glue('outcome ~ {col}'), data = cars, family = 'binomial')
# Get prediction values for the model
predictions <- round(fitted(model))
# Convert to a table
outcomes <- table(predictions, cars$outcome)
# If table only has one row
if (dim(outcomes)[1] == 1) {
# Accuracy is the first value divided by all values
accuracy <- outcomes[1] / sum(outcomes)
} else {
# Accuracy is the sum of the top left and bottom right values divided by all values
accuracy <- (outcomes[1,1] + outcomes[2,2]) / sum(outcomes)
}
# Append accuracy to accuracies
accuracies <- c(accuracies, accuracy)
}
# Find the feature with the largest accuracy
best_feature <- features[which.max(accuracies)]
best_accuracy <- max(accuracies)
# Create best_feature_df
best_feature_df <- data.frame(best_feature, best_accuracy)
# Run in a new cell to check your solution
best_feature_df