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Agriculture Raw Material Prices

For the course project Data Analysis with Python: Zero to Pandas, I decided to choose Kaggle dataset Agriculture Raw Material Prices. The purpose of this analysis is to idenify any existing corelation between different raw material prices and % price change. This is a single csv file with 25 columns.

The course Zero to Pandas covered Python from scratch and even a novice beginner with no programming background can easily understand the basic concepts. The course is well taught by the instructor with real life examples to make the content easier to grasp. It covers all the impostant libraries required for EDA i.e Pandas, Numpy, Matplotlib, Seaborn.

Downloading the Dataset

This dataset was downloaded using Joivan opendatasets Python library and following step-by-step instructions. As this is a Kaggle dataset, we need to follow below instrictions to generate API credentials.

Sign in to https://kaggle.com/, then click on your profile picture on the top right and select "My Account" from the menu.

Scroll down to the "API" section and click "Create New API Token". This will download a file kaggle.json with the following contents:

{"username":"YOUR_KAGGLE_USERNAME","key":"YOUR_KAGGLE_KEY"}

When you run opendatsets.download, you will be asked to enter your username & Kaggle API, which you can get from the file downloaded in step 2.

Note that you need to download the kaggle.json file only once.

!pip install jovian opendatasets --upgrade --quiet

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

# Downloading Data from Kaggle
dataset_url = 'https://www.kaggle.com/kianwee/agricultural-raw-material-prices-19902020?select=agricultural_raw_material.csv'