Classification

Classification is a technique in which we classify series of objects. Let us assume we have six categories of samples of wine and we need to make a model to classify the wine quality.

Import the libraries required for the analysis,

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense,Dropout
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder

Load the file,

df=pd.read_csv("winequality-red.csv")
df.head()
X=df.iloc[:,0:11].values
y=df.iloc[:,-1].values

Now , transform the data to be fit into a deep learning model,

from keras.utils import np_utils
encoder = LabelEncoder()
encoder.fit(y)
encoded_Y = encoder.transform(y)
# convert integers to dummy variables
# convert integers to dummy variables (i.e. one hot encoded)
dummy_y = np_utils.to_categorical(encoded_Y)
dummy_y.shape

Create a model, this model is having 11 input and 6 as the categorical shape as we have encoded it with categorical encoding( refer shape above)

def baseline_model():
# create model
    model = Sequential()
    model.add(Dense(100, input_dim=11, kernel_initializer='normal', activation='relu'))
    model.add(Dense(6, kernel_initializer='normal', activation='sigmoid'))
    # Compile model
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

Using K-Fold method,

from keras.wrappers.scikit_learn import KerasClassifier
estimator = KerasClassifier(build_fn=baseline_model, epochs=100, batch_size=100, verbose=0)
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(estimator, X, dummy_y, cv=kfold)
print("Accuracy: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
Accuracy: 56.85% (2.52%)

Using simple modeling technique,

model = Sequential()
model.add(Dense(100, input_dim=11, kernel_initializer='normal', activation='relu'))
model.add(Dense(6, kernel_initializer='normal', activation='sigmoid'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, dummy_y)
Epoch 1/10
1599/1599 [==============================] - 1s - loss: 1.3069 - acc: 0.4221         
Epoch 2/10
1599/1599 [==============================] - 0s - loss: 1.1760 - acc: 0.4909     
Epoch 3/10
1599/1599 [==============================] - 0s - loss: 1.1731 - acc: 0.4209     
Epoch 4/10
1599/1599 [==============================] - 0s - loss: 1.1622 - acc: 0.4459     
Epoch 5/10
1599/1599 [==============================] - 0s - loss: 1.1571 - acc: 0.4928     
Epoch 6/10
1599/1599 [==============================] - 0s - loss: 1.1346 - acc: 0.5003     
Epoch 7/10
1599/1599 [==============================] - 0s - loss: 1.1211 - acc: 0.5116     
Epoch 8/10
1599/1599 [==============================] - 0s - loss: 1.1210 - acc: 0.5003     
Epoch 9/10
1599/1599 [==============================] - 0s - loss: 1.1077 - acc: 0.5078     
Epoch 10/10
1599/1599 [==============================] - 0s - loss: 1.0982 - acc: 0.5009     

The Graph of the model is as:


The loss is as,

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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