I am seeking to commission a machine learning developer with expertise in data analysis and pattern recognition to create a solution for identifying âcookie-cutter homeâ neighborhoods. This project aims to leverage property listing data to detect patterns and trends indicative of standardized home designs within specific regions.
Project Overview:
Objective: To develop a machine learning model that can analyze and cluster similar property listings, identifying neighborhoods with cookie-cutter homes.
Data Source: The model will utilize data from platforms like Zillow (or other specified sources) and may require integration with relevant APIs.
Key Metrics: The analysis should consider attributes such as builder name, square footage, year built, number of bedrooms and bathrooms, architectural style, geographic proximity, and other relevant characteristics.
Output: The model should categorize and group identified properties into clusters, e.g., âGroup Aâ with 13 similar homes, âGroup Bâ with 22 similar homes, etc.
Requirements:
Data Collection: Automate the collection of relevant property listing data through APIs.
Preprocessing and Analysis: Clean and preprocess the data to analyze the key metrics that define cookie-cutter homes.
Model Development: Create a machine learning model capable of identifying and clustering similar property listings.
Validation and Verification: Implement validation steps to ensure accuracy and reliability.
Reporting and Visualization: Provide tools or dashboards to visualize and interpret the findings.
Compliance: Adhere to all relevant legal and ethical guidelines, including privacy and terms of service of the data providers.
Please provide your thoughts on the approach, a proposed methodology, estimated timelines, and a detailed quotation. Additionally, examples of similar work you have done would be helpful to understand your capabilities.
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