Zi Xin Lee

West Nile virus prediction

This was the 4th project and the only group project done as part of my General Assembly Data Science Immersive coursework. We were tasked to predict the presence of West Nile virus in mosquitos, which is an infectious mosquito-borne disease that flares up in summer months.

While this is also a classification problem like the 3rd project, we experimented with many more classification algorithms here, specifically Decision Tree and Boosting algorithms.

Models tried:

• Logistic regression
• Support vector machine
• Bagged decision trees
• Extra trees
• Random forest
• AdaBoost
• Gradient boosting
• XGBoost

Housing price prediction

My 2nd project at General Assembly, which allowed me to really properly dip my toes into machine learning for the first time.

In this project, I developed a regularized regression model for predicting housing sale prices. The dataset had more than 80 features (many of which were categorical), which provided interesting opportunities for enhancing the model performance through feature engineering and feature selection.

Through this project, I learnt a ton about feature engineering, feature selection, regularised linear models and hyperparameter tuning, which I continued to apply in subsequent projects.

Subreddit classification

My 3rd project for the General Assembly Data Science course, which involved data collection by calling an API and performing natural language processing on the text data.

In this project, I scraped posts from 2 similar subreddits (r/AskWomen vs. r/AskWomenOver30) and examined whether machine learning models would be able to classify them accurately. I found that the best model had a decent accuracy of 80%; the top predictive words for each subreddit proved to be a little unanticipated but gave us some insight into the topics discussed there.

Models tried:

• Logistic regression
• Multinomial Naive Bayes
• Support Vector Classifier

SAT & ACT analysis

My 1st project at General Assembly.

Here, I analysed the 2017 and 2018 ACT and SAT participation and scoring data to glean insights that the College Board can use to decide which U.S. state to target to increase SAT participation rates.

This project focuses more on exploring our data through extensive data visualisation and supplementing our analysis with external qualitative research.

Personalised recommendations to increase AOV of Instacart loyalists

My final capstone project at General Assembly.

For this project, I chose to explore recommender systems. As a digital native who shops online shopping fairly often, I have been introduced to many new and interesting products –— and spent more than I intended –— thanks to personalised recommendations. Thus, it was of great interest to me to understand the algorithms powering these recommendation systems and try my hand at building one myself.

In addition, I used an unsupervised machine learning algorithm (K-means clustering) to segment the customers based on their past transaction behaviour.

Types of recommender systems tested:

• Content-based filtering
• User-based collaborative filtering
• Item-based collaborative filtering
• Matrix factorization (SVD) model for collaborative filtering