Insurance cost prediction using linear regression.

In this assignment we’re going to use information like a person’s age, sex, BMI, no. of children and smoking habit to predict the price of yearly medical bills. This kind of model is useful for insurance companies to determine the yearly insurance premium for a person. The dataset for this problem is taken from: https://www.kaggle.com/mirichoi0218/insurance

We will create a model with the following steps: 1. Download and explore the dataset 2. Prepare the dataset for training 3. Create a linear regression model 4. Train the model to fit the data 5. Make predictions using the trained model

This assignment builds upon the concepts from the first 2 lectures. It will help to review these Jupyter notebooks:

• PyTorch basics: https://jovian.ml/aakashns/01-pytorch-basics
• Linear Regression: https://jovian.ml/aakashns/02-linear-regression
• Logistic Regression: https://jovian.ml/aakashns/03-logistic-regression
• Linear regression (minimal): https://jovian.ml/aakashns/housing-linear-minimal
• Logistic regression (minimal): https://jovian.ml/aakashns/mnist-logistic-minimal