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Insurance cost prediction using linear regression

Make a submisson here: https://jovian.ai/learn/deep-learning-with-pytorch-zero-to-gans/assignment/assignment-2-train-your-first-model

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:

  1. Download and explore the dataset
  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
# !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
pip install jovian
Requirement already satisfied: jovian in /usr/local/lib/python3.7/dist-packages (0.2.41) Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from jovian) (7.1.2) Requirement already satisfied: uuid in /usr/local/lib/python3.7/dist-packages (from jovian) (1.30) Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from jovian) (3.13) Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from jovian) (2.23.0) Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->jovian) (1.24.3) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->jovian) (2021.5.30) Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->jovian) (2.10) Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->jovian) (3.0.4)
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-assignment' # will be used by jovian.commit