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 identify the single feature that results in the best performing model, as measured by accuracy, so they can start with a simple model in production.
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 modules
import pandas as pd
import numpy as np
from statsmodels.formula.api import logit
# Load the dataset
data = pd.read_csv('car_insurance.csv')
# Handle missing values by filling with the mean
data["credit_score"].fillna(data["credit_score"].mean(), inplace=True)
data["annual_mileage"].fillna(data["annual_mileage"].mean(), inplace=True)
# Initialize variables to store the best feature and its accuracy
best_feature = None
best_accuracy = 0
# Feature columns
features = data.drop(columns=['id', 'outcome']).columns
# Empty list to store model results
models = []
# Loop through each feature to find the best one
for col in features:
# Create and fit the logistic regression model
model = logit(f'outcome ~ {col}', data=data).fit()
# Add the model to the list
models.append(model)
# Empty list to store accuracies
accuracies = []
# Loop through models to compute accuracies
for model in models:
# Compute the confusion matrix
conf_matrix = model.pred_table()
# True negatives
tn = conf_matrix[0,0]
# True positives
tp = conf_matrix[1,1]
# False negatives
fn = conf_matrix[1,0]
# False positives
fp = conf_matrix[0,1]
# Compute accuracy
acc = (tn + tp) / (tn + fn + fp + tp)
accuracies.append(acc)
# Find the feature with the highest accuracy
best_feature = features[accuracies.index(max(accuracies))]
# Create a DataFrame to store the best feature and its accuracy
best_feature_df = pd.DataFrame({
'best_feature': [best_feature],
'best_accuracy': [max(accuracies)]
})
# Display the result
print(best_feature_df)