# 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:

- 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 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.

```
# Uncomment and run the appropriate command for your operating system, if required
# Linux / Binder
# !pip install numpy matplotlib pandas torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
# Windows
# !pip install numpy matplotlib pandas torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
# MacOS
# !pip install numpy matplotlib pandas torch torchvision torchaudio
```

```
import numpy as np
import torch
import jovian
import torchvision
import torch.nn as nn
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import torch.nn.functional as F
from torchvision.datasets.utils import download_url
from torch.utils.data import DataLoader, TensorDataset, random_split
```

`project_name='02-insurance-linear-regression' # 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.