Machine learning practice - neural network-21

Machine learning practice - neural network - Handwritten digit recognition project

# pip install scikit-learn --upgrade
from sklearn.neural_network import MLPClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report,confusion_matrix

digits = load_digits()# Load data 
x_data = digits.data # data 
y_data = digits.target # label 

#  Standardization 
scaler = StandardScaler()
x_data = scaler.fit_transform(x_data)
x_train,x_test,y_train,y_test = train_test_split(x_data,y_data) # Split data 1/4 For test data ,3/4 For training data 

mlp = MLPClassifier(hidden_layer_sizes=(100,50) ,max_iter=500)
mlp.fit(x_train,y_train )

predictions = mlp.predict(x_test)

print(confusion_matrix(y_test,predictions))

Machine learning practice - neural network - Wine classification

import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import classification_report,confusion_matrix

#  Load data 
data = np.genfromtxt("wine_data.csv", delimiter=",")
x_data = data[:,1:]
y_data = data[:,0]
print(x_data.shape)
print(y_data.shape)

#  Data segmentation 
x_train, x_test, y_train, y_test = train_test_split(x_data, y_data)

#  Data standardization 
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.fit_transform(x_test)

#  modeling 
mlp = MLPClassifier(hidden_layer_sizes=(100,50),max_iter=500)
#  Training 
mlp.fit(x_train,y_train)

#  assessment 
predictions = mlp.predict(x_test)
print(classification_report(y_test,predictions))

print(confusion_matrix(y_test,predictions))