kaggle House_Price_XGBoost

kaggle House_Price_final

代码

import numpy as np

import pandas as pd

from sklearn.ensemble import RandomForestRegressor

from sklearn.metrics import mean_absolute_error

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import Imputer

from sklearn.linear_model import LinearRegression

from sklearn.tree import DecisionTreeRegressor

import matplotlib.pyplot as plt

from sklearn.preprocessing import Imputer

from xgboost import XGBRegressor

train_path = r"C:\Users\cbattle\Desktop\train.csv"

test_path = r"C:\Users\cbattle\Desktop\test.csv"

out_path = r"C:\Users\cbattle\Desktop\out.csv"

# 读入数据

train = pd.read_csv(train_path)

test = pd.read_csv(test_path)

print('train:',train.shape)

print('test :',test.shape)

# 划分X,y

X = train.drop(['Id','SalePrice'],axis=1)

y = train['SalePrice']

Xtest = test.drop(['Id'],axis=1)

print('X    :',X.shape)

print('y    :',y.shape)

print('Xtest:',Xtest.shape)

# for col in X:

#     print(X[col].dtype,col)

key = [col for col in X

      if X[col].dtype in ['int64','float64']

      or X[col].dtype == 'object' and X[col].nunique()<10

      ]

X = X[key]

Xtest = Xtest[key]

# 独热编码

key = [col for col in X

      if X[col].dtype in ['int64','float64']

      or X[col].dtype == 'object' and X[col].nunique()<10

      ]

X = X[key]

Xtest = Xtest[key]

print(X.shape, Xtest.shape)

X = pd.get_dummies(X)

Xtest = pd.get_dummies(Xtest)

X, Xtest = X.align(Xtest, join = 'left', axis=1)

print(X.shape, Xtest.shape)

# 填补空值

my_imputer = Imputer()

X = my_imputer.fit_transform(X)

Xtest = my_imputer.transform(Xtest)

print(X.shape, Xtest.shape)

# 决策树

# decisionTree = DecisionTreeRegressor()

# decisionTree.fit(X,y)

# ans = decisionTree.predict(Xtest)

# XG boost

xgb = XGBRegressor()

xgb.fit(X,y,verbose=False)

ans = xgb.predict(Xtest)

# my_model = XGBRegressor(n_estimators=1000)

# my_model.fit(train_X, train_y, early_stopping_rounds=5,

#              eval_set=[(val_X, val_y)], verbose=False)

# ans = my_model.predict(Xtest)

# 输出

myAns = pd.DataFrame({'Id':test['Id'],'SalePrice':ans})

myAns.to_csv(r"C:\Users\cbattle\Desktop\out.csv", index=False)

print('ok')