Data Analysis of Indian Foods

This notebook analyses a data-set taken from the opendatasets on the Indian foods and tries to clean it and answer some interesting questions using it like the average cooking time, flavour profiles of the different regions in the country etc.. This is a project done as a part of the course Data Analysis with Python: Zero to Pandas.

How to run the code

This is an executable Jupyter notebook hosted on Jovian.ml, a platform for sharing data science projects. You can run and experiment with the code in a couple of ways: using free online resources (recommended) or on your own computer.

Option 1: Running using free online resources (1-click, recommended)

The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. You can also select "Run on Colab" or "Run on Kaggle".

Option 2: Running on your computer locally

1. Install Conda by following these instructions. Add Conda binaries to your system PATH, so you can use the conda command on your terminal.

2. Create a Conda environment and install the required libraries by running these commands on the terminal:

conda create -n zerotopandas -y python=3.8 
conda activate zerotopandas
pip install jovian jupyter numpy pandas matplotlib seaborn opendatasets --upgrade

3. Press the "Clone" button above to copy the command for downloading the notebook, and run it on the terminal. This will create a new directory and download the notebook. The command will look something like this:

jovian clone notebook-owner/notebook-id

4. Enter the newly created directory using cd directory-name and start the Jupyter notebook.

jupyter notebook

You can now access Jupyter's web interface by clicking the link that shows up on the terminal or by visiting http://localhost:8888 on your browser. Click on the notebook file (it has a .ipynb extension) to open it.

Downloading the Dataset

The dataset is taken from the page: https://www.kaggle.com/datasets?fileType=csv

It is a CSV format file and can be used to download using opendatasets Python library. One time creation of the username and the key from http://bit.ly/kaggle-creds is enough for the usage of their data any number of times.

Let's begin by downloading the data, and listing the files within the dataset.