kaggle Pipelines

# Most scikit-learn objects are either transformers or models.

　　# Transformers are for pre-processing before modeling. The Imputer class (for filling in missing values) is an example of a transformer. # Over time, you will learn many more transformers, and you will frequently use multiple transformers sequentially.

　　# Models are used to make predictions. You will usually preprocess your data (with transformers) before putting it in a model.

　　# You can tell if an object is a transformer or a model by how you apply it. After fitting a transformer, you apply it with the transform # command. After fitting a model, you apply it with the predict command. Your pipeline must start with transformer steps and end with a # model. This is what you'd want anyway.

　　# Eventually you will want to apply more transformers and combine them more flexibly. We will cover this later in an Advanced Pipelines # tutorial.

import pandas as pd
from sklearn.model_selection import train_test_split

# Read Data
data = pd.read_csv('../input/melb_data.csv')
cols_to_use = ['Rooms', 'Distance', 'Landsize', 'BuildingArea', 'YearBuilt']
X = data[cols_to_use]
y = data.Price
train_X, test_X, train_y, test_y = train_test_split(X, y)

from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Imputer

my_pipeline = make_pipeline(Imputer(), RandomForestRegressor())
my_pipeline.fit(train_X, train_y)
predictions = my_pipeline.predict(test_X)