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:
- 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 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.