我们当中有多少人喜欢动物，并将我们的宠物视为家庭的一部分？好吧，如果我告诉您，在每年进入救援庇护所的6-8万只动物中，将近3-4万（即50％的即将到来的动物）被安乐死了。更令人心碎的是，有10％ - 25％的人被判处死刑，特别是因为庇护所每年人满为患。

The problem of overpopulation of domestic animals continues to rise, leaving shelters faced with the challenge of how to increase adoption rates. Though animal shelters provide incentives such as reduced adoption fees and sterilizing animals before adoption, only a quarter of total animals living in the shelter are adopted.

在每年进入救援庇护所的6-8万只动物中，将近3-400万只被安乐死

These staggering statistics led us to investigate the length of stay of animals at shelters and the factors influencing the rate of animal adoption. The overarching goal of this study was to use these factors to predict and then minimize how long an animal will stay in a shelter, thereby decreasing the number of animals euthanized due to overcrowding. Several steps must be conducted to accomplish this goal, such as a literature search for the factors, collection of data from databases and animal shelters, and utilizing machine learning algorithms on this data to make predictions on length of stay in the shelters.

To answer the question of what factors influence the length of stay, a thorough literature review was conducted. Several factors were found to influence the length of stay including color, gender, breed, animal type, and age. To make the predictions about the length of stay using these factors, we evaluated using machine learning algorithms and predictive analytics.

Machine learning is just the ability to program computers to learn and improve by itself using training experience. The developed system needs to analyze big data, quickly deliver accurate and repeatable results, and adapt to new data. A system can be trained to make accurate predictions by learning from examples of desired input-output data. In other words, we wanted to utilize a labeled data set with the output (length of stay) already known, so that the computer could learn from it. The next step was to obtain this data from databases and animal shelters across the country.

从数据库和动物收容所收集的数据包括动物类型，摄入量和结果日期，性别，颜色，品种以及摄入量和结果状态（动物进入庇护所和结果类型的动物行为的行为）。这些数据集包括来自南部和西南州的信息。在住宿的时间内，类别包括“低”，“中”，“高”和“非常高”（安乐死）。收集和清洁数据后，是时候将其输入机器学习算法了。

There are so many different types of algorithms that can be used on a data set to make predictions. The hard part is determining which algorithm will perform the best on the given data set, as the performance of the models depends on the application. Simple classification algorithms such as logistic regression, artificial neural network, gradient boosting, and random forest were used in this study.

在检查结果时，最熟练的预测模型是由该数据集的梯度增强算法开发的，其次是随机森林算法。逻辑回归算法似乎具有所有住院时间类别的最差性能指标。有趣的是，当预测非常高的住院时间或结果为70-80％时，梯度的提升和随机森林算法表现良好。

从上面的探索性数据中查看结果，观察到，随着年龄的增长，狗留在庇护所的天数减少。这不是预期的，因为可以预测，庇护所的天数对于年轻的狗和幼犬的数量将较低。该观察结果可能是由于年轻狗的数据点更多。

结果表明，年龄，大小和颜色对住院时间的影响有重大影响或影响。

该研究的另一个有趣的结果是每种机器学习算法的主要特征或因素。结果表明，年龄（高级，超级和小狗），大小（大小）以及颜色（多色）对住院时间有重大影响或影响。

对于未来的研究，将采用规范性分析方法。我们的目标不仅是提高动物收容所中宠物的采用率，而且还确定动物避难所最少的动物庇护所位置，将其保留在庇护所中，并且很可能会被采用。