The Austin Animal Center provides its animal intake and outcome datasets on Socrata. When an animal is taken into the shelter, it is given a unique identifier that is also used in the outcomes dataset. We have already investigated and performed exploratory data analysis on the Austin Animal Center's intakes
The Austin Animal Center, the largest no-kill municipal shelter in the United States, makes available its collected data on Austin's Open Data Portal. This data includes both animals incoming into the shelter and the animals' outcome. In this post, we perform some exploratory data analysis on the intakes dataset to see if we can find any noticeable trends or interesting pieces of information of the data. First, we will extract the data from Austin's Data Portal, which is supported by Socrata
The city of Seattle makes available its database of pet licenses issued from 2005 to the beginning of 2017 as part of the city's ongoing Open Data Initiative. This post will explore extracting the data from Seattle's Open Data portal using requests, then transform the extracted JSON data into a workable dataset with pandas
In the previous notebook analysis, we identified several likely candidate features and variables that could be significant in predicting a cat's outcome as it enters the shelter. Using that information and scikit-learn, we can train a machine learning model to predict if a cat will be adopted or transferred to a partner facility. For this first task, we are only interested in the adoption and transfer outcomes to see if our assumptions based on experience and the information we learned from the previous analysis align with predicted results. Adoptions and transfers represent over 90% of all the outcomes in the Austin Animal Center shelter system, therefore focusing on these outcomes and their more specific subtype outcomes and building a model to predict these outcomes is still quite valuable.
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.
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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