Lab | Supervised ML Regression Challenge

Introduction

In this exercise, you will be tasked with building a predictive model for insurance costs based on various features from a dataset. This exercise focuses on regression techniques in supervised machine learning. The goal is to achieve the highest R² score on the test data, so be prepared to explore different modeling approaches.

Getting Started

Open the main.ipynb file located in the your-code directory. The notebook contains a series of exercises to guide you through the process of data exploration, preprocessing, model training, and evaluation. If you encounter difficulties with one exercise, feel free to move on to the next.

Dataset

You will find the dataset in the CSV file named insurance.csv. The target column to predict is labeled "charges".

Deliverables

• main.ipynb with your responses and code implementations for each exercise.

Guidelines

• Use train_test_split with:
  • random_state = 42
  • test_size = 0.3

Resources

• Machine Learning Basics | Scikit-Learn Documentation
• Understanding R² Score | Towards Data Science