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Project: Modeling Car Insurance Claim Outcomes
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  • 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

    ColumnDescription
    idUnique client identifier
    ageClient's age:
    • 0: 16-15
    • 1: 26-39
    • 2: 40-64
    • 3: 65+
    genderClient's gender:
    • 0: Female
    • 1: Male
    driving_experienceYears the client has been driving:
    • 0: 0-9
    • 1: 10-19
    • 2: 20-29
    • 3: 30+
    educationClient's level of education:
    • 0: No education
    • 1: High school
    • 2: University
    incomeClient's income level:
    • 0: Poverty
    • 1: Working class
    • 2: Middle class
    • 3: Upper class
    credit_scoreClient's credit score (between zero and one)
    vehicle_ownershipClient's vehicle ownership status:
    • 0: Does not own their vehilce (paying off finance)
    • 1: Owns their vehicle
    vehcile_yearYear of vehicle registration:
    • 0: Before 2015
    • 1: 2015 or later
    marriedClient's marital status:
    • 0: Not married
    • 1: Married
    childrenClient's number of children
    postal_codeClient's postal code
    annual_mileageNumber of miles driven by the client each year
    vehicle_typeType of car:
    • 0: Sedan
    • 1: Sports car
    speeding_violationsTotal number of speeding violations received by the client
    duisNumber of times the client has been caught driving under the influence of alcohol
    past_accidentsTotal number of previous accidents the client has been involved in
    outcomeWhether the client made a claim on their car insurance (response variable):
    • 0: No claim
    • 1: Made a claim
    # Import required modules
    import pandas as pd
    import numpy as np
    from statsmodels.formula.api import logit
    
    # Importing data
    data = pd.read_csv("car_insurance.csv")
    
    # Exploring the dataset
    data_info = pd.DataFrame({
        "Data Type" : data.dtypes,
        "Missing Values" : data.isna().sum()
    })
    print(data_info)
    
    
    # Filling missing values
    data["credit_score"].fillna(data["credit_score"].mean(), inplace= True)
    data["annual_mileage"].fillna(data["annual_mileage"].mean(), inplace= True)
    print(data.isna().sum())
    # Creating a list to store the models
    models = []
    
    # Dropping the columns
    columns_to_drop = ["id", "outcome"]
    features = data.drop(columns=columns_to_drop).columns
    
    # Modelling
    for col in features:
        model = logit(f"outcome ~ {col}", data=data).fit()
        models.append(model)
    
    # Calculating accuracy of each model
    accuracies = []
    for feature in range(0, len(models)):
        # Creating confusion matrix
        conf_matrix = models[feature].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 + tp + fn + fp)
        accuracies.append(acc)
    
    
    # Find the feature with the largest accuracy
    best_feature = features[accuracies.index(max(accuracies))]
    # Create the best feature df
    best_feature_df = pd.DataFrame({
        "best_feature" : [best_feature],
        "best_accuracy" : [max(accuracies)]},
        index=[0])  
    print(best_feature_df)