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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:

  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

This assignment builds upon the concepts from the first 2 lessons. It will help to review these Jupyter notebooks:

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 torch

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

pip install jovian
Collecting jovian Downloading jovian-0.2.26-py2.py3-none-any.whl (66 kB) |████████████████████████████████| 66 kB 580 kB/s eta 0:00:011 Requirement already satisfied: pyyaml in /opt/conda/lib/python3.7/site-packages (from jovian) (5.3.1) Requirement already satisfied: click in /opt/conda/lib/python3.7/site-packages (from jovian) (7.1.1) Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from jovian) (2.23.0) Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->jovian) (1.25.9) Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests->jovian) (3.0.4) Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->jovian) (2020.12.5) Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->jovian) (2.9) Collecting uuid Downloading uuid-1.30.tar.gz (5.8 kB) Building wheels for collected packages: uuid Building wheel for uuid (setup.py) ... done Created wheel for uuid: filename=uuid-1.30-py3-none-any.whl size=6500 sha256=fba0d4c8972457413a57bfe75d4e88da9f8704ee4a6216fabc85cb6d1cff68e5 Stored in directory: /root/.cache/pip/wheels/2a/ea/87/dd57f1ecb4f0752f3e1dbf958ebf8b36d920d190425bcdc24d Successfully built uuid Installing collected packages: uuid, jovian Successfully installed jovian-0.2.26 uuid-1.30 Note: you may need to restart the kernel to use updated packages. 
import jovian
project_name='02-insurance-linear-regression' # will be used by jovian.commit