Solar Shield
Real Estate Platypuses
Randomized Forest Regression Model
Predicting Gas Price
Community Matcher
BUSI
Asthma & Smoking Risk Analysis
ClimateCheck
Unity Nonprofits
Moove
GEOdle
ValueGuessr
Shelterfy
ZipScope
LandMark
mQuery
Predicting Home Prices
I See OC Health
Bathrooms of Orange County
Property Guessr
ZotLease
Bathrooms of Orange County
Solar Shield
April 2025
Created by Atif Ali Usmani
Solar Shield is a cybersecurity risk-mapping platform that predicts potential power grid attacks targeting solar infrastructure. It focuses on vulnerabilities in solar inverters: key entry points that attackers can exploit to trigger large-scale blackouts. The system scores ZIP Codes in the US on solar readiness based on solar infrastructure density and the severity of known cybersecurity vulnerabilities sourced from NIST and CISA. It also enables users to check a specific property against their community’s risk profile, with the help of Melissa datasets. The final product is an interactive map that ranks areas into five risk tiers, helping grid operators and residents identify where attacks are most likely.
Melissa Product(s) Used
Real Estate Platypuses
April 2025
Created by Nivedha Aravindmali, Pratham Hebbar, Kalea Conje, Hailey Chen
This project uses Melissa Consumer and Property Assessment datasets to reveal how personal lifestyle traits, like pet ownership and hobbies, influence real estate choices. The team built a k-nearest neighbors (KNN) model that predicts a user's ideal home features, such as lot size and bedroom count, based on their unique household profile. The team also integrated Omni Analytics to create a visual dashboard, making the complex relationships between consumer behavior and property attributes easy to explore. Ultimately, the project turned real-world data into a helpful tool that predicts home-buying behavior.
Melissa Product(s) Used
Randomized Forest Regression Model
April 2025
Created by Abhinav Kumar, Inesh Agarwal
This datathon project helps aspiring restaurant owners identify the perfect location for their business. By sourcing data from various Melissa datasets, the team analyzed factors such as local demographics, consumer interests, and population movement to predict a restaurant's likelihood of success in a specific neighborhood. A key takeaway from the project was that net migration—which signals whether a population is growing—is one of the strongest indicators of business potential.
Melissa Product(s) Used
Predicting Gas Price
April 2025
Created by Charan Kaur Sandha, Hanin Ali Barakat, Gracy Sutaria, Meera Sriram
With gas prices on the rise, this team set out to determine whether income levels and demographics across ZIP Codes in Los Angeles County correlate with local gas prices. By combining Melissa ZIP Code data with additional gas pricing datasets, they conducted a series of analyses and visualizations. Their findings indicate that gas prices show no strong correlation with demographic factors in these areas.
Melissa Product(s) Used
Community Matcher
April 2025
Created by Sonia Mangat, Revti Gaikwad, Karl Klaffenbach, Shubham Waldiya
Community Matcher is a project that explores whether a person’s lifestyle, values, and habits can predict where they would best fit within a community, using a residential dataset from Melissa and focusing on the city of Rancho Santa Margarita to start with. By analyzing factors like demographics, finances, and personal interests, the tool creates a “lifestyle fingerprint” and maps users to neighborhoods that align with their profiles. Despite the complexity of predicting where people choose to live, the project showed that meaningful patterns do exist, highlighting how certain lifestyles tend to cluster in specific communities.
Melissa Product(s) Used
BUSI
April 2025
Created by Anish Bhandarkar, Alex Misra, Zachary Niu
Winner of Data Heist 2026, BUSI is a tool that identifies optimal commercial locations for a user’s business. Its prediction model, based on a modified logistic regression model, leverages data from the Melissa Consumer Data dataset to assign viability scores using key factors such as income, interests, and demographics. The project demonstrated promising accuracy, with predicted locations ending up near similar businesses that already exist, highlighting its effectiveness in guiding real-world business decisions.
Melissa Product(s) Used
Asthma & Smoking Risk Analysis
April 2025
Created by Ethan Chu, Thy Tran, Hiu Tung Nathalie Chiu, Faria Mostafa
For Data Heist 2026, this team of students analyzed key factors associated with asthma and smoking. Using a variety of tools and datasets, including the Melissa ZIP Code dataset, they examined variables such as race, household income, education levels, and more. Their findings showed that asthma rates were more strongly correlated with certain racial groups, while cigarette smoking rates were more closely linked to education level and median household income. Both asthma and smoking were correlated with frequent mental distress.
Melissa Product(s) Used
ClimateCheck
March 2025
Created by Sristi Bhattacharjee, Ziqiao, Nivedha Aravindmali, Angie Che
ClimateCheck is a climate risk assessment platform submitted to IrvineHacks 2026 that predicts a property's risk of floods, wildfires, and landslides. This application is built with a variety of tools and datasets, one of which is the Melissa Global Address Verification API, used to accurately parse and geocode user-entered property addresses into precise latitude and longitude coordinates. These coordinates are then paired with national flood, wildfire, landslide, and elevation datasets to generate 0–100 climate risk scores, 30-year projections, and personalized safety recommendations for prospective homeowners.
Melissa Product(s) Used
Unity Nonprofits
January 2025
Created by Tom Zheng, Daniel Wu, William Jiang, Candice Li
Unity Nonprofits allows users to look for nonprofits around their ZIP Code that best suit their interests. The team built this project for IrvineHacks 2025 and won the award for "Best Use of Melissa API or Data Sets" due to their creative utilization of the Melissa Nonprofit Organization Lookup.
Melissa Product(s) Used
Moove
May 2024
Created by Leilani Bascos, Isaac Nguyen, Robert Gilmore, Alexander Yin.
