Model Predictive assessment

1. Data processing

1. First load data View data details

2. Check whether the data item and the NAN.

Three pairs of items to fill empty

4 book data into training and test data

5. Data normalization

2. The model predicts

MLP - regression model

integrated return

Linear regression

svm regression

knn regression

tree regression

tree regression

Random Forest Regression

Adaboost return

gbrt return

bagging return

3. Model Integration

Using k-fold cross validation
uses several preferred process model

Use the data results result

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)

Using the processed data useful life score

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