Ivan E Sepulveda's repositories
job_postings_webscraper
Scrapes listings of lever.co and greenhouse.io for relevant open positions using keywords
algorithms
Djjkstra's, Binary Search, BFS, DFS, etc.
daily_coding_problem
Solving problems by Ales Miller and Lawrence Wu. First I try my own solution, but if theirs is better, I try to optimize it. Even if there's no room for further optimization, I make sure I understand their solution as is and walk through the code.
did-stock-beat-s-and-p
Using various classification methods, I sought to determine the most relevant variables in a stock’s performance; securities were evaluated in a binary manner to reflect if they had surpassed the Standard and Poor 500’s (S&P-500) performance for the equivalent time period.
ivan-sepulveda.github.io
Website portfolio of all my computer science, data science, finance, and physics accomplishments. Built using open source tools.
predict-ad-click
Final accuracy: 97.1% The goal of the project was to predict who is likely going to click on the Ad on a website based on the features of a user. Following are the features involved in this dataset which is obtained from Kaggle.
find-craigslist-python-gigs
I noticed that I was spending 5-10 minutes a day searching for Python related gigs on Craigslist across multiple cities. I also wanted to figure out AWS. Behold Python + Lambda + REST API + S3 with a pleasant user experience.
german-credit
In this project, customers' demographic and behavioral data is available and the aim is to predict the probability of default. The target variable is "class" which takes value as "Good" and "Bad". The aim is to build a credit risk scorecard (just a set of probability values) based on the sample data provided.
mit-0002f16
Coursework from John Guttag's MIT Course: Introduction To Computational Thinking And Data Science
mollys-notebooks
Notebooks from lessons with Molly McGuire
nlp-restaurant-reviews
97.1% Accuracy
prac-stats-4-data-scientists
Practicing Use cases from O'Reilly Book: Practical Statistics for Data Scientists
solutions-to-quantitative-questions
I answer questions from Heard on the Street: Quantitative Questions from Wall Street Job Interviews by Timothy Crack visually and mathematically.
SupernovaeClassification
CNN adapted to time series for the Supernovae classification
technical-challenge
Technical challenge I completed for a bank recently. Questions and solutions uploaded with their permission.
titanic-survivors
For this class exercise, we used SVM to predict whether or not a passenger on the titanic survived based on features such as gender, age, children aboard, fare, etc. The model reached 78.58% accuracy.