3 years ago

# 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:

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

# Windows

# 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
``project_name='02-insurance-linear-regression' # will be used by jovian.commit``
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.