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
!pip install jovian --upgrade --quiet

import mxnet
from mxnet import gluon, init, npx, np
import jovian
# import torchvision
from mxnet.gluon import Block, nn
from mxnet.gluon import loss as gloss
import pandas as pd
import matplotlib.pyplot as plt
# import torch.nn.functional as F
from mxnet.test_utils import download
from mxnet.gluon.data import dataset, RandomSampler, DataLoader
# from torch.utils.data import DataLoader, TensorDataset, random_split

npx.set_np()

project_name='Assignment-2-Rishabh' # will be used by jovian.commit

Step 1: Download and explore the data

Let us begin by downloading the data. We'll use the download_url function from PyTorch to get the data as a CSV (comma-separated values) file.