This repository is the PUBLIC repo for students of the 2018 MIT BWSI Medlytics course for WEEK 1. Most notebooks do not include solutions, and instead has [# Your code here] where students should write their own code.
This repository does not allow pull requests so we recommend you fork this repository and work on your own copy.
Datasets Used in this Repo:
PIMA Indians diabetes dataset: https://www.kaggle.com/uciml/pima-indians-diabetes-database/version/1 Depression Dataset from UFHealth Biostatistics Open Learning Textbook: http://bolt.mph.ufl.edu/2012/08/02/learn-by-doing-exploring-a-dataset/ Hypothyroidism Data from UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/datasets/thyroid+disease
- Contains jupyter notebook lessons along with solutions notebooks on Intro to Classification (Danelle Shah), Decision Trees (John Passarelli), SVMs (John Passarelli), Intro to Using Colab with Github (Thomas Possidente), Nearest Neighbors Algorithm (Thomas Possidente and Lyle Lalunio), Pandas and Matplotlib Intro (John Passarelli), and Propensity Score Matching (Thomas Possidente).
- Contains the hypothyroidism challenge for the end of week 1. Students have to classify whether a patient has hypothyroidism or not given tabular dataset. Creator: Lyle Lalunio