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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 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

Lets start our assignment by importing various modules and libraries.

import torch
import numpy as np
import jovian
import torchvision
import torch.nn as nn
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import torch.nn.functional as F
from torchvision.datasets.utils import download_url
from torch.utils.data import DataLoader, TensorDataset, random_split
%matplotlib inline
project_name='insurance-linear-regression'

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.

DATASET_URL = "https://hub.jovian.ml/wp-content/uploads/2020/05/insurance.csv"
DATA_FILENAME = "insurance.csv"
download_url(DATASET_URL, '.')
Using downloaded and verified file: ./insurance.csv