kulkarni-vishwanath/job-a-thon

This repo contains the code and approach document related to JOB-A-THON conducted by Analytics Vidhya.

JOB-A-THON

This repo contains the code and approach document related to JOB-A-THON conducted by Analytics Vidhya. Link to Competition here.

Problem Statement

Your Client FinMan is a financial services company that provides various financial services like loan, investment funds, insurance etc. to its customers. FinMan wishes to cross-sell health insurance to the existing customers who may or may not hold insurance policies with the company. The company recommend health insurance to it's customers based on their profile once these customers land on the website. Customers might browse the recommended health insurance policy and consequently fill up a form to apply. When these customers fill-up the form, their Response towards the policy is considered positive and they are classified as a lead. Once these leads are acquired, the sales advisors approach them to convert and thus the company can sell proposed health insurance to these leads in a more efficient manner.

Now the company needs your help in building a model to predict whether the person will be interested in their proposed Health plan/policy given the information about:

Demographics (city, age, region etc.)

Information regarding holding policies of the customer

Recommended Policy Information

Data Dictionary

Variable	Description
ID	Unique Identifier for a row
City_Code	Code for the City of the customers
Region_Code	Code for the Region of the customers
Accomodation_Type	Customer Owns or Rents the house
Reco_Insurance_Type	Joint or Individual type for the recommended insurance
Upper_Age	Maximum age of the customer
Lower_Age	Minimum age of the customer
Is_Spouse	If the customers are married to each other ((in case of joint insurance))
Health_Indicator	Encoded values for health of the customer
Holding_Policy_Duration	Duration (in years) of holding policy (a policy that customer has already subscribed to with the company)
Holding_Policy_Type	Type of holding policy
Reco_Policy_Cat	Encoded value for recommended health insurance
Reco_Policy_Premium	Annual Premium (INR) for the recommended health insurance
Response	0 : Customer did not show interest in the recommended policy, 1 : Customer showed interest in the recommended policy

Evaluation Metric

The evaluation metrics for this competition is ROC AUC Score.

Leaderboard

Public Leaderboard: 214/2363:

Public LB Score: 0.715168786285103

Private Leaderboard: 214/2363

Private LB Score: 0.703859067099935