The goal of this project is to wrangle WeRateDogs Twitter data to create interesting and trustworthy analyses and visualizations.

The data for this project involved three datasets, they were gathered as follows:

• The WeRateDogs Twitter archive ('twitter-archive-enhanced.csv' file): this dataset was provided by Udacity and was downloaded manually via the provided download link.

• The tweet image predictions ('image_predictions.tsv' file): this dataset contains the results obtained from running every image in the WeRateDogs twitter archive through a neural network for the purpose of classifying different breeds of dogs. This file was downloaded programmatically using python's request library and the provided URL from Udacity.

• Additional data from the Twitter API: for this file, using the 'tweet_id' in the WeRateDogs Twitter archive dataset, I successfully queried and downloaded the 'favorite_count' and 'retweet_count' for each 'tweet_id' using Python's tweepy library from Twitter's API.

The dataset was assessed to identify several quality and tidiness issues with each issue documented.

The quality and tidiness issue documented were cleaned using the define-code-test framework

The cleaned master dataset was stored in a CSV file called 'twitter_archive_master.csv'

The master dataset was analyzed with visualizations made from the insights gained from within.