模型预测评估

1.数据处理

1.首先加载数据查看数据详情

2. 查看数据是否又NAN的项。

3 对有空的项进行填充

4 将数据分成训练书和测试数据

5 .对数据归一化处理

2.模型预测

多层感知机-回归模型

集成回归

线性回归

svm回归

knn 回归

决策树回归

决策树回归

随机森林回归

Adaboost回归

gbrt 回归

bagging 回归

3.模型整合

使用k折交叉验证
使用了几个较好的模型进行处理

使用result数据结果

import warnings
warnings.filterwarnings("ignore")
from sklearn import preprocessing
from sklearn.neural_network import MLPRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn import ensemble 
import pandas as pd
import math
from sklearn.model_selection import KFold



df = pd.read_excel("xxx.xlsx",encoding='utf8',index_col=0)
df=df.fillna(method='ffill')
data = df.values.astype('float')
x = data[:,1:]
y = data[:,0]
for i in range(len(y)):
    y[i] = math.log(y[i])

kf = KFold(n_splits=5,shuffle=True)

for train_index,test_index in kf.split(x):
    train_x = x[train_index]    
    test_x = x[test_index]
    train_y = y[train_index]
    test_y = y[test_index]
 
    ss_x = preprocessing.StandardScaler()
    train_x = ss_x.fit_transform(train_x)
    test_x = ss_x.transform(test_x)
    
    ss_y = preprocessing.StandardScaler()
    train_y = ss_y.fit_transform(train_y.reshape(-1,1))
    test_y = ss_y.transform(test_y.reshape(-1,1))
    
    model_mlp = MLPRegressor(solver='lbfgs',hidden_layer_sizes=(20,20,20),random_state=1)
    model_mlp.fit(train_x,train_y.ravel())
    mlp_score = model_mlp.score(test_x,test_y.ravel())
    print("sklearn多层感知器-回归模型得分",mlp_score)
    
    model_gbr = GradientBoostingRegressor(learning_rate=0.1)
    model_gbr.fit(train_x,train_y.ravel())
    gbr_score = model_gbr.score(test_x,test_y.ravel())
    print("sklearn集成-回归模型得分",gbr_score)
    
    model_br=ensemble.BaggingRegressor()
    model_br.fit(train_x,train_y)
    model_brscore = model_br.score(test_x,test_y)
    print("sklearn  bagging 回归模型得分",model_brscore)

    
    
    model_rfr=ensemble.RandomForestRegressor(n_estimators=20)

    model_rfr.fit(train_x,train_y)
    model_rfrscore = model_rfr.score(test_x,test_y)
    print("sklearn  随机森林回归模型得分",model_rfrscore)
    
    model_br=ensemble.BaggingRegressor()
    model_br.fit(train_x,train_y)
    model_brscore = model_br.score(test_x,test_y)
    print("sklearn  bagging 回归模型得分",model_brscore)

使用处理过年限的数据的得分

参考：https://www.jianshu.com/p/f92d9ac14692