I have developed a Health Insurance Cross Sell Prediction Model

HEALTH-INSURANCE-CROSS-SELL-PREDICTION Model Development.

Problem Statement: Our client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from past year will also be interested in Vehicle Insurance provided by the company. Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue. Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel) etc.

This file includes a csv file which has data of all the previously insured customer. Some customer has purchased vehicle insurance also form the company.

Features are as below:

1. Gender 381109 non-null object
2. Age 381109 non-null int64
3. Driving_License 381109 non-null int64
4. Region_Code 381109 non-null float64
5. Previously_Insured 381109 non-null int64
6. Vehicle_Age 381109 non-null object
7. Vehicle_Damage 381109 non-null object
8. Annual_Premium 381109 non-null float64
9. Policy_Sales_Channel 381109 non-null float64,/li>
10. Vintage 381109 non-null int64
11. Response

This Notebook will cover :

Exploratory Data Analysis Data Modelling and Evaluation

Steps:

Installing and Importing Libraries Import Dataset and Initial Data Checks Data Preparation and Cleaning Exploratory Data Analysis Data Preprocessing and Feature Engineering Cleaned Data Exporting Hyperparameter Tuning Building Prediction Systems using ML Models Classifier Performance Analytics

Inferences and Conclusions:

We've drawn many interesting inferences from the health_insurance-cross-sell-prediction data , here's a summary of the few of them: this is confirmed with both the bivariate analysis of each feature ,as well as the Feature Importanaces returned by the notebook.

Conclusions:

Customers of age between 30 to 60 are more likely to buy insurance. Customers with Driving License have higher chance of buying Insurance. Customers with Vehicle_Damage are likely to buy insurance. The variables : Age, Previously_insured,Annual_premium are more afecting the target variable. comparing ROC curve we can see that Random Forest model preform better. Because curves closer to the top-left corner, it indicate a better performance.

📜 Credits

< Sarvesh > | Data Scientist | Machine Learning Engineer | Deep Learning enthusiast

Linkedin: Contact me for Data Science Project Collaborations

YouTube: Follow me for interesting AI/ML Projects

References:

1. https://scikit-learn.org/stable/
2. https://catboost.ai/
3. https://xgboost.ai/
4. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
5. https://www.kdnuggets.com/2020/05/hyperparameter-optimization-machine-learning-models.html