Predictive Modeling - Financial Markets

Service Description

In this project, students use real-world data to predict home prices with artificial intelligence. They gather information from Zillow—such as number of bedrooms, bathrooms, square footage, age, and lot size—for houses in their area, then train a simple deep learning model that uses these features to estimate market value. Along the way, they learn essential machine learning concepts like data preprocessing, model training, and evaluation, all while working with an intuitive dense neural network with ReLU activation. By the end, students can test their model on new homes and compare predictions to actual prices, making this a fun and practical introduction to AI, math, and data science. Prerequisite Students participating in this project should be proficient in basic programming concepts, including working with variables, loops, and functions. Prior experience with Python is recommended, as it will be the primary language used for building and training the machine learning model. Students should have completed or be currently enrolled in Precalculus, as the project will involve mathematical concepts such as functions, basic algebra, and data interpretation that are essential for understanding how the model processes information.