Made at Metis

What To Drink Next? A Simple Beer Recommendation System using Collaborative Filtering

Medford Xie

As a self-proclaimed beer enthusiast, Medford Xie routinely found himself looking for new brews to try – but he dreaded the possibility of disappointment once actually experiencing the first sips. This often led to purchase-paralysis. For his final project at Metis, he set out " to utilize machine learning and readily available data to create a beer recommendation engine that can curate a customized list of recommendations in milliseconds."

Finding Success on Twitch

Natasha Borders

In this blog post, Natasha Borders walks you through how she built a streamer recommender for Twitch (live streaming platform for gamers), including details on the various tools used to make the resulting app, which is available now on Heroku.

Mind Reading Lady Justice: Predicting court decisions using machine learning

Luke Persola

Using the Federal Contractor Misconduct Database, curated by the nonprofit Project on Government Oversight (POGO), Luke Persola set out to build a piece of machine learning software that could predict the outcome of a given case of contractor misconduct based on the other information in the database.

Produce2Recipe: "What Should I Cook Tonight?"

Jhonsen Djajamuliadi

After trying out a couple existing recipe recommendation apps, Jhonsen Djajamuliadi thought to himself, “Wouldn’t it be nice to use my phone to take photos of stuff in my refrigerator, and then get (personalized) recipes from them?” He decided to go for it, creating a photo-based recipe recommendation app for his final bootcamp project.

Nuanced Analysis of LocalBitcoins Data Suggests Bitcoin is Working as Satoshi Intended

Matt Ahlborg
Consultant at Airtm

While Bitcoin showcases a vast body of anecdotal evidence that argues its utility, or potential future utility, due to the pseudonymous nature of the blockchain as well as other factors, it's still hard to come across aggregate data that shows this behavior occurring on a consistent and measurable scale. To attempt to address this problem, Ahlborg took a close look at trading volumes on the Peer-to-Peer Bitcoin trading website Localbitcoins.com.

A Data-Driven Historical Analysis of Cosmo

Nora G. May

In an attempt to understand the market appeal of magazines, specifically women’s magazines, Nora G. May used data analytical tools to abstract magazines' marketing techniques. She extracted the text from magazine covers, performed NLP topic modeling, and used image processing techniques to understand graphic trends and representation.

Predicting overdose mortality per US county

Joyce Lee
Data Scientist at Clover Health

The opioid epidemic is one of the major public health catastrophes for this generation of Americans; similar to what tobacco/smoking or HIV/AIDS were to earlier generations, the opioid epidemic appears to be this era’s defining public health crisis. Lee set out to build a model to predict opioid-related mortality on a county by county basis with location-based insights and interventions in mind as a larger goal.

Obtaining Insights From Data: Optimizing an NBA Career

Aaron Frederick

Being a statistics-motivated sports fan, Frederick wanted to solve an atypical basketball problem: How can we optimize a typical basketball player’s career in the NBA? The question itself may seem open-ended, so in order to better scope this endeavor, he measured success by dollars earned.

Mapping Farmland from Satellite Imagery

Matt Maresca
Data Scientist at Annalect

The goal of Maresca's project was to perform semantic segmentation on satellite images in order to map out farmland around the city of Shanghai. He wanted to highlight a method that can be used to track farmland, urban development, and natural resources around the world in order to make better decisions for the future of our planet.

The Dankstimate: Cannabis Price Estimation — Part I

AJ Davis

With cannabis being legal for medicinal use in 31 states and recreational use in 9 states, there are thousands of dispensaries from which one can obtain pricing data to analyze. Davis thought it was a good time to dive into cannabis pricing to build a model that outputs a price benchmark for dispensaries (a “dankstimate” in the vein of Zillow’s “zestimate”).

Training a Neural Network to Detect Gestures with OpenCV in Python

Brenner Heintz

Imagine you’re hosting a birthday party. Everyone’s having a great time, music’s playing, and the party is noisy. Suddenly, it’s time for birthday cake! It’s too loud to use Alexa, and rather than hunting for your phone or a remote control, what if you could simply raise an open hand while in mid-conversation, your smart home device would recognize that gesture, and turn off the music? And with the same gesture, you could dim the lights just as the candles are lit?

Hack a Designer Look by Using Convolutional Neural Networks

Jiamin Han

Although uniqueness and personalization are great selling points, keeping up with the fashion trend is still the major theme that runs throughout the retail fashion business. The goal of this project is to find affordable alternatives to a designer outfit by using convolutional neural networks and other deep learning techniques.

A statistical analysis of minesweeper – Placing the Mines

David Dupuis
Data Scientist Researcher at Kwanko

There are some key elements to coding the game that can and probably should be memorized as they have other practical applications in computer science.

Which Hotel to Recommend?

Xuan Qi
Machine Learning Engineer at Petuum, Inc.

