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 modules
import pandas as pd
import numpy as np
from statsmodels.formula.api import logit
# Start coding!
cins = pd.read_csv('car_insurance.csv')
# Replace the null values with the mean of the column
cins['credit_score'] = cins['credit_score'].fillna(cins['credit_score'].mean())
cins['annual_mileage'] = cins['annual_mileage'].fillna(cins['annual_mileage'].mean())
# Create a list to store the models
models = []
# Create a variable of features except for id and outcome
features = cins.columns.drop(['id', 'outcome'])
# Build one model per feature a save the result to a list with a for loop
# Loop through features
for feature in features:
print(f"{feature}")
model = logit(f"outcome ~ {feature}", data=cins).fit() # Fixed the formula string
models.append(model)
# Create a list to store model accuracies
accuracies = []
# Loop through the index of the models
for model in models: # Fixed the loop variable name
# Create a confusion matrix
conf_matrix = model.pred_table() # Fixed the method call
# Get the accuracy of each
acc = (conf_matrix[0,0] + conf_matrix[1,1]) / (conf_matrix[0,0] + conf_matrix[1,1] + conf_matrix[1,0] + conf_matrix[0,1])
# Append models accuracy to accuracies
accuracies.append(acc)
# Identify the index of accuracies with the largest score
accuracies.index(max(accuracies))
# Best score is on index 2
# Map the highest accuracy to the feature
best_feature = features[accuracies.index(max(accuracies))]
best_feature_df = pd.DataFrame({
"best_feature": best_feature,
"best_accuracy": max(accuracies)},
index=[0]
)
best_feature_df