Analysis of House prices with Machine Learning

In this project we develop an analysis over the house prices. We make a basic EDA to undertand the data. After that we use 3 different linear regression models to try to predict the results.

You should check the EDA and the Model Jupyter notebook to understand the data.

Models

1 Linear Regression: with a

ERROR PREDICTIONS

MAE 1890091.6085735606

MSE 52837100282241.73

RMSE 7268913.280693457

R2 0.7911340192783549

2 Lasso:

ERROR PREDICTIONS

MAE 994575.8051471912

MSE 168991930773220.47

RMSE 12999689.641419156

R2 0.3319719445910607

3 Ridge:

ERROR PREDICTIONS

MAE 1381841.2284836913

MSE 36933593916162.945

RMSE 6077301.532437151

R2 0.8540008578505007

4 Random Forest Regressor:

ERROR PREDICTIONS

MAE 1267626.8885942616

MSE 122856752785652.98

RMSE 11084076.541852865

R2 0.5143451093686058

Collaborators:

1. Alejandro Rodríguez

2. Roger Mesén

3. Coralia Arguedas

4. Luis Rodolfo Valverde Delgado