In this repo you can find an inplementation of some of the most important ML algorithms using the Scikit-Learn library to predict housing prices in Buenos Aires, Argentina. Both of the analysis that you'll find here are
Project 1 is a first approach to accomplish this goal. Some of the models/algorithms and techniques implemented in this notebook are:
- EDA (Exploratory Data Analysis)
- Decision Trees
- Linear Regression
- KNN
In project 2 we use some of the more advanced models, as well as feature engineering techniques to use more available information in the dataset. Some of the models/algorithms and techniques implemented in this notebook are:
- EDA
- Outliers imputation
- One-hot-encoding
- Polynomial Regression (Ridge and Lasso)
- Xgboost
- PCA