# 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
```

```
WARNING: You are using pip version 20.1; however, version 20.1.1 is available.
You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.
```

```
import torch
import jovian
import torchvision
import torch.nn as nn
import pandas as pd
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
```

`project_name='assignment02-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.