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 seaborn pandas torchvision cpuonly -c pytorch -y
!pip install matplotlib --upgrade --quiet
!pip install jovian --upgrade --quiet

Collecting package metadata (current_repodata.json): done
Solving environment: done


==> WARNING: A newer version of conda exists. <==
  current version: 4.8.2
  latest version: 4.8.3

Please update conda by running

    $ conda update -n base conda



## Package Plan ##

  environment location: /srv/conda/envs/notebook

  added / updated specs:
    - cpuonly
    - numpy
    - pandas
    - pytorch
    - seaborn
    - torchvision


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    cycler-0.10.0              |             py_2           9 KB  conda-forge
    icu-64.2                   |       he1b5a44_1        12.6 MB  conda-forge
    kiwisolver-1.2.0           |   py37h99015e2_0          87 KB  conda-forge
    matplotlib-base-3.2.1      |   py37h30547a4_0         7.1 MB  conda-forge
    patsy-0.5.1                |             py_0         187 KB  conda-forge
    scipy-1.4.1                |   py37ha3d9a3c_3        18.8 MB  conda-forge
    seaborn-0.10.1             |             py_0         158 KB  conda-forge
    statsmodels-0.11.1         |   py37h8f50634_1        10.1 MB  conda-forge
    ------------------------------------------------------------
                                           Total:        49.0 MB

The following NEW packages will be INSTALLED:

  cycler             conda-forge/noarch::cycler-0.10.0-py_2
  icu                conda-forge/linux-64::icu-64.2-he1b5a44_1
  kiwisolver         conda-forge/linux-64::kiwisolver-1.2.0-py37h99015e2_0
  matplotlib-base    conda-forge/linux-64::matplotlib-base-3.2.1-py37h30547a4_0
  patsy              conda-forge/noarch::patsy-0.5.1-py_0
  scipy              conda-forge/linux-64::scipy-1.4.1-py37ha3d9a3c_3
  seaborn            conda-forge/noarch::seaborn-0.10.1-py_0
  statsmodels        conda-forge/linux-64::statsmodels-0.11.1-py37h8f50634_1



Downloading and Extracting Packages
matplotlib-base-3.2. | 7.1 MB    | ##################################### | 100% 
cycler-0.10.0        | 9 KB      | ##################################### | 100% 
kiwisolver-1.2.0     | 87 KB     | ##################################### | 100% 
patsy-0.5.1          | 187 KB    | ##################################### | 100% 
scipy-1.4.1          | 18.8 MB   | ##################################### | 100% 
statsmodels-0.11.1   | 10.1 MB   | ##################################### | 100% 
icu-64.2             | 12.6 MB   | ##################################### | 100% 
seaborn-0.10.1       | 158 KB    | ##################################### | 100% 
Preparing transaction: done
Verifying transaction: done
Executing transaction: done

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='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.