4 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: https://www.kaggle.com/mirichoi0218/insurance

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 lectures. 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 commands below if imports fail
# !conda install numpy pytorch torchvision cpuonly -c pytorch -y
# !pip install matplotlib --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
``project_name='assignment02-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.