Welcome to this project where my three teammates and I created a predictor for NYC housing prices.
Through the dataset from year 2016-2017, we used supervised models in shallow learning for optimized predictions.
Some models included Ridge Regression, K-Nearest Neighbor, Random Forest, and XGBoost.
We ended up seeing the best performance among the more complex tree-based models.
An end-to-end pipeline is built within the notebook for the most automated performance. To see more, please read the ipynb notebook.
• Google Colab • Python• numpy
• pandas
• sklearn
• scikit-learn
• xgboost