1. Exploratory Data Analysis of Shelter Cat Outcomes with Pandas and Seaborn

    In this step, we visualize the data we extracted from the AAC database with the additional features that were added to the data in the previous notebook. The visualization of the outcomes and variables of which we have an interest will help us better understand the data and how the variables relate to each other. This knowledge will be crucial when selecting which variables we should focus on and include in our prediction model during the model building phase.

  2. Extraction and Feature Engineering of Animal Austin Center's Shelter Outcomes Dataset using Requests and Pandas

    The Austin Animal Center is the largest no-kill animal shelter and shelters and protects over 18,000 animals each year. As part of the City of Austin's Open Data Initiative, the Center makes available their data detailing shelter pet intake and outcomes. According to the data portal, over 90% of animal outcomes are adoptions, transfers to other shelter partners or returning lost pets to owners.

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