Xuan Qi's goal for her project was to "accurately match customers with hotel inventory in this highly competitive market." On a personal note, she writes that "as a mom, when I book a hotel, I would like the hotel to be family friendly, closer to the sightseeing, and relatively quiet. But, my standards would be different booking a romantic weekend for me and my husband. We would like to pick the hotels with great food, closer to bars, and musical events are a plus. "

Pitch Recommendation (a look into the data science process)

Vicky Szuflita

Vicky created a model dedicated to recommending pitches to the Cubs in games against the Cardinals. (Technically, this model could help any team – or any talented pitcher quite frankly – when throwing pitches against Cardinals players, but this model is specifically dedicated to her beloved Cubs.)

Travel Time Optimization with Machine Learning and Genetic Algorithm

Vladimir Lazovskiy
Data scientist working at the intersection of machine learning, content creation, and media.

In this project, Vladimir tackles the question: what is the relationship between machine learning and optimization? He explores how delivery companies can use the power of machine learning to forecast travel times between two locations and use the genetic algorithm to find the best travel itinerary for each delivery truck.

Major or Minor? Classifying the Mode of a Song

Alex Smith

Alex chose to work with music data because it is a type of audio that can evoke emotion in addition to thought. When she listens to music, she asks herself, "Why does a particular song make me feel happy or sad?" The key of a song helps determine the feeling and is made up of the tonic note and the mode. For this project, she aimed to predict the mode.

Everyone Poops

Mattie Terzolo

In San Francisco, human waste is a growing issue, both for the people who run into it and for the people who have no other option than to relieve themselves on public streets. Mattie built a model that predicted where and when human waste will show up, which could be used to better inform resource allocation for programs like San Francisco’s Pitstop (a program that brings portable bathrooms to areas that have high homeless populations).

Remote Sensing: An Overview of Common Pixel Classification Techniques

Alando Ballantyne
Founder & Data Scientist, Sovereign Finance

Image analysis and classification is something that Alando is passionate about (specifically as it pertains to analyzing satellite imagery to generate economic data for emerging economies). In this post, he writes about a few of the more common pixel classification techniques used in remote sensing.

SnapLoc — Places of interest in a city, from geo-tagged photos

Kalgi Shah

Kalgi created SNAPLOC, a product that does automatic image classification and spatio temporal analysis in order to recommend the places of interest for traveling in a new city.

Forecasting Uber Demand in NYC

Ankur Vishwakarma

For his final project, Ankur decided to see if he could forecast hourly Uber demand across NYC neighborhoods. In addition to time-lagged features (such as previous week’s demand), he added information specific to each neighborhood to improve predictions.

Ads That Click

Maragatham KN

Classifying ads using CATBoost Model based on the features of the ads and the user’s behavior.

Exploring Craigslist Musicians Communities

Robert Hill
Data Analyst at Anchor Worldwide

Robert used Craigslist to reflect what different musician communities value in aggregate.

A Friendly Introduction to Recommender Systems

Oren Trevet
Data Scientist at Fellowship.AI

Recommender systems are an effective key solution to overcome information overload. Oren wrote an article exploring the motivation behind recommendation systems, as well as providing an overview of different characteristics and potentials of various prediction techniques.

Predicting Crime in SF- a toy WMD Machine Learning 101: from Linear Regression To Deep Learning

Orlando Torres

Orlando started this project to show the potential ethical conflicts created by our new algorithms. In every conceivable field, algorithms are being used to filter people. In many cases, the algorithms are obscure, unchallenged, and self-perpetuating.

How to Simplify Your Holiday Festive Meal Planning

Heng-Ru May

Once again, the holiday season is upon us...Should you find yourself preparing the whole meal or offering to contribute a dish or two and in the mood for homemade culinary adventures, there’s a little web application, called the MenuPlannerHelper (abbreviated as MenuHelper) Heng-Ru May developed a while back that could come in handy.

Fighting Gerrymandering: Using Data Science To Draw Fairer Congressional Districts

Joseph Gambino

Politicians have used gerrymandering, the practice of drawing political districts for partisan advantage, to skew elections since the early days of this great country...Joseph's goal was to build a tool that would let anyone optimize a map on whatever they think most important.

Car Back! A Video-Based Car Detector For Cyclists

Rebekah Cunningham

Rebekah's vision is to be able to attach a camera to the back of her bike, near the seat which captures video in real time and alerts of any cars that are approaching from behind. The alert would be an audio cue that is played in one of the apps that is already running -- Strava, Spotify, or Audible as examples.

Mix Retriever: A Hip-Hop Playlist Generator

Zach Heick

To combine the functionality of individual song-based playlist generators with a focus on making content based recommendations, Zach created a web app that builds a hip-hop playlist of songs with similar lyrical meaning and mood around a song specified by the user.

