crebollarramirez,Christopher Rebollar-Ramirez,github

I am currently studying Math & Computer Science at University of California - San Diego, I am also double majoring in Cognative Science with a Specialization in Machine Learning and Neural Computation.

Math & Computer Science | Cognitive Sciece with a Specialization in Machine Learning and Neural Computation:

I decided to double major because of my passion in Computer Science and how the human brain works. I am intrigued by the comparison between how the human brain computes and computer computation. My academic journey, with a focus on Math-Computer Science, has equipped me with a robust foundation in both the theoretical and practical aspects of computer science. This, combined with Cognitive Science, places me at the intersection of analytical thinking, computational expertise, and an in-depth understanding of how machine learning algorithms work.

🔭 I’m currently working on Full-Stack Development.
🌱 I’m currently learning how to Front-end development.
👯 I’m looking to collaborate on anything related to backend development or data analysis projects.
📫 How to reach me: [email protected]

Check out my portfolio! --> https://crebollarramirez.github.io/crebollarramirez/

Christopher Rebollar-Ramirez's Projects

aipingpong

using neural network to make a ping pong bot to play against

flight-price-prediction

Using LInear Regression, we discovered the contributing factors to flight prices

instagram-python-script

This script shows you who does not follow you back on Instagram.

localization-data-using-sql

This project includes data from a hypothetical free-to-play mobile game. The task is to use the data set given to answer questions. I used python + sqlite3 to retreive and analyze information from a data base.

nlp-movie-reviews

Using Natural Language Processing, I created a model that determines if a movie review is positive or negative.

parking-ticket-tracker

Parking ticket program that tracks parking tickets for vehicles.

sales-forecasting-next-5-years

This project employs CatBoost and UpGenie in Python for accurate sales predictions across ten stores, emphasizing meticulous data preparation, feature enrichment, and achieving a substantial error reduction using Mean Absolute Percentage Error (MAPE) as a key metric.