The dataset to be analysed in this project is PetFinder_dataset.csv, containing selected and modified data from the following competition Kaggle competition: PetFinder.my Adoption Prediction.

PetFinder.my has been Malaysia’s leading animal welfare platform since 2008, with a database of more than 150,000 animals. PetFinder collaborates closely with animal lovers, media, corporations, and global organizations to improve animal welfare.

In this project, described in detail in AA_202425_Project.ipynb (and its html version AA_202425_Project.html), your team is supposed to check how far it can go in predicting and understanding PetFinder.my adoptions.

You should use both supervised and unsupervised learning to tackle 2 machine learning tasks:

Task 1 (Supervised Learning) - Predicting Adoption and Adoption Speed Task 2 (Unsupervised Learning) - Charactering Pets and their Adoption Speed The project's solution should be uploaded in Moodle before the end of December, 22nd 2024 (last day before Christmas holidays).

Teams should upload a .zip file containing all the files necessary for project evaluation. Teams should be registered in Moodle and the zip file, upload by one of the group members, should be identified as AA202425nn.zip where nn is the group number.

It is mandatory to produce a Jupyter notebook containing code and text/images/tables/etc describing the solution and the results. Projects not delivered in this format will not be graded. You can use AA_202425_Project.ipynb as template. In your .zip folder you should also include an HTML version of your notebook with all the outputs.