Insurance cost prediction using linear regression

Make a submisson here: https://jovian.ai/learn/deep-learning-with-pytorch-zero-to-gans/assignment/assignment-2-train-your-first-model

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 Kaggle.

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 lessons. It will help to review these Jupyter notebooks:

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

As you go through this notebook, you will find a ??? in certain places. Your job is to replace the ??? with appropriate code or values, to ensure that the notebook runs properly end-to-end . In some cases, you'll be required to choose some hyperparameters (learning rate, batch size etc.). Try to experiment with the hypeparameters to get the lowest loss.