IntoTheHomeQuest

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Housing Statistics

Inspiration

With the advent of AI, we wanted to use it to change the market. The first thing we thought of was the housing market which is one of the largest Investments anyone can make. Instead of spending hours upon hours looking for a house and wasting time looking for just the right house for you, the technology does the work for you. AI will research the current market to find or build your dream home so that you can rest stress-free.

What it does

Takes in data from online housing databases and parses through it to find what's right for you. Our app uses advanced machine learning models to generate a prediction on whether the user would want a specific house over another. Furthermore, the app also generates, using cutting edge computer vision models, 3D renderings of the ideal home for the user in which they can experience day-to-day life like they were actually there.

How we built it

We initially started development making a type-form questionnaire that asks the user questions regarding their ideal living conditions. Then, we integrated the survey with the Hugging face open-source library's many machine learning models including blender bot, gpt2, and ctrl. Afterwards, we imported multiple online housing databases and fed it to the machine learning models to receive an output of the best housing options for the user. Finaly, we made an option for the user to interact with our computer vision model to generate their ideal home on the off chance our models haven't satisfied them already.

Challenges we ran into

We encountered a problem in which the models we were using didn't output the text we intended it to. Therefore, we utilized a wider range of models to ensure more content validity. We also encountered issues data collection from databases, so we decided to code our own tool to collect various domains without the use of online restrictive API's.

Accomplishments that we're proud of

We managed to read through the Huggingface's api, vertex's api, as well as Google cloud. We also tested out many machine learning models, ran our own data analysis to ensure user satisfaction, and created our own tools for dynamic allocation of data.

What we learned

AI ATL was a very difficult mental endeavor of different problem-solving techniques that challenged us in ways we didn't foresee, but we paved through and managed to create something that we believe will revolutionize the housing market for the foreseeable future. We learned how to work as a team and succeed through the most difficult of times and persevere till we saw gratifying results.

What's next for IntoTheHomeQuest

For our next step, we will continue the development of this application and invest time and effort to create a tool for everyone to apply in your daily life. We will be marketing this tool to anyone who have become stressed through the housing purchasing process. Finally, we will be implementing other 3D modeling features and eventually build our own 3D engine to grow and expand our startup!