AI_Model_Innovation_Hub

Inspiration

The inspiration for our house price predictor stemmed from the dynamic and complex nature of the real estate market, where accurate price predictions can empower buyers, sellers, and investors to make informed decisions. Traditional machine learning models like linear regression or gradient boosting often rely heavily on structured numerical data, but they may miss nuanced patterns in unstructured data, such as property descriptions or images. Google’s Gemini 2.5 Pro, with its advanced multimodal reasoning capabilities and ability to process text, images, and audio, offered an exciting opportunity to explore a generative AI approach. We aimed to create a model that not only predicts house prices but also generates rich, contextual insights about property value drivers, revolutionizing how stakeholders interact with real estate data

What it does

Predict house prices with high accuracy by analyzing multimodal inputs. Generate human-readable explanations of price predictions, detailing how features like location, amenities, or even aesthetic appeal (from images) influence the price. Produce hypothetical property listings or price scenarios based on user prompts (e.g., “What would a 3-bedroom house in San Francisco with a modern kitchen cost?”). Provide visualizations, such as heatmaps of feature importance or simulated property images, to enhance interpretability.

How we built it

Data Collection, Data Preprocessing, Model Development, Deployment, Visualization

Challenges we ran into

Multimodal Integration: Combining structured numerical data with unstructured text and image inputs was complex, as Gemini’s strengths lie in reasoning rather than traditional regression. We had to experiment with prompt engineering to align generative outputs with precise numerical predictions. Data Limitations: The California Housing dataset lacked rich text descriptions and images, requiring us to generate synthetic data, which introduced potential biases. Ensuring the synthetic data aligned with real-world patterns was time-consuming.