[Kaggle Courses] From Fitting to Prediction

1. Selecting Data for Modeling

data = pd.read_csv( filename )
data.columns
data = data.dropna(axis=0)

- Selecting The Prediction Target:

• Dot-notation: 필요한 column 추출 
• prediction target(y)

y = data.Price

 

2. Choosing "Features" (X)

data_features = ['Rooms', 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude']
X = data[data_features]

 

 

3. Building My Model

1. Define: model의 타입은?( 결정트리? 다른 거?)
2. Fit: data의 패턴을 포착해라
3. predict
4. evaluate

from sklearn.tree imoprt DecisionTreeRegressor
data_model = DecisionTreeRegressor(random_state=1)
data_model.fit(X,y)
# data_model.predict(X.head())
# data_model.predict(X)