d-alvear

120-Data-Science-Interview-Questions

Answers to 120 commonly asked data science interview questions.

Analyzing-Employee-Exit-Surveys

In this project, I cleaned, analyzed, and plotted employee exit survey data to find out whether employees are resigning due to job dissatisfaction, and whether younger employees are more dissatisfied than older employees.

Analyzing-Music-by-Decade

I analyze Billboard number-one singles from each decade (1950s - present) to generate insights about how music has changed over time, and if songs that go number-one have similar attributes.

Analyzing-Star-Wars-Surveys

exploratory data analysis based on survey results

clean-code-ml

:bathtub: Clean Code concepts adapted for machine learning and data science

Coursera_Capstone

if you truly want to know whether NYC is better than San Francisco

discoverify

Discoverify uses signal processing and audio features from the Spotify-API to recommend songs. An offshoot of my Friendshipify app.

flask-friendship-playlist

AKA Friendshipify. This is the repository for my multi-user music recommender that analyzes the music taste of two people, finds the similarities between their taste, and generates a playlist of songs based on that similarity.

NLP-Predicting-Upvotes

I experiment with linear regression and random forests to build a model that can predict, using NLP methods, the number of upvotes a HackerNews article will receive based on the headline.

NYC-vs-SF

A refactoring of my old Coursera project; I wanted to answer, based on neighborhood venue types, whether Manhattan or San Francisco offers more "bang for your buck." I compared rent prices and venues per neighborhood for both cities. I used k-means clustering analysis and leveraged data using the Foursquare API.

d-alvear/flask-friendship-playlist

AKA Friendshipify. This is the repository for my multi-user music recommender that analyzes the music taste of two people, finds the similarities between their taste, and generates a playlist of songs based on that similarity.

d-alvear/Coursera_Capstone

if you truly want to know whether NYC is better than San Francisco

d-alvear/NLP-Predicting-Upvotes

I experiment with linear regression and random forests to build a model that can predict, using NLP methods, the number of upvotes a HackerNews article will receive based on the headline.

d-alvear/NYC-vs-SF

A refactoring of my old Coursera project; I wanted to answer, based on neighborhood venue types, whether Manhattan or San Francisco offers more "bang for your buck." I compared rent prices and venues per neighborhood for both cities. I used k-means clustering analysis and leveraged data using the Foursquare API.

d-alvear/120-Data-Science-Interview-Questions

Answers to 120 commonly asked data science interview questions.

d-alvear/Analyzing-Employee-Exit-Surveys

In this project, I cleaned, analyzed, and plotted employee exit survey data to find out whether employees are resigning due to job dissatisfaction, and whether younger employees are more dissatisfied than older employees.

d-alvear/Analyzing-Music-by-Decade

I analyze Billboard number-one singles from each decade (1950s - present) to generate insights about how music has changed over time, and if songs that go number-one have similar attributes.

d-alvear/Analyzing-Star-Wars-Surveys

exploratory data analysis based on survey results

d-alvear/clean-code-ml

:bathtub: Clean Code concepts adapted for machine learning and data science

d-alvear/discoverify

Discoverify uses signal processing and audio features from the Spotify-API to recommend songs. An offshoot of my Friendshipify app.

d-alvear/course-nlp

A Code-First Introduction to NLP course

d-alvear/ctci-python

working through Cracking the Coding Interview 6th Edition, in Python

d-alvear/d-alvear

d-alvear/database_creation

create SQL database for MLB data

d-alvear/ecommerce-analytics

d-alvear/fitbit-project

d-alvear/HackerNews_Project

I compare which types of HackerNews posts are more popular

d-alvear/ISL-Python

Chapter by Chapter notes, exercises and code for a variety of machine learning books using Python

d-alvear/Modelling-Credit-Risk

I use logistic regression and random forests to build a model that can predict whether or not a borrower will pay off their loan

d-alvear/Optimal-Price-for-AirBnB-Rentals

I explore using the K Nearest Neighbors algorithm with AirBnB data

d-alvear/portfolio_projects

This is my portfolio of data science projects. Descriptions of each project can be found in the README as well as links to their repositories and notebook viewer.

d-alvear/Predicting-Bike-Rentals

I used decision trees and random forests to build a model that predicts the number of bike rentals on a given day

d-alvear/SQL-Business-Analysis-Project

answer business questions using SQL

d-alvear/tensorflow

Repo of notebooks where I followed tensorflow tutorials.

d-alvear/visualizing_earnings_from_major

answer questions about job outcomes of college graduates