Copenhagen Price Prediction
We’ve embarked on an exciting project that brings a new way to predict housing prices in Copenhagen with amazing accuracy. Using PowerBI and Python, we’ve reached an accuracy rate of 97%. Our project is at the cutting edge, using a detailed machine learning model. This model was carefully trained with data from 1,234 homes in the lively Amager area, zip code 2300. This choice reflects Copenhagen’s diverse housing market.
Over ten years, we’ve gathered data showing changes and trends in the housing market. This long-term view helps make our price predictions more reliable. It’s a valuable tool for people looking to buy a home, real estate investors, and policy makers.
This project highlights the power of combining PowerBI and Python to understand complex data. Our work, titled “Accommodation Price Prediction in Copenhagen: A Machine Learning Approach,” improves how we predict prices. It also helps make better decisions in the ever-changing real estate market.