Analytical, problem-solver, and highly adaptable. Jeddah leverages her proficiency in Data Science, IT, and Mathematics, to provide businesses with data-driven solutions and deliver valuable and actionable insights.
Jeddah graduated with an MSc in Data Science (MSDS) degree at the Asian Institute of Management (AIM). She has developed several data science projects with her most recent work involving a decision support tool for predicting healthcare utilization that aims to optimize resource planning and provide better health outcomes.
She served as a Data Science Fellowship Mentor at Eskwelabs and prior to doing her master's, she worked as a software engineer and ETL consultant for select top firms in the IT industry for more than 5 years.
Dog Breed Quiz Web Application Using VGG16 Neural Network
I created a Dog Breed Quiz web app where you compete with an AI that uses VGG16 convolutional neural network in determining the breed of a dog. The project was developed in Flask, using only 2 HTML files, 2 CSS, and 2 JS files.
Tags: data apps, web app, deployment, VGG16, Flask, HTML, CSS, JS
Hybrid NLP Models for Sentiment Prediction
In this project, we created an algorithm predicting employee sentiment using hybrid of NLP (topic modelling and word embedding) and neural networks based on Philippine-based employee review text data scraped from Glassdoor.com. With the models we've developed, we are able to give insights on what pushed these ratings and provide organizations a new way of better understanding their employees.
Tags: HR analytics, NLP, topic modelling, LDA, word embedding, neural networks, Stacked GRU, deep learning
Frequent Itemset Mining (FIM) and Association Rule Mining (ARM) Analysis on Big Data
Our team conducted an analysis on the posts from the Philippine-based subreddit r/phr4r to paint a clearer picture of the dating and hook-up culture in the Philippines. Frequent Itemset Mining (FIM) and Association Rule Mining (ARM) was employed to extract the most frequently occurring verbs, adjectives, and nouns from individual posts. Data points were identified according to gender, age, and sexual orientation due to the consistent topic name formatting imposed in the subreddit.
Tags: big data, dask, cloud computing, AWS, FIM, ARM, spark, NLP
Clustering Player Styles in DOTA2 Using Unsupervised ML
This study utilizes the unsupervised machine learning algorithm KMeans Clustering to uncover different "styles" of play from data collected from all 317 verified tournament players from Dotabuff.
Tags: unsupervised clustering, kmeans, PCA
Text Generation Using Recurrent Neural Networks
One of the great things I've realized through our MSDS journey is not only the ability to ask the right questions and maximize the business value of the problems we solve, but also the ability to incorporate our learnings to our hobbies and passion. For this study, I aim to apply the learnings from our Machine Learning 2.0 class to one of my passions, music, by generating OPM lyrics using one type of recurrent neural network—LSTM.
Tags: text generation, RNN, LSTM
A Machine Learning Approach to Movie Revenue Prediction
Using the machine learning model XGBoost Classifier, we were able to determine whether or not a given movie will make money in the box office prior to its release date using data scraped from Box Office Mojo.
Tags: machine learning, classification, XGBoost
Bailiwicks Bipartite Network Analysis
This study aims to explore a new way to quantify the strength of political strongholds and use eigenvector and betweenness centrality measures of bipartite politician-city network graphs of Ilocos and NCR to determine the most influential figures in an area of political jurisdiction for potential alliance formation.
Tags: network science, bipartite, projected graph, eigenvector centrality, betweenness centrality, subgraph, networkx, gephi
Asian Institute of Management
MS in Data Science Batch 2020
