Skip to content

Health Insurance Cross-Sell Prediction

AIM

To predict whether a Health Insurance customer would be interested in buying Vehicle Insurance.

https://www.kaggle.com/datasets/anmolkumar/health-insurance-cross-sell-prediction

https://www.kaggle.com/code/sid4ds/insurance-cross-sell-prediction-eda-modeling

LIBRARIES NEEDED

LIBRARIES USED
  • pandas
  • numpy
  • scikit-learn (>=1.5.0 for TunedThresholdClassifierCV)
  • xgboost
  • catboost
  • lightgbm
  • matplotlib
  • seaborn
  • joblib

DESCRIPTION

Why is it necessary?

  • This project aims to predict the chances of cross-selling Vehicle insurance to existing Health insurance customers. This would be extremely helpful for companies because they can then accordingly plan communication strategy to reach out to those customers and optimise their business model and revenue.
How did you start approaching this project? (Initial thoughts and planning)
  • Going through previous research and articles related to the problem.
  • Data exploration to understand the features. Using data visualization to check their distributions.
  • Identifying key metrics for the problem based on ratio of target classes - ROC-AUC & Matthew's Correlation Coefficient (MCC) instead of Accuracy.
Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.).

EXPLANATION

DETAILS OF THE DIFFERENT FEATURES

Feature Name Description Type Values/Range
id Unique ID for the customer Numerical Unique numerical values
Gender Binary gender of the customer Binary [0: Male, 1: Female] (or other binary representations as applicable)
Age Numerical age of the customer Numerical Measured in years
Driving_License Indicates if the customer has a Driving License Binary [0: No, 1: Yes]
Region_Code Unique code for the customer's region Numerical Unique numerical values
Previously_Insured Indicates if the customer already has Vehicle Insurance Binary [0: No, 1: Yes]
Vehicle_Age Age of the vehicle categorized as ordinal values Categorical [< 1 year, 1-2 years, > 2 years]
Vehicle_Damage Indicates if the vehicle was damaged in the past Binary [0: No, 1: Yes]
Annual_Premium Amount to be paid as premium over the year Numerical Measured in currency
Policy_Sales_Channel Anonymized code for the channel of customer outreach Numerical Unique numerical codes representing various channels
Vintage Number of days the customer has been associated with the company Numerical Measured in days

WHAT I HAVE DONE

Exploratory Data Analysis

  • Summary statistics
  • Data visualization for numerical feature distributions
  • Target splits for categorical features

Data cleaning and Preprocessing

  • Removing duplicates
  • Categorical feature encoding

Feature engineering and selection

  • Discretizing numerical features
  • Feature selection based on model-based feature importances and statistical tests.

Modeling

  • Holdout dataset created or model testing
  • Setting up a framework for easier testing of multiple models.
  • Models trained: Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Gaussian Naive-Bayes, Decision Tree, Random Forest, AdaBoost, Multi-Layer Perceptron, XGBoost, CatBoost, LightGBM
  • Class imbalance handled through:
  • Class weights, when supported by model architecture
  • Threshold tuning using TunedThresholdClassifierCV
  • Metric for model-tuning: F1-score (harmonic weighted mean of precision and recall)

Result analysis

  • Predictions made on holdout test set
  • Models compared based on classification report and chosen metrics: ROC-AUC and MCC.

PROJECT TRADE-OFFS AND SOLUTIONS

Accuracy vs Recall & Precision

Data is heavily imbalanced, with only ~12% representing the positive class. This makes accuracy unsuitable as a metric for our problem. Our goal is to correctly predict all the positive samples, due to which we must focus on recall. However, this lowers the overall accuracy since some negative samples may be predicted as positive.

  • Solution: Prediction threshold for models is tuned using F1-score to create a balance between precision and recall. This maintains overall accuracy at an acceptable level while boosting recall.

SCREENSHOTS

Project workflow

  graph LR
    A[Start] --> B{Error?};
    B -->|Yes| C[Hmm...];
    C --> D[Debug];
    D --> B;
    B ---->|No| E[Yay!];
Feature distributions (Univariate Analysis)

01_featdist_age

02_featdist_license

03_featdist_regioncode

04_featdist_previnsured

05_featdist_vehicleage

06_featdist_vehicledamage

07_featdist_premium

08_featdist_policychannel

09_featdist_vintage

Engineered Features

10_featengg_agegroup

11_featengg_policygroup

Feature Distributions (Bivariate Analysis)

