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ads_teaching

Teaching repo for Applied Data Science @ Columbia, a project-based course for data science skills (statistical thinking, machine learning, data engineering, team work, presentation, endurance of frustration, etc).

baseball_analysis

This study aims to construct a classification model for the prediction of award-winning players in order to reveal some potential hidden future baseball stars from a large pool of players. In addition, this study creates a career peak prediction model for the team managers to apply during player selection process in order to predict whether the players have passed their career peak. Furthermore, the study proposes a salary prediction model for the players to evaluate their current contracts on whether they are being underpaid. Lastly, the study performs unsupervised machine learning techniques in categorizing different pitchers. All models result in promising and accurate performances.

collaborative-filtering

In this project, we use memory-based algorithm and model-based algorithm to do collaborative filtering.

face-and-object-detection

In this project, we aim to construct a face detection model that can accurately detect and count the faces on both images and WebCam. We used a method haar to extract features. After that, by applying extracted features to cascade method, we are able to detect people's faces and also count the number of faces through pictures as well as webcam. Finally, we implement the contemporary model which can distinguish people as well as objects with confidence value through pictures and real-time webcam.

image-classification

In this project, we improved a classification baseline model for images of dogs, fried chickens and blueberry muffins in terms of ruing time cost and prediction accuracy. The baseline model for comparison uses Gradient Boosting Machine (GBM) with decision stumps on 2000 SIFT features. Our group uses 3 image feature selection models, including SIFT, RGB, LBP and the arbitrary combinations of them, and 8 classification models, including GBM, SVM, XgBoost, Random Forest, Neural Network, AdaBoost, Logistic Regression and Classification Trees.

leetcode

lemmatization-lists

Machine-readable lists of lemma-token pairs in 23 languages.

presidential-inaugural-speech-analysis

What did the presidents say at their inauguation?

rshiny-alumni-employment

Open Data App using RShiny - Where did our alumni go?