My name is Luis and I am a mathematician who utilize logical programming to extract meaningful insights from data using appealing visualizations, statistical techniques, and machine learning. I love to create visualizations using several python packages such as Pandas, Matplotlib, Plotly and more. I also utilize Power Bi to make dashboard reports with essential KPI's and clean data using Power Query Editor. Some of Statistical techniques I commonly use are correlation analysis, t-test, chi-square, anova/manova, and many more. I know how to setup up proper experimentation setups and utilize A/B testing to check results. My strong foundation in Mathematics allows me to fully grasp the process of machine learning with my Linear Algebra, Multivariable calculus, Optimization and Probability to ensure the best models with the correct assumptions. I love to learn about data and I keep up with the latest news by reading the latest research papers in machine learning, Medium for up to date data techniques, and follow the leaders in Artificial Intelligence.
In the future I see myself sharing my knowledge and helping others learn. I see myself creating full tutorials on projects that relate to Data Science and Data Engineering on my GitHub. I plan to start writing blogs on Medium and uploading more work onto Kaggle. I hope to inspire new comers into the world of Data by taking a simplistic approach in my methods that will motivate them to grow.
-
Airline Passenger Survey Analysis
An airline brand was receiving a fair amount of unwanted sentiment towards their services. Taking passenger satisfaction surveys, I used Machine Learning Algorithms such as Logistic Regression, Random Forest, and Gradient Boosting to pin point the root cause driving these sentiments. Our Random Forest Classifier was able to accurately predict the passenger sentiment by over 93% and leveraged the feature importance along the Logistic Regression slope parameters to find the key services impacting sentiment in a positive way. A research paper was written detailing the full analysis and how specific services can be improved to increase passenger sentiment. Overall, the analysis provided the airline a 15% increase in customer retention which improved the overall branding. Full Project.
-
Los Angeles Crime Analysis
Collect Data from LACITY website and uploaded to a AWS Database using Planet Scale. Performed Data Cleansing and Explored Key Metrics using MySQL. The motivation for this project was to investigate how Covid and certain policy in California regarding to crime has affected the city. My visualizations were created using Python by using libraries Matplotlib, Seaborn and connecting to the database using SQLAlchemy. My business intelligence tool created using Power BI made it easy for the user to visualize specific crime spots and choose filter likings. In the end, my analysis demonstrated that a change in a particular policy Prop. 47 in California should be considered and inspires action by the people. Full Project.
-
Credit Card Fraud Detection
The goal for this project was to reduce unnecessary costs for a financial institution by not only identifying fraudulent transactions at the expense of not misclassifying correct transactions which can lead to more costs. I leveraged certain metrics using specific machine learning algorithms to figure out an adequate probability of classification to enhance the existing policy. The final model was able to effectively identify fraudulent transaction and reduced misclassifications by 30%. At the end I built a web application where given certain attributes it identifies the transaction type while explaining how the attributes contributed to the final decision. I built the app using Python with Gradio for designing the application and pulling a hidden model stored in Amazon Web Service (AWS) Simple Storage Solution (S3) by calling API keys. I then deployed the application onto the cloud using Hugging Face. Full Project.
-
Microsoft Stock Analysis
A personal project where I use deep learning algorithms such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) to try to predict the stock's closing price. A regression problem where I use metrics such as Mean Absolute Error, Mean Squared error, and Root Mean Squared Error to check the validity of the model. I also showcase my passion in macro-economics by using a Power BI dashboard to tell a compelling story that relates Microsoft stock price to tech economic events. Full Project.
-
Fake News Prediction System
In the age of advanced technology especially in the realm of Artificial Intelligence it has become rather difficult to tell whether a news article contains fake news or not. The danger of fake news can manipulate people's perception of reality, influence politics, and promote false advertising. The goal for this project was to predict whether a news article was fake news or not using natural language processing. Some of the steps I took was using Regex to remove unwanted characters and then convert the text into mathematical symbols so that our algorithm can recognize a pattern. The machine learning algorithm used was Term Frequency-Inverse Document Frequency which capture the importance of a token relative to the entire document. The model was able to predict a news article by over 96% and built a web app using streamlit for users to test out. Full Project.
