In this project i have developed a machine learning model for predicting the price of AirBnb stays using regression. Used a multitude of machine learning models for regression to select the best model for the job. Usage of Pipelines to transform the data for Linear and non-Parametric models.
As Linear models require the data the to be normalised and Non-Parametric models do better without normalization, it was imperative that there were two different datasets one for linear models and the other for non-Parametric models.
Models used
- Decision Tree Models:
a) Decision Tree Regression
b) Random Forrest Regressor
c)Gradient Boosted Regression
2)Linear Models:
a) Lasso regression
b) Ridge regression
c) ElasticNet regression
d) Support Vector regression
Hyper parameter optimization using GridSearchCV and RandomizedSearchCV.
Cross validation using K-fold validation and visualization of metrics.