12_bivariate_pairplots

13_bivariate_spearmancorr

14_bivariate_pointbiserial

15_bivariate_tetrachoric

Feature Selection

16_featselect_pointbiserial

17_featselect_anova

18_featselect_tetrachoric

19_featselect_chisquared

20_featselect_mutualinfo

21_featselect_xgbfimp

22_featselect_xtreesfimp

MODELS USED AND THEIR PERFORMANCE

Best threshold after threshold tuning is also mentioned.
Model + Feature set ROC-AUC MCC Best threshold
Logistic Regression + Original 0.8336 0.3671 0.65
Logistic Regression + Extended 0.8456 0.3821 0.66
Logistic Regression + Reduced 0.8455 0.3792 0.67
Logistic Regression + Minimal 0.8177 0.3507 0.60
Linear DA + Original 0.8326 0.3584 0.19
Linear DA + Extended 0.8423 0.3785 0.18
Linear DA + Reduced 0.8421 0.3768 0.18
Linear DA + Minimal 0.8185 0.3473 0.15
Quadratic DA + Original 0.8353 0.3779 0.45
Quadratic DA + Extended 0.8418 0.3793 0.54
Quadratic DA + Reduced 0.8422 0.3807 0.44
Quadratic DA + Minimal 0.8212 0.3587 0.28
Gaussian Naive Bayes + Original 0.8230 0.3879 0.78
Gaussian Naive Bayes + Extended 0.8242 0.3914 0.13
Gaussian Naive Bayes + Reduced 0.8240 0.3908 0.08
Gaussian Naive Bayes + Minimal 0.8055 0.3605 0.15
K-Nearest Neighbors + Original 0.7819 0.3461 0.09
K-Nearest Neighbors + Extended 0.7825 0.3469 0.09
K-Nearest Neighbors + Reduced 0.7710 0.3405 0.01
K-Nearest Neighbors + Minimal 0.7561 0.3201 0.01
Decision Tree + Original 0.8420 0.3925 0.67
Decision Tree + Extended 0.8420 0.3925 0.67
Decision Tree + Reduced 0.8419 0.3925 0.67
Decision Tree + Minimal 0.8262 0.3683 0.63
Random Forest + Original 0.8505 0.3824 0.70
Random Forest + Extended 0.8508 0.3832 0.70
Random Forest + Reduced 0.8508 0.3820 0.71
Random Forest + Minimal 0.8375 0.3721 0.66
Extra-Trees + Original 0.8459 0.3770 0.70
Extra-Trees + Extended 0.8504 0.3847 0.71
Extra-Trees + Reduced 0.8515 0.3836 0.72
Extra-Trees + Minimal 0.8337 0.3682 0.67
AdaBoost + Original 0.8394 0.3894 0.83
AdaBoost + Extended 0.8394 0.3894 0.83
AdaBoost + Reduced 0.8404 0.3839 0.84
AdaBoost + Minimal 0.8269 0.3643 0.86
Multi-Layer Perceptron + Original 0.8512 0.3899 0.22
Multi-Layer Perceptron + Extended 0.8528 0.3865 0.23
Multi-Layer Perceptron + Reduced 0.8517 0.3892 0.23
Multi-Layer Perceptron + Minimal 0.8365 0.3663 0.21
XGBoost + Original 0.8585 0.3980 0.68
XGBoost + Extended 0.8585 0.3980 0.68
XGBoost + Reduced 0.8584 0.3967 0.68
XGBoost + Minimal 0.8459 0.3765 0.66
CatBoost + Original 0.8579 0.3951 0.46
CatBoost + Extended 0.8578 0.3981 0.45
CatBoost + Reduced 0.8577 0.3975 0.45
CatBoost + Minimal 0.8449 0.3781 0.42
LightGBM + Original 0.8587 0.3978 0.67
LightGBM + Extended 0.8587 0.3976 0.67
LightGBM + Reduced 0.8587 0.3983 0.67
LightGBM + Minimal 0.8462 0.3753 0.66

CONCLUSION

WHAT YOU HAVE LEARNED

Insights gained from the data

  1. Previously_Insured, Vehicle_Damage, Policy_Sales_Channel and Age are the most informative features for predicting cross-sell probability.
  2. Vintage and Driving_License have no predictive power. They are not included in the best model.
Improvements in understanding machine learning concepts
  1. Implemented threshold-tuning for more accurate results.
  2. Researched and utilized statistical tests for feature selection.
Challenges faced and how they were overcome
  1. Shortlisting the apropriate statistical test for bivariate analysis and feature selection.
  2. Deciding the correct metric for evaluation of models due to imbalanced nature of the dataset.
  3. F1-score was used for threshold-tuning. ROC-AUC score and MCC were used for model comparison.

USE CASES OF THIS MODEL

  • Companies can use customer data to predict which customers to target for cross-sell marketing. This saves cost and effort for the company, and protects uninterested customers from unnecessary marketing calls.

FEATURES PLANNED BUT NOT IMPLEMENTED

  • Complex model-ensembling through stacking or hill-climbing was not implemented due to significantly longer training time.