More than a Million Pro-Repeal Net Neutrality Comments were Likely Faked

Jeff Kao

Jeff used natural language processing techniques to analyze net neutrality comments submitted to the FCC from April-October 2017, and the results were disturbing.

Snow Prediction

James Cho

Using weather radar and terrain information to fill in gaps between ground snow sensors.

College Closure Risk

Andrew Wiegel

PUBmatch.co

Ryan Lambert
Data Scientist at Gild

PUBmatch.co makes it easier to parse through the giant open access database PubMed by allowing you to input anything from a news article clipping to an email thread.

Machine Learning for Self Driving Cars

Galen Ballew

Using LinearSVC and Histogram of Oriented Gradients to detect and track vehicles.

Incorporating Curb Appeal Into Home Price Estimates Using Deep Learning

Lauren Shareshian

Lauren used Zillow metadata, natural language processing on realtor descriptions, and a convolutional neural net on home images to predict Portland home sale prices.

Flood Water Detection with A Semi-Supervised U-Net

Daniel Licht

Text to Video Generation with AI

Antonia Antonova

This project aims to build a deep learning pipeline that takes text descriptions and generates unique video depictions of the content described.

Justin Bieber Helped Me Learn About Neural Networks

Susan Fung

Building a Music Recommender with Deep Learning

Matt Murray

Targeting Disaster Relief from Space

Emily Miller

Emily used machine learning to better target disaster relief efforts, focusing on Typhoon Haiyan, which hit the Philippines in November of 2013.

RecipEAT

Phillip Tan

Data Sciencing Motorcycles: Lean Assist

Josh Peng

Motorcycle Lean Assist uses a convolutional neural network to detect the lean angle of a motorcycle through image classification, providing you with rider feedback on your current lean angle so you don’t have to guess.

Promoting Positive Climate Change Conversations via Twitter

Tim Martin

Tim's project explores the conversations about climate change that took place on Twitter in March 2017. With 1 million tweets from 560,000 users, Tim identified people belonging to different communities and used tools such as the Twitter API, Spark, NetworkX, and Gephi to derive insight from those conversations.

Improving Brand Analytics with Image Logo Detection

Max Melnick

Using a convolutional neural net in TensorFlow, Max developed an application that can improve brand analytics through logo detection in images.

Music Composition with LSTMs

Naoya Kanai

Naoya explores the intersection between data and art by designing a recurrent neural network utilizing Long Short-Term Memory nodes (LSTMs) to learn patterns in the Six Cello Suites by J.S. Bach and generate its own musical fragments.

Creating a Beer Recommendation Engine

Will Chernetsky

To combat flaws in other reccomendation systems, Will decided to use natural language processing of beer reviews to find similarity of language used to describe beers. He found the words people use to describe beer give better results than arbitrary scores or styles.

Marijuana through the lens of the New York Times

Peter Rasmussen

The legality of and public’s view towards marijuana is rapidly changing as more states decriminalize and legalize the drug. As such, how have the words associated with marijuana in news articles changed over time?

Identifying cars using Motordex

Justin Chien

Using Flask, Justin created a web app to combine his passion for photos and cars. Motordex uses the decision tree process and different models to identify cars based on a submitted picture.

Cracking Passwords with Neural Networks

Hasan Haq

You don't have to be an expert to know that password security is a big issue for companies these days. It seems every other week you hear of a well known website getting hacked. Hasan Haq's project uses neural networks to generate "dictionary" word lists to be used in password cracking.

Take Control of your Healthcare with MedTracker

Katherine Pully

MedTracker i s a system to track your (psychiatric) medications and your moods and to compare what is working well for other users. Users can register as a patient or a doctor.

Using convolutional neural networks to predict clothing

Brian Holligan

The Clothing Predictor is a web app that uses convolutional neural networks to identify images with one person in them, and then predict the clothing being worn by that person.

Personalizing Travel Recommendations with MemoTrek

Li Zhang

MemoTrek is an application that takes your travel photos as input and makes personalized recommendations for future travel destinations. It provides two types of recommendations: you-may-also-likes for a similar type of experience and something-different for new adventures.

Parking Lot Image Classification through OpenCV and a Flask App

Rohan Shah

As more data is sourced through satellite imagery it has become an important task to accurately identify important hotspots and targets within these images so as to classify them for practical use.

Applying Data Science to the Supreme Court

Emily Barry
Data Scientist at LegalServer

The Supreme Court is arguably the most important branch of government for guiding our future, but it's incredibly difficult for the average American to get a grasp of what's happening.

Basketball Player Tracker

Micheal Lai
Strategy Consultant & Data Scientist at IBM

Micheal created a system that can track players in a basketball clip and translate them to a coordinate grid. This kind of motion tracking already exists in the form of SportVU - but you can use the accessibility of YouTube clips to create player tracking.

Made atMetis