Pet adoption and reduce euthanasia are two of the most crucial tasks for not only Word Animal Associations. With enjoyable moments owners can be obtained from owning pets, when it is necessary to relocate or having financial problems, do not relinquish the pet would be the best choice. However, as there are so many pets being abandoned, analyzing the features, which can help them find new homes are the first task. This capstone project developing data mining, visualization and machine learning on NLP attempt to find some characters of pets can increase their possibilities to be adopted. The accuracy results from NLP are low, and clearly, more data needs to be implemented in order to get better accuracy. However, from the visualization analysis, a few outcomes appear. The physical characteristics of age scaled in months, cat or dog, colors of pet, gender, maturity size, fur length, any injury, a specific breed of cats or dog, have impacts on adoption speed. Continued trend analysis with implementing another classification and improving natural language processing has the potential to provide some other support to increasing pet adoption possibilities.