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#Load data | |
import pandas as pd | |
white = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv", sep=';') | |
red = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv", sep=';') | |
#define Target | |
red['type'] = 1 | |
white['type'] = 0 | |
wines = red.append(white, ignore_index = True) | |
#split training/testing | |
from sklearn.model_selection import train_test_split | |
import numpy as np | |
# X = wines.ix[:,0:11] # Deprecated ix | |
X = wines.iloc[:,0:11] | |
y=np.ravel(wines.type) | |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size =0.33, random_state = 1) | |
#preprocess | |
from sklearn.preprocessing import StandardScaler | |
scaler = StandardScaler().fit(X_train) | |
X_train = scaler.transform(X_train) | |
X_test = scaler.transform(X_test) | |
#Keras - build model | |
from keras.models import Sequential | |
from keras.layers import Dense | |
model = Sequential() | |
model.add(Dense(12, activation = 'relu', input_shape = (11,))) | |
model.add(Dense(8, activation = 'relu')) | |
model.add(Dense(1, activation = 'sigmoid')) | |
model.summary() | |
#Keras - run model | |
model.compile(loss = 'binary_crossentropy', | |
optimizer = 'adam', | |
metrics = ['accuracy']) | |
model.fit(X_train, y_train, epochs = 20, batch_size = 5, verbose = 1) | |
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, cohen_kappa_score | |
y_pred = model.predict(X_test).astype('int') | |
score = model.evaluate(X_test, y_test, verbose = 1) | |
print('loss and accuracy:]\n',score) | |
print('\nconfusion matrix:\n',confusion_matrix(y_test, y_pred)) | |
print('\nprecision:', precision_score(y_test, y_pred)) | |
print('\nrecall:', recall_score(y_test, y_pred)) | |
print('\nF1:', f1_score(y_test, y_pred)) | |
print('\nKappa:', cohen_kappa_score(y_test, y_pred)) |
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