This project aims to develop a machine learning (ML) model using the HouseCanary Bulk Housing Dataset to create a consumer-facing application called "Build-Your-Dream-Home." The app allows users to input various characteristics they desire in a home and receive a price estimate. The goal is to provide an easy-to-use interface that offers relatively accurate price estimates for custom-built homes.
User-Friendly Interface: The app will be designed for ease of use, allowing users to input their desired home characteristics effortlessly.
Price Estimation: The ML model will generate a price estimate based on the user's inputs.
Sales Funnel Integration: The app will feature a link that directs users to a filtering system, helping them find exact matches of homes based on their selections.
Fast Predictions: The model will focus on delivering quick predictions using a limited number of columns.
Ease-of-Use and Interpretability: These are key objectives to ensure the app is user-friendly and the results are easily understandable.
Business Impact: Demonstrate how a "Build-Your-Dream-Home" app can smoothly guide customers through the home-buying process.
Dataset: We will use the HouseCanary Bulk Housing Dataset. A newer version of the dataset will be downloaded to simulate the model's performance in a production environment.
Model Accuracy: Due to the limited size of the dataset, the model may not offer high accuracy. However, the concepts and techniques used will be solid and can improve with access to a larger dataset.
Metric: We will use MAE, RMSLE, Adjusted R-squared, and MAPE to measure the performance of the model.
Customer Sentiment: The primary impact of the project is to enhance customer sentiment by providing a fun and engaging way to estimate the cost of their dream home.
Low Cost of Inaccurate Predictions: Since the output is a continuous variable quantifying a total, the cost of inaccurate predictions is low.
Low Operational Cost: The cost of running this basic model is also low.
Data Availability: Ideally, access to more data would allow for a more complex model and potentially different algorithms.
Real-World Application: The overarching goal is to demonstrate real-world ML techniques on a 'messy' real-world dataset that translates well for business use.
Data Limitations: Throughout the project, notes will be included to recommend changes that could account for more data.
Continuous Variable Output: Since the output is a continuous variable quantifying a total, ML is the correct solution for our task.
Exact Money Amounts: Exact money amounts are not needed for this project; the goal is to provide a fun way for customers to envision the cost of their dream home.
This project aims to create a user-friendly application that leverages ML to provide home price estimates based on user inputs. While the dataset's limitations may affect accuracy, the techniques and concepts demonstrated will be valuable for real-world applications. The primary focus is on enhancing customer sentiment and demonstrating the business impact of such an application.
Feel free to contribute to this project by opening issues or submitting pull requests. Your feedback and suggestions are welcome!
Disclaimer: This project is for educational and demonstration purposes. The accuracy of the predictions may vary, and the model's performance can improve with access to a larger dataset.