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 --upgrade --quiet
!pip install pandas --upgrade --quiet
!pip install seaborn --upgrade --quiet
!pip install jovian --upgrade --quiet
Collecting package metadata (current_repodata.json): done
Solving environment: done
## Package Plan ##
environment location: /opt/conda
added / updated specs:
- cpuonly
- numpy
- pytorch
- torchvision
The following packages will be downloaded:
package | build
---------------------------|-----------------
ca-certificates-2020.6.20 | hecda079_0 145 KB conda-forge
certifi-2020.6.20 | py37hc8dfbb8_0 151 KB conda-forge
numpy-1.18.5 | py37h8960a57_0 5.1 MB conda-forge
------------------------------------------------------------
Total: 5.4 MB
The following packages will be UPDATED:
ca-certificates 2020.4.5.2-hecda079_0 --> 2020.6.20-hecda079_0
certifi 2020.4.5.2-py37hc8dfbb8_0 --> 2020.6.20-py37hc8dfbb8_0
numpy 1.18.1-py37h8960a57_1 --> 1.18.5-py37h8960a57_0
Downloading and Extracting Packages
certifi-2020.6.20 | 151 KB | ##################################### | 100%
numpy-1.18.5 | 5.1 MB | ##################################### | 100%
ca-certificates-2020 | 145 KB | ##################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
ERROR: osmnx 0.14.1 has requirement geopandas>=0.7, but you'll have geopandas 0.6.3 which is incompatible.
ERROR: hypertools 0.6.2 has requirement scikit-learn<0.22,>=0.19.1, but you'll have scikit-learn 0.23.1 which is incompatible.
ERROR: osmnx 0.14.1 has requirement geopandas>=0.7, but you'll have geopandas 0.6.3 which is incompatible.
ERROR: hypertools 0.6.2 has requirement scikit-learn<0.22,>=0.19.1, but you'll have scikit-learn 0.23.1 which is incompatible.
ERROR: hypertools 0.6.2 has requirement scikit-learn<0.22,>=0.19.1, but you'll have scikit-learn 0.23.1 which is incompatible.
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
import seaborn as sns
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.