Objectives:
The project addresses the challenge of accurately predicting house prices based on various factors like the number of rooms, area, and location. It aims to identify the most effective regression algorithm for this purpose by benchmarking linear regression and Bayesian ridge regression models.
Approach:
The problem was solved through a structured approach:
Results and Insights:
The project successfully provided accurate predictions for house prices and demonstrated the strengths of both regression models. By evaluating and comparing their performance, the project highlighted the suitability of Bayesian ridge regression for handling certain aspects of the dataset, offering valuable insights for practical applications in real estate pricing.
Wir benötigen Ihre Zustimmung zum Laden der Übersetzungen
Wir nutzen einen Drittanbieter-Service, um den Inhalt der Website zu übersetzen, der möglicherweise Daten über Ihre Aktivitäten sammelt. Bitte überprüfen Sie die Details in der Datenschutzerklärung und akzeptieren Sie den Dienst, um die Übersetzungen zu sehen.