Real Estate Investment Value Prediction Model

The project aims to develop a predictive financial investment model for real estate, focusing on estimating the future value of properties based on investors' goals. The model will integrate historical data of investment properties with macroeconomic indicators, county-specific insights, investment rates, and loan types. By analyzing these factors, the model will provide a comprehensive understanding of how various elements influence property values over time. This project offers learners the opportunity to apply their knowledge of data analysis, financial modeling, and economic principles to a real-world scenario. The goal is to create a tool that can assist investors in making informed decisions by predicting property values at specific future points.

PHP Web Application Accelerator Prototype

The project involves developing a functional prototype for a PHP-based web application accelerator to enhance user experience (UX) through improved usability, performance, and scalability. The goal is to create a robust and user-friendly prototype to serve as a foundation for future development. The project will focus on integrating cloud services, utilizing Terraform to deploy the application as containers on platforms like AWS, Azure, or GCP. This will allow learners to apply their knowledge of the web, cloud computing, and infrastructure as code. The project will provide an opportunity to explore the intersection of web development and cloud technologies, emphasizing best practices in UX design and scalable architecture. By the end of the project, learners will have gained hands-on experience in creating a cloud-ready web application prototype.

Data Analysis Research

The goal of the project is to perform data analysis. The company is building a Market intelligence product focused on creating a human-centric sales experience through AI. The data sources include 10k, 8k, and other financial statements. We need help in analyzing these financial statements and capturing key insights from this data. We need this input to train and build an ML Model and use it to generate pitch documents on the fly. We would like to collaborate with students to analyze our raw datasets. This will involve several different steps for the students, including: Familiarizing themselves with our project’s objectives and analysis procedures. Checking for and capturing key business imperatives, risks/issues for S&P 500 companies and creating a dataset. Creating an automation bot to extract key information from the financial statements. Identifying other data sources that can provide market insights for the ML model Bonus steps in the process would also include: Recommending data analysis approach and automation