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# Jovian Commit Essentials
# Please retain and execute this cell without modifying the contents for `jovian.commit` to work
!pip install jovian --upgrade -q
import jovian
jovian.utils.colab.set_colab_file_id('eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE2MDY0MDI5NTQsIm5iZiI6MTYwNjQwMjk1NCwianRpIjoiYjczMWMzNDUtNDg5MC00M2VlLTk4YTgtOGU3MTk0NTk0MzcwIiwiZXhwIjoxNjEwMjkwOTU0LCJpZGVudGl0eSI6eyJpZCI6Mzg1NTMsInVzZXJuYW1lIjoiYWhtZWQtb3VsYWRhbWFyYSJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.zSmLiIQyHX3bmzU-QyBLE2cxk4O8Xido8Ov7fDZaH60')

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
Looking in links: https://download.pytorch.org/whl/torch_stable.html Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (1.18.5) Requirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (3.2.1) Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (1.1.5) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib) (2.4.7) Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib) (0.10.0) Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib) (2.8.1) Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (1.18.5) Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib) (1.2.0) Requirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.7/site-packages (from pandas) (2019.3) Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib) (2.8.1) Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (1.18.5) Collecting torch==1.7.0+cpu Downloading https://download.pytorch.org/whl/cpu/torch-1.7.0%2Bcpu-cp37-cp37m-linux_x86_64.whl (159.3 MB) |████████████████████████████████| 159.3 MB 3.7 kB/s eta 0:00:01 Requirement already satisfied: future in /opt/conda/lib/python3.7/site-packages (from torch==1.7.0+cpu) (0.18.2) Requirement already satisfied: dataclasses in /opt/conda/lib/python3.7/site-packages (from torch==1.7.0+cpu) (0.6) Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch==1.7.0+cpu) (3.7.4.1) Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (1.18.5) Collecting torchaudio==0.7.0 Downloading torchaudio-0.7.0-cp37-cp37m-manylinux1_x86_64.whl (7.6 MB) |████████████████████████████████| 7.6 MB 12.0 MB/s eta 0:00:01 Collecting torchvision==0.8.1+cpu Downloading https://download.pytorch.org/whl/cpu/torchvision-0.8.1%2Bcpu-cp37-cp37m-linux_x86_64.whl (11.8 MB) |████████████████████████████████| 11.8 MB 64.2 MB/s eta 0:00:01 Requirement already satisfied: pillow>=4.1.1 in /opt/conda/lib/python3.7/site-packages (from torchvision==0.8.1+cpu) (8.0.1) Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (1.18.5) Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from cycler>=0.10->matplotlib) (1.14.0) Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from cycler>=0.10->matplotlib) (1.14.0) Installing collected packages: torch, torchvision, torchaudio Attempting uninstall: torch Found existing installation: torch 1.7.0 Uninstalling torch-1.7.0: Successfully uninstalled torch-1.7.0 Attempting uninstall: torchvision Found existing installation: torchvision 0.8.1 Uninstalling torchvision-0.8.1: Successfully uninstalled torchvision-0.8.1 Attempting uninstall: torchaudio Found existing installation: torchaudio 0.7.0a0+ac17b64 Uninstalling torchaudio-0.7.0a0+ac17b64: Successfully uninstalled torchaudio-0.7.0a0+ac17b64 Successfully installed torch-1.7.0+cpu torchaudio-0.7.0 torchvision-0.8.1+cpu 
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' # will be used by jovian.commit