This repository includes the final project information for the Machine Learning Laboratory - MSDS 699 course. Our dataset is game play history for the PBS Kids Measure Up! app provided by Kaggle 2019 Data Science Bowl. Using the PBS Kids Measure Up app game play data, we created a pipeline to predict how many attempts it would take an individual to complete their next Assessment.
In this dataset, we are provided with game analytics for the PBS KIDS Measure Up! app. In this app, children navigate a map and complete various levels, which may be activities, video clips, games, or assessments. Each assessment is designed to test a child's comprehension of a certain set of measurement-related skills. There are five assessments: Bird Measurer, Cart Balancer, Cauldron Filler, Chest Sorter, and Mushroom Sorter.
The objective of this project is to predict a users next Assessment outcome using their previous game history. The categorical labels we have in our dataset are defined into 4 groups (labeled as accuracy_group in the data):
3: the assessment was solved on the first attempt
2: the assessment was solved on the second attempt
1: the assessment was solved after 3 or more attempts
0: the assessment was never solved
There are roughly 11 million rows in the training data, with 17,000 unique installation_ids, which can be assumed to map to a single user.
Contains the submission notebook with the data processing and modeling pipeline that will result in writing predictions to a .csv file for the Kaggle 2019 Data Science Bowl Competition.
To get started with the notebook create the conda environment with the include environment.yml file. This will make sure that you have all of the neccessary packages installed. To do this run the following command while in this directory:
conda env create -f environment.yml