(Bryan) Zeyang Yu

Jersey City, NJ

ac0f1r@r.postjobfree.com Phone: 484-***-****

LinkedIn: https://www.linkedin.com/in/zeyang-yu-machine-learning/

SUMMARY

Data Scientist with project experience specializing in machine learning, predictive modeling, relational database query, natural

language processing, experimental design, data structure and algorithm using Python (and its libraries Scikit-learn, Pandas, NumPy,

TensorFlow), SQL and VBA. Proven skills in working with large data sets (Hadoop), A/B testing, building convolutional/recurrent neural

network. Proficient in machine learning techniques such as neural network and linear regression and their application in business.

EDUCATION BACKGROUND

Udacity, Deep Learning Nanodegree, Expected June 2017 Online Degree

Udacity, Machine Learning Nanodegree, Expected June 2017 Online Degree

Lehigh University, M.Eng. Industrial and System Engineering, May 2016 (GPA: 3.5/4.0) Bethlehem, PA

Jilin University, B.S. Automotive Engineering, May 2014 (GPA: 3.7/4.0) Changchun, China

SKILLS

Tools: Python (Scikit-learn, Pandas, NumPy, TensorFlow), SQL, VBA, SAS

Technical: Random Forest, Logistic Regression, SVM, PCA, Convolutional/Recurrent Neural Network, Hadoop/MapReduce

PROJECT EXPERIENCE

Apply PCA and K-means Clustering to make customer segmentation, Udacity May 2017

Applied Scikit-learn in Python to make customer segmentation using data on customers' annual spending amounts of diverse

product categories, to help the distributor on how to best structure the delivery service to meet the needs of the customer

Conducted feature scaling, outlier detection, then performed feature transformation (PCA) to reduce the dimension of the

data from six to two

Implemented K-means clustering method on two principle components and separated the customers into three segments

(with silhouette coefficient = 0.412)

Apply Supervised Algorithms to Find Donors for Charity, Udacity April 2017

Implemented different machine learning algorithms (Random Forest, Logistic Regression, Support Vector Machine) with scikit-

learn to predict whether an individual makes more than $50,000 a year

Conducted data preprocessing to make the data cleaned and formatted (one-hot encoding), then trained different models and

evaluated their performance

Selected Logistic Regression as the best model, then performed feature transformation(PCA) and grid search to achieve 85% of

accuracy and 75% of F-score

Bike Sharing Prediction with Neural Network, Udacity March 2017

Built a neural network from scratch to predict daily bike ridership

Implemented gradient descent and backpropagation with NumPy and Pandas to train the neural network

Adjusted the hyperparameters (iterations, learning rate, hidden nodes) and analyzed bias/variance tradeoff to find the optimal

point of minimizing training loss and validation loss while avoiding overfitting, and get the final model with 0.13 MSE

WORK EXPERIENCE

Data Analyst, Avis Budget Group, Inc., Parsippany NJ August 2016 Present

Analyzed demand forecast and Revenue Management optimization model results, monitoring data integrity and results while

evaluating effectiveness, then recommended changes to be made in the optimization model

Applied decision support tools and database queries to business problems in order to access/capture appropriate data,

interpret the results, and built automated report creating process with 80% of time saved

Built web scrapper to collect car rental data and flight data with Python and stored them in the database for future analysis

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