Regression with an insurance dataset #997
Description
To perform regression on an insurance dataset, the goal is to build a model that predicts the target variable (likely "charges" in an insurance dataset) using features such as age, sex, BMI, number of children, smoking status, region, and other factors.
Here’s a step-by-step guide on how to approach this regression task using the insurance dataset. I will explain it in the context of building a pull request (PR) that addresses this problem.
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Problem Understanding:
In the insurance dataset, each record consists of information about a customer, including personal details (age, sex, BMI), number of children, whether the person smokes, and the region they live in. The target variable for regression is usually the insurance charges (the amount the customer has to pay for the insurance). -
Approach:
Goal: Predict the insurance charges (charges) based on the available features.
Model: Regression (e.g., Linear Regression, Random Forest Regressor, etc.).
Metrics: Evaluate model performance using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). -
Step-by-Step Template for the Pull Request (PR)
Title:
"Implemented Regression Model to Predict Insurance Charges (regression with an insurance dataset #994)"
Description:
This pull request implements a regression model to predict insurance charges using the provided dataset. The dataset contains various features such as age, sex, BMI, number of children, smoking status, and region, which are used to predict the target variable: insurance charges.
Checklist:
Code has been tested and works as expected.
Proper comments have been added.
Performance metrics (RMSE, R-squared) have been evaluated and reported.
Relevant model selection and results are documented.
Code is properly structured and readable.