This is my submission to the Housing Prices Competition for Kaggle using XGBoost for prediction.
Goal:
to predict the sales price for each house. For each Id in the test set, you must predict the value of the SalePrice variable.
Machine Learning Models Used Models: Gradient Boosting Regressor
Preprocessing:
Data preprocessing is the first and crucial step while creating a machine learning model. by passing two separate objects to the preprocessing methods one is representing the training data and the other represents the unseen data for testing.
Feature Engineering: to facilitate the machine learning process and increases the predictive power of machine learning algorithms by creating features from raw data.
Model Fitting and Prediction:
measureing how well the model generalizes to similar data to that on which it was trained. and predicting the outcome of the data