Data Scientist
2019 - PRESENTEricsson- Developed an algorithm for geolocalization and size estimation of street objects.
- Prevented cybersecurity attacks using anomaly detection algorithms, including isolation forest and robust autoencoders.
- Developed object detection/localization using DenseNet and YOLO.
- Developed a proprietary algorithm for geolocalization and size estimation of street objects.
- Mentored junior data scientists.
Technologies: Computer Vision, Scikit-learn, TensorFlow, KerasData Scientist
2018 - 2018PayPal- Predicted customer churn through machine learning.
- Led label inference and semi-supervised machine learning in order to determine customer presence.
- Improved customer conversion by predicting merchant attribute.
Technologies: Scikit-learnResearch Scientist II
2012 - 2017Eureka Therapeutics- Designed and executed experiments to understand the effects of variables on the system.
- Generated and evaluated biophysical data based on purity, stability, binding, and specificity.
- Used artificial neural network package, NETMHC, to predict the existence of peptide drug targets.
Technologies: Python
Neal Cheng
Machine Learning Developer in Austin, TX, United States
Member since August 5, 2019
Neal has a professional track record of success over the past decade, working with various clients. For example, he's improved monthly item sales by 10% to 40% by implementing a machine learning model to predict customer demand. Neal is looking forward to helping more clients achieve their goals through the use of data science and technology.
Neal is now available for hire
Portfolio
- EricssonComputer Vision, Scikit-learn, TensorFlow, Keras
- PayPalScikit-learn
- Eureka TherapeuticsPython
Experience
- Python 7 years
- Pandas 3 years
- Machine Learning 3 years
- Scikit-learn 3 years
- SQL 2 years
- Deep Learning 2 years
- Keras 2 years
Availability
Part-time
Preferred Environment
MacOS, Jupyter Notebook, Docker, Scikit-learn, Keras, Python, Kubernetes
The most amazing...
...project I've worked on involved turning project visual information into geospatial coordinates and then triangulating to obtain physical object locations.
Employment
Experience
- Predicting the Outcome of Cold-callinghttps://github.com/nneal1213/Data_Science_Projects/tree/master/01_Cold_Calling
Created a gradient-boosted tree model that predicted the outcomes of cold-calling based on customer attributes. The model offered a 150% improvement.
- Modeling Yelp Reviews Through NLPhttps://github.com/nneal1213/Data_Science_Projects/blob/master/06_Predicting_YelpRating_from_Text/06_Predicting_YelpRating_from_Text.ipynb
Through the correct use of data structures, it was possible to parse over 4GB of data to extract text and ratings. Afterward, through the use of natural language processing (NLP) and SGDRegressor, it was possible to predict the rating of user reviews through semantic language. A data pipeline was created for the transformation and model fitting of the data.
- Use of Deep Learning to Enhance the Accuracy of Real Estate Predictionshttps://github.com/nneal1213/Data_Science_Projects/tree/master/03_Price_Prediction_Deep_Learning
VGG-16, a convolutional neural network model, was adapted and trained upon ~20,000 images to better predict real estate listing prices. This initial study increased the explained variance metric by 16%, demonstrating its viability as a proof of concept.
Skills
Languages
Python, SQL, ScalaLibraries/APIs
Scikit-learn, Pandas, Keras, PyTorch, TensorFlowOther
Machine Learning, Data Visualization, Deep Learning, Computer VisionParadigms
Object-oriented Programming (OOP)Platforms
Jupyter Notebook, MacOS, Docker, KubernetesStorage
Apache Hive
Education
- Ph.D. in Chemistry2005 - 2012University of California Davis - Davis, CA
- Bachelor of Science Degree in Chemistry2000 - 2005University of California Davis - Davis, CA
- Bachelor of Science Degree in Physics2000 - 2005University of California Davis - Davis, CA
