/HEALTH-INSURANCE-CROSS-SELL-PREDICTION

Primary LanguageJupyter Notebook

HEALTH-INSURANCE-CROSS-SELL-PREDICTION

Project Classification : Insurance Cross-Selling

-- Project Status: [Completed]

Project Summary :

Problem Statement :

The task is to use existing Health and Vehicle Insurance Customer Data to predict whether the any new Customers are open to purchasing Vehicle Insurance from this company .

About the Data :

We have the data of existing Health Insurance Customers , this Data includes 12 relevant data points such as age, gender, sales channel data, vehicle ownership data. And most importantly , the target variable : whether the customer has vehicle insurance or not . The Data is available for 390K existing customers .

Approach taken :

The task was divided into 2 main parts : 1.Statistical Analysis over the dataset to discover relationships between each feature and the target variable . So that this relationship information can be used by the management in making better Business decisions 2.Creating a Machine Learning Pipeline , that can take in the data of any new customer and predict whether they will be interested in vehicle insurance . It was required to kepp this pipeline modular , such that it can be retrained often when new data is collected

Technical Details for ML : We trained 5 Different Algorithms

( Logistic Regression , K-Nearest Neighbors , Random Forest , XGBoost and CatBoost ) We used GridSearchCV and BayesSearchCV for HyperParameter Tuning Comparing both F1 and AUC-ROC Score , we can see that Random Forrest and XGBoost model performs the best . Recall of 65%, Best AUC-ROC = 0.86 , Best F1=0.44

Conclusions : Insights from exploring the Data :

● Customers of age between 30 and 70 are more likely to buy insurance. ● Customers with Driving Licence have higher chance of buying Insurance. ● Customers with Vehicle Damage are more likely to buy insurance. ● Customers with Vehicle age between 1 and 2 years are more likely to interested. ● Customer who are not insured previously are more likely to be interested.

Python Libraries used

Datawrangling :

  • Numpy
  • Pandas

For Graphing :

  • Matplotib
  • Seaborn

Machine learning :

  • Scikit-Learn
  • SK-Opt
  • XGBoost
  • CatBoost

Miscellaneous :

  • Google colab tools

Getting Started

The Structure of the main IPYNB notebook :

  • About this Project

  • Problem Statement

  • Bussiness Goal

  • Approach Taken in this Project

    • Understanding the given Data

  • Initial Code : Initliaing the Data and Modules

    • Installing and Importing Libraries
    • Import Dataset and Initial Data Checks

  • Data Preparation and Cleaning

  • Exploratory Data Analysis

    • Initial Macro-Level Data Analysis
    • Variable wise EDA
      • Target Variable (Response)
      • Age variable
      • Annual_Premium
      • Gender variable
      • Driving License
      • Previously Insured
      • Vehicle Age
      • Vechicle damage
      • Vintage
      • Region Code
      • Policy Sales Channel
    • Correlation Plot for Numeric Features

  • Data Preprocessing and Feature Engineering

    • Outlier Treatment in feature : Annual_Premium
    • Label Encoding
    • Target Mean Encoding
    • Cleaned Data Exporting

  • Building Prediction Systems using ML Models

    • Import cleaned final data

    • Classifier Performance Reporting Function

      • Overfitting Underfitting Debugging Notes
      • Metrics to be used during HyperParameter
      • Random Forrest Specific Cutom Defined Metrics
      • Function Definations for Analytics report generation

    • Logistic Regression Classifier Algorithm

      • LR Classifier Generator Function
      • LR Hyper Parameter Tuning : GridSearch
      • Final Logistic Regression Training run

    • K Nearest Neighbours Classifier Algorithm

      • Default Parameters : KNeighborsClassifier
      • KNN Model Generator Funciton
      • KNN Hyper Parameter Tuning : GridSearch
      • Final KNN Training run

    • Random Forrests of Decsision Trees

      • Default Parameters : RandomForestClassifier
      • Base Estimator Generator Function
      • HyperParameter tuning using GridSearchCV
      • Final Training Run

    • Gradient Boosted Trees using XGBoost Library

      • XGBoost algorithn training and tuning notes
      • XGBoost Estimator Instance Generator Function
      • HyperParameter tuning using BayesSearchCV
      • Final Training Run for XGBoost
      • Feature Importance

    • Categorical Gradient Boosted Trees using CatBoost Library

      • Cleaning Raw Data with Categorical Encoding
      • CatBoost Estimator Instance Generator Function
      • Model Evaluation
      • Final Training Run for CatBoost
      • Feature Importance

  • Inferences and Conclusions

  • What Worked? What Did Not Work?

  • Future Work and More Ideas to Explore

    • Custom Metric , based on Cost of approaching te customer , so as to create