• Utilized the Google Maps API to create Heat map figures from position data
• Created animated line graphs and 2D line skeleton in Matplotlib from force and joint position data respectively
• Webscraped thousands of data points for analysis
• Cleaned, scaled, and analyzed data for a variety of different research topics

• Drew conclusions using Scipy statistics
  • Linear Regression
  • Spearman and Pearson correlations
  • Wilcoxon signed-rank test
• Collected data in person while distributing mobility devices

This project, completed in my role as a Data Engineer / Analyst at SixTwentySix, involves the design and development of a dynamic web application that aids in enhanced budget management and cost savings. The application, hosted on AWS, automates the extraction of budget data from past projects stored in Dropbox using its API, transforms the disparate data into a uniform format, and visualizes it in a user-friendly, interactive Google Looker dashboard.

This real-time, auto-updating dashboard provides easy analysis and decision-making tools for users by making the most current data available at all times. I built this solution to identify potential areas for cost savings and facilitate more accurate budget estimations for future projects. This project showcases the integration of multiple data services (Dropbox API and Google Looker) for efficient, automated data handling and visualization.

I developed a Flask API hosted on AWS Elastic Beanstalk integrated with AWS S3 buckets, YouTube Data API, and Google Sign-In API. This API facilitates communication between a livestream and an application for user appearance customization. Using Flask, I built robust endpoints for authentication, data retrieval, and updates. AWS S3 ensures secure storage and retrieval of user data. YouTube Data API retrieves channel information, and Google Sign-In API enables secure authentication. The API is hosted on AWS Elastic Beanstalk for scalability and reliable performance. This project showcases my expertise in Flask, AWS services, API integration, and secure data management.

I developed machine learning models to predict sentiment about various products on Twitter, enabling businesses to quickly and accurately analyze social media data and gain valuable insights. I combined and filtered two datasets, cleaned and processed the data, and engineered features to improve model performance. I trained and evaluated binary and multiclass classification models using accuracy, precision, recall, and macro-averaged F1 score as evaluation metrics. Using the sentiment analysis models, I identified areas for improvement, responded to customer feedback, informed product development, and monitored competitors. The project demonstrated the value of sentiment analysis for businesses looking to make data-driven decisions based on customer feedback.

This project uses machine learning to predict the likelihood of readmission within 30 days of initial discharge for diabetes patients. By analyzing admission, demographic, clinical, medication, and discharge data, the model identifies high-risk patients and provides targeted interventions to reduce the likelihood of readmission. The project demonstrates the potential of data science and machine learning in healthcare, and contributes to efforts to improve patient outcomes and reduce healthcare costs.

The goal of this project was to develop a machine learning classification model to predict whether a user's activity on a company website would lead to a purchase. To achieve this, I utilized several ensemble tree models, including XGBoost and Light GBM, on a dataset of 6165 user sessions. Through preprocessing, hyperparameter tuning, and model optimization, I achieved an accuracy of 93.76% with good precision. Deploying this model would enable companies to better target their marketing, optimize their website, and incorporate dynamic pricing in order to boost revenue.

Python 3 | Pandas, SKLearn, Keras, Tensorflow, Scipy, Matplotlib, Numpy, OpenCV (cv2)

Graphic Design | Affinity Designer and Affinity Photo

Godot / GDScript

Spanish | Conversational