Predicting Car Insurance Claims

Overview

Insurance companies invest significant resources into accurately predicting customer behavior, such as the likelihood of filing insurance claims. On the Road car insurance approached us to build a predictive model that determines whether a customer will make a claim during their policy period. This project aims to identify the most predictive feature using machine learning techniques, specifically logistic regression, to assist On the Road in optimizing their pricing and risk assessment strategies.

Dataset

The dataset, car_insurance.csv, contains various client attributes and a binary outcome indicating whether a claim was made:

id: Unique client identifier
age: Client's age category
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 (normalized)
vehicle_ownership: Client's vehicle ownership status
vehicle_year: Year of vehicle registration
married: Client's marital status
children: Number of children
postal_code: Client's postal code
annual_mileage: Number of miles driven annually
vehicle_type: Type of vehicle
speeding_violations: Total number of speeding violations
duis: Number of times caught driving under the influence
past_accidents: Total number of previous accidents
outcome: Binary variable indicating if a claim was made (0: No claim, 1: Made a claim)

Methodology

Data Preparation: Handle missing values for credit_score and annual_mileage.
Model Building: Build logistic regression models for each feature to predict the outcome.
Model Evaluation: Calculate accuracy metrics to determine the best performing feature.

Implementation

Requirements

Ensure you have Python and the following libraries installed:

pandas
numpy
statsmodels

Steps to Run

Clone the repository:

git clone https://github.com/NonsoOmoko/Project-Modelling-Car-Insurance-Claim-Outcomes.git
cd Project-Modelling-Car-Insurance-Claim-Outcomes