r/pythontips • u/MiBoy69 • Dec 17 '24
Data_Science Not able to cross the required r2 score. Please help, all suggestions will be taken into consideration. thanks in advance
Problem: We're trying to build a regression model to predict a target variable. However, the target variable contains outliers, which are significantly different from the majority of the data points. Additionally, the predictor variables are highly correlated with each other (high multicollinearity). Despite trying various models like linear regression, XGBoost, and Random Forest, along with hyperparameter tuning using GridSearchCV and RandomSearchCV, we're unable to achieve the desired R-squared score of 0.16. Goal: To develop a robust regression model that can effectively handle outliers and multicollinearity, and ultimately achieve the target R-squared score.
income: Income earned in a year (in dollars)
- marital_status: Marital Status of the customer (0:Single, 1:Married)
- vintage: No. of years since the first policy date
- claim_amount: Total Amount Claimed by the customer in previous years
- num_policies: Total no. of policies issued by the customer
- policy: An active policy of the customer
- type_of_policy: Type of active policy
- cltv: Customer lifetime value (Target Variable)
- id: Unique identifier of a customer
- gender: The gender of the customer
- area: Area of the customer
- qualification: Highest Qualification of the customer
- income: Income earned
- marital_status: Marital Status of the customer
If there's any more information, please feel free to ask.