Innovative Real Estate Prediction App - Bank of Greece Datathon 2023
"Join the Bank of Greece Datathon 2023 team in creating a groundbreaking app using machine learning to predict real estate prices in Greece. Explore the value proposition, revenue model, target audience, open data sources, and challenges faced. See compelling visualizations and the architecture draft utilizing Python libraries."
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Presentation Transcript
Idea Presentation Datathon 2023 Bank of Greece Team ML s (Machine Learner s) Isaak Karagiannidis MSc Data Science Dimitris Alexiou MSc Information and Communication Systems Engineering
Idea The app uses machine learning algorithms and open source tools, in order to predict the real estate price index by geographic area in the wider area of Greece, but also a calculator to calculate mortgage installments. 2
Value Proposition Our property value predictor tools use machine learning algorithms to make predictions based on various data points, providing valuable insights for investors and real estate agents. 3
Revenue model The app can be offered for a subscription-based model that includes additional features or benefits. 4
Target Group Target Group Investors who want to predict real estate property values. Key Partners Real estate agents and mortgage brokers who can help promote the app to their clients. Financial institutions who may want to offer the app to their customers. 5
Open Data Streams The Open Data are gathered from the Bank of Greece, European Central Bank and the Bank for International Settlements. 6
Problems faced/resolved The volume of data provided by the Bank of Greece is not sufficient for proper training of the algorithms, although we can gather additional data from European Central Bank and the Bank for International Settlements for the first sprint. 7
Architecture First draft of the architecture for the machine learning algorithm uses the programming language Python and the following libraries: Pandas NumPy Matplotlib-Seaborn Scikit-Learn Pickle As for the rest of the architecture model, the UI part can be developed using the React framework coordinated by an Express Node server, aiming the app for Web use, although the complete architecture is to be discussed and decided in a later sprint. 11
Thank you for your time Team ML s (Machine Learner s) Isaak Karagiannidis MSc Data Science Dimitris Alexiou MSc Information and Communication Systems Engineering
