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:

Download and explore the dataset
Prepare the dataset for training
Create a linear regression model
Train the model to fit the data
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

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.

# Uncomment and run the commands below if imports fail
# !conda install numpy pytorch torchvision cpuonly -c pytorch -y
# !pip install matplotlib --upgrade --quiet
!pip3 install jovian --upgrade --quiet

from pathlib import Path
import os
import torch
import jovian
import torchvision
import torch.nn as nn
import pandas as pd
import matplotlib.pyplot as plt
import altair as alt
import torch.nn.functional as F
from torchvision.datasets.utils import download_url
from torch.utils.data import DataLoader, TensorDataset, random_split

jovian.configure()

[jovian] It looks like Jovian is already configured ( check ~/.jovian/credentials.json ).
Do you want to overwrite the existing configuration? [y/N]:

[jovian] Skipping..

device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')