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 torchvision cpuonly -c pytorch -y
!pip install matplotlib pandas --upgrade --quiet
!pip install jovian --upgrade --quiet
!pip install seaborn
Collecting package metadata (current_repodata.json): done
Solving environment: done
# All requested packages already installed.
Requirement already satisfied: seaborn in /Users/grimbsi/opt/anaconda3/envs/deep-learning/lib/python3.7/site-packages (0.10.1)
Requirement already satisfied: numpy>=1.13.3 in /Users/grimbsi/opt/anaconda3/envs/deep-learning/lib/python3.7/site-packages (from seaborn) (1.18.1)
Requirement already satisfied: matplotlib>=2.1.2 in /Users/grimbsi/opt/anaconda3/envs/deep-learning/lib/python3.7/site-packages (from seaborn) (3.2.2)
Requirement already satisfied: pandas>=0.22.0 in /Users/grimbsi/opt/anaconda3/envs/deep-learning/lib/python3.7/site-packages (from seaborn) (1.0.5)
Requirement already satisfied: scipy>=1.0.1 in /Users/grimbsi/opt/anaconda3/envs/deep-learning/lib/python3.7/site-packages (from seaborn) (1.4.1)
Requirement already satisfied: cycler>=0.10 in /Users/grimbsi/opt/anaconda3/envs/deep-learning/lib/python3.7/site-packages (from matplotlib>=2.1.2->seaborn) (0.10.0)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/grimbsi/opt/anaconda3/envs/deep-learning/lib/python3.7/site-packages (from matplotlib>=2.1.2->seaborn) (2.4.7)
Requirement already satisfied: python-dateutil>=2.1 in /Users/grimbsi/opt/anaconda3/envs/deep-learning/lib/python3.7/site-packages (from matplotlib>=2.1.2->seaborn) (2.8.1)
Requirement already satisfied: kiwisolver>=1.0.1 in /Users/grimbsi/opt/anaconda3/envs/deep-learning/lib/python3.7/site-packages (from matplotlib>=2.1.2->seaborn) (1.2.0)
Requirement already satisfied: pytz>=2017.2 in /Users/grimbsi/opt/anaconda3/envs/deep-learning/lib/python3.7/site-packages (from pandas>=0.22.0->seaborn) (2020.1)
Requirement already satisfied: six in /Users/grimbsi/opt/anaconda3/envs/deep-learning/lib/python3.7/site-packages (from cycler>=0.10->matplotlib>=2.1.2->seaborn) (1.15.0)
import torch
import jovian
import torchvision
import torch.nn as nn
import pandas as pd
import numpy as np
import seaborn as sns
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.