ValueError: Setting a random_state has no effect since shuffle is False. You should leave random_state to its default (None), or set shuffle=True.

Original Code :

# Multiplayer Perceptron Model for Boston House Problem
# Regression Example With Boston Dataset : Baseline 
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# load Dataset
dataframe = pd.read_csv("housing.csv",delim_whitespace=True,header=None)
dataset = dataframe.values

# split into input (X) and output (Y) variables
X = dataset[:,0:13]
Y = dataset[:,13]
# define base model
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(13, input_dim=13, kernel_initializer='normal',activation='relu'))
    model.add(Dense(1,kernel_initializer='normal'))
    # Compile model
    model.compile(loss='mean_squared_error',optimizer='adam')
    return model

# fix random seed for reproducibility
seed = 7
np.random.seed(seed)

# evaluate model with standardized dataset
estimator = KerasRegressor(build_fn=baseline_model,epochs=100, batch_size=5, verbose=0)
kfold = KFold(n_splits=10, random_state=seed)
results = cross_val_score(estimator, X, Y, cv=kfold)
print("Baseline: %.2f (%.2f) MSE" %(results.mean(),results.std()))

Modified Code , kfold = KFold(n_splits=10, random_state=seed, shuffle=True)

# Multiplayer Perceptron Model for Boston House Problem
# Regression Example With Boston Dataset : Baseline 
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# load Dataset
dataframe = pd.read_csv("housing.csv",delim_whitespace=True,header=None)
dataset = dataframe.values

# split into input (X) and output (Y) variables
X = dataset[:,0:13]
Y = dataset[:,13]
# define base model
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(13, input_dim=13, kernel_initializer='normal',activation='relu'))
    model.add(Dense(1,kernel_initializer='normal'))
    # Compile model
    model.compile(loss='mean_squared_error',optimizer='adam')
    return model

# fix random seed for reproducibility
seed = 7
np.random.seed(seed)

# evaluate model with standardized dataset
estimator = KerasRegressor(build_fn=baseline_model,epochs=100, batch_size=5, verbose=0)
kfold = KFold(n_splits=10, random_state=seed, shuffle=True)
results = cross_val_score(estimator, X, Y, cv=kfold)
print("Baseline: %.2f (%.2f) MSE" %(results.mean(),results.std()))