Capstone-project-Supervised_machinelearning_classification_-on_HEALTH-INSURANCE-CROSS-SELL-PREDICTIO
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.
An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee.
For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if, God forbid, you fall ill and need to be hospitalised in that year, the insurance provider company will bear the cost of hospitalisation etc. for upto Rs. 200,000. Now if you are wondering how can company bear such high hospitalisation cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalised that year and not everyone. This way everyone shares the risk of everyone else.
Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of certain amount to insurance provider company so that in case of unfortunate accident by the vehicle, the insurance provider company will provide a compensation (called ‘sum assured’) to the customer.
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. Health Insurance Cross Sell Prediction AlmaBetter Verfied Project - AlmaBetter School image
💾 Table of Content
📖 Introduction:
Insurance is a contract, represented by a policy, in which an individual or entity receives financial protection or reimbursement against losses from an insurance company.
This EDA will use Python libraries, matplotlib, and Seaborn to examine the Subscribed health insurance customers dataset through visualizations and graphs.
The dataset is of Subscribed Health insurance customers from insurance companies, contains information such as Age, Gender, Driving Licence, Region Etc.
Machine learning has a wide range of applications in our organization. Prediction and analysis has long been the most well-known application of machine learning, which fuels our sale prediction.
We're also utilizing machine learning to assist in designing our Sale strategies and Campaign programmes by identifying traits that lead to successful content.
We utilize it to help Company's to rapidly expand their reach to customers with appropriate data driven decisions.
We can also employ machine learning to improve Service and Customer retention, Target oriented promotions.
Our major goal in this project is to identify Customers who are interested in purchasing vehcle insurance based on subscribed health insurance data of the company.
dataset-cover
📖 Abstract:
The objective was to anticipate Customers who are interested in purchasing vehcle insurance.
Exploratory Data Analysis is done on the dataset to get the insights from the information however the principal invalid qualities are taken care of. Likewise, some hypothesis testing was additionally performed from the experiences from EDA.
After that Response variable is our objective variable must be highlighted where Analysis activities are performed on it and after that visualization has done for it to understand hidden insights.
From that point forward, all that was left was to track down the important factors and feature encoding and fit our models by creating various features, and further, the model is assessed utilizing the metrics.
📖 Dataset information:
id - A unique id for each customer.
Gender - Gender details of the health insurance owner.
Age - Age details of the health insurance owner.
Driving_License - Whether the customer has a driving license or Not.
Region Code - Region with code details of the health insurance owner.
Previously_Insured -Whether the customer previously_Insured or Not.
Vehicle_Age - Age of vehicle of the health insurance owner.
Vehicle_Damage - Whether the customer Vehicle Damaged or Not.
Annual Premium - Annual Premium amount details of a Customer.
Policy_Sales_Channel - Policy Sales Channel shows us,the number of the sales channel.
Response - Response of the customer to buying vehicle insurance.
📖 Problem Statement:
This dataset consists information Subscribed Health insurance customers from insurance company.
The dataset is provided by the insurance company.
They are expanding their services to vehicle insurance from Health insurance.
The task was to predict and build the model to understand the factors and customers who are interested in vehicle insurance.
It will be interesting to explore what all other insights can be obtained from the same dataset.
Integrating the factors affecting the purchase and insights from the data, will help companie to identify strategies and expand there reach to customer.
📖 Conclusion:
Our main goal in this project was to determine different factors based on response, which we have done.
After fitting different models, we did hyperparameter tunig for better results.
which we evaluated using the different evaluation metrics.
Comparing the ROC curve we concluded that the Random Forest model performs better..