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
# Start coding!# Read in the dataset using dataframe
df = pd.read_csv("car_insurance.csv")
df.head()# Explore the data to check datatypes and missing values
df.info()# Explore the distribution with descriptive statistics
df.describe()# Fill the missing values to prepare the data for modeling
df["credit_score"].fillna(df["credit_score"].mean(), inplace = True)
df["annual_mileage"].fillna(df["annual_mileage"].mean(), inplace = True)# Create variables for modeling
# create a list "models"
models = []
# Create a variable "feature"
features = df.drop(columns = ["outcome", "id"]).columns# Building and storing the variables
# Loop through features and create Logistic Regression models
models = [] # Initialize an empty list to store the models
for feature in features:
# Create and fit the model
model = logit(f"outcome ~ {feature}", data = df).fit()
# Append model to the list "models"
models.append(model)
# Measuring Peformance
# Create a list for model accuracies
accuracies = []
# Loop through the models and compute confusion matrix
for feature in range(0, len(models)):
conf_mx = models[feature].pred_table()
TN = conf_mx[0,0]
TP = conf_mx[1,1]
FN = conf_mx[1,0]
FP = conf_mx[0,1]
# Calculate accuracy
acc = (TN + TP)/ (TN + TP + FN + FP)
# Append acc to accuracies
accuracies.append(acc)# Finding the best performing model
# Identify the index of accuracies with largest score
best_feature = features[accuracies.index(max(accuracies))]
# Create best feature dataframe
best_feature_df = pd.DataFrame({"best_feature" : best_feature,
"best_accuracy" : max(accuracies)},index = [0])
best_feature_df