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Natural Language Processing

CS224n: Natural Language Processing with Deep Learning

My complete implementation of assignments and projects in CS224n: Natural Language Processing with Deep Learning by Stanford (Winter, 2019).

Neural Machine Translation: An NMT system which translates texts from Spanish to English using a Bidirectional LSTM encoder for the source sentence and a Unidirectional LSTM Decoder with multiplicative attention for the target sentence (GitHub).

Dependency Parsing: A Neural Transition-Based Dependency Parsing system with one-layer MLP (GitHub).

Detect Spam Messages: TF-IDF and Naive Bayes Classifier

In order to predict whether a message is spam, first I vectorized text messages into a format that machine learning algorithms can understand using Bag-of-Word and TF-IDF. Then I trained a machine learning model to learn to discriminate between normal and spam messages. Finally, with the trained model, I classified unlabel messages into normal or spam.

Data Science

Credit Risk Prediction Web App

After my team preprocessed a dataset of 10K credit applications and built machine learning models to predict credit default risk, I built an interactive user interface with Streamlit and hosted the web app on Heroku server.

Kaggle Competition: Predict Ames House Price using Lasso, Ridge, XGBoost and LightGBM

I performed comprehensive EDA to understand important variables, handled missing values, outliers, performed feature engineering, and ensembled machine learning models to predict house prices. My best model had Mean Absolute Error (MAE) of 12293.919, ranking 95/15502, approximately top 0.6% in the Kaggle leaderboard.

Predict Breast Cancer with RF, PCA and SVM using Python

In this project I am going to perform comprehensive EDA on the breast cancer dataset, then transform the data using Principal Components Analysis (PCA) and use Support Vector Machine (SVM) model to predict whether a patient has breast cancer.