Moove is a property purchasing platform aimed at college students that provides property-owner-authentication (via a unique milKEY) and financial advice (Moove Report). Leveraging Melissa APIs to verify ownership of a property via name, SSN, and property address, Moove keeps students safe from potential scams by ensuring that the seller is really who they say they are and that they legally own the property. Once the seller is confirmed to be valid, Moove generates a unique milKEY that students can use while looking up an address. After looking up the property, Moove generates a Moove Report for the student, which uses Melissa Property WEB API to display helpful information such as future property costs, general information of the home, a picture of the property, and mortgage plan. Finally, Moove assigns the property a custom Moo score, which rates how well the property suits the student based on factors such as their yearly income and credit score. Moove makes great use of Melissa APIs to create a safe and informative platform for students to learn more about a property, and won Best Use of Melissa Cloud API/Data Sets at VenusHacks 2024!
Melissa Product(s) Used
GEOdle
April 2024
Created by Ryan Nguyen, Dennis Lustre, Sam Free Pittman, and Keisun Nguyen Luc
Created for the 2024 Atlantis Datathon held at UC Irvine and winning two categories (People's Choice and Best Use of Melissa API or Data Sets), GEOdle took inspiration from the popular games Wordle and Geoguessr to create a fun, colorful online guessing game, where the player gets 7 chances to guess a randomly-chosen city based on hints such as population size and latitude/longitude. The GEOdle team made creative use of Melissa Data resources, including various datasets available on the Melissa Education portal as well as the Reverse GeoCoder API, to get all the geospatial data the game needed!
Melissa Product(s) Used
ValueGuessr
January 2025
Created by Sean Liem, Preston Takashi Sasaki, Julius Yang, Raymond Che
Inspired by the game GeoGuessr, this team of students created a game called ValueGuessr for IrvineHacks 2025. The player's goal is to guess the price of a property, given the location and additional hints such as the number of bedrooms and bathrooms, and how many stories the building has. ValueGuessr makes use of Melissa's Property Assessment Data to obtain all this information.
Melissa Product(s) Used
Shelterfy
January 2025
Created by Justin Rodriguez Macalad, Jonathan Chau, Carson McArthur Davis, Jackson Yan
In response to the LA wildfires, this team of students came up with Shelterfy, a website that allows users to view the wildfires and shelters around them on a map. Shelterfy leverages Melissa's Business Coder and Property APIs to validate shelter data and filter out suspicious shelters.
Melissa Product(s) Used
ZipScope
January 2025
Created by Lee Stilwell, Jared Lee, Megan Lai, Daniel Yang
ZipScope is a website that helps users navigate various neighborhoods in LA and Orange County by scoring ZIP Codes on several key metrics: Safety, Housing Affordability, Public Education Quality, Transportation, Environment, and Healthcare Accessibility. This project uses various data sources including the Melissa Zip Code dataset to obtain information on these ZIP Codes.
Melissa Product(s) Used
LandMark
May 2024
Created by Alvina Chow, Sruthi Rita Leon, Jenny CHau Phan, Dia Ganesh Kumar
LandMark is an informative website that educates users on the real estate market, helping them to make informed decisions by providing detailed insights into market trends, property information, and projected property values. This project utilizes Melissa's Email Verification API to verify the user's email address upon logging in; after that, the user is able to add houses to their dashboard by entering a residential address, which is then verified using Melissa's Address Search API.
Melissa Product(s) Used
mQuery
May 2024
Created by Conner Liu, Michelle Jinhua Sheu, saharsid123
MQuery is an AI project that helps business owners find the best consumers to target based on their proposal. It uses Melissa Consumer Data to find customers that are most likely to be interested in the business product and retrieves their contact information.
Melissa Product(s) Used
Predicting Home Prices Using Neural Networks & Melissa Data
April 2024
Created by Matthew Eimers, Vincent Ramos, Christopher Effarah, Seth Abuhamdeh
This team of students trained a neural network on Melissa Data Sets to find correlations in property prices with other factors such as location and date built, winning the Best Presentation category at Atlantis Datathon 2024.
View PresentationMelissa Product(s) Used
I See OC Health
April 2024
Created by Himal Malik, Matthew Angeles, John Fortin, Thien Vu
The I See OC Health team utilized the Melissa Consumer Data Set to develop a machine learning model that predicts how likely an Orange County resident is to prioritize their own health with a 77% confidence rate.
Melissa Product(s) Used
Bathrooms of Orange County
April 2024
Created by Arnav Kanekar, Nishant Nuthalapati, Dron Mongia
Wanting to explore trends in the Orange County area, this team of students used Melissa Datasets to discover interesting statistics about bathroom counts and cities. They analyzed bathroom counts versus other factors, including lot sizes and population density, to discover any correlations and historical trends.
Melissa Product(s) Used
Property Guessr
February 2024
Created by Leah Sun, Bryan Silva, Jacob Moy, Peter Tumali
Property Guessr is a web game in which users can guess which house out of the two displayed has a higher/lower assessed value. Users who are logged in can also access additional features, including building a streak if they continuously guess right, earning a rank on the leaderboard, and viewing their accuracy percentage and high score.
Melissa Product(s) Used
ZotLease
February 2024
Created by Hai Nguyen, Kayla Son, Jessie He, Richard
ZotLease is an all-encompassing website that simplifies subletting for UCI students. It enables users to effortlessly sublet their homes or discover suitable sublets, streamlining the entire housing process.
Melissa Product(s) Used
Bathrooms of Orange County
April 2024
Created by Arnav Kanekar, Nishant Nuthalapati, Dron Mongia
Wanting to explore trends in the Orange County area, this team of students used Melissa Datasets to discover interesting statistics about bathroom counts and cities. They analyzed bathroom counts versus other factors, including lot sizes and population density, to discover any correlations and historical trends.
Melissa Product(s) Used