WakaraNai

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 Define: model의 타입은?( 결정트리? 다른 거?) Fit: data의 패턴을..

Prediction of New House Price in Melbourne¶ ['Rooms', 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude']에 따라 house의 Price가 어떻게 되는지 model을 만들자. In [6]: import pandas as pd #It has DataFrame(SQL) melbourne_file_path = r"C:\Users\32mou\Desktop\melb_data.csv\melb_data.csv" melbourne_data = pd.read_csv(melbourne_file_path) melbourne_data.describe() #Checking Missing Value is important Out[6]: Rooms P..