title: How to make a new prediction
body: <p>Let me explain, i'm working with an Artificial Neural Network.
  This model has 15 variables, 14 independents and one dependent.
  In the independent variables i've 3 categorical variables 
  <code>(day of week, month, direction(north,south, etc...))</code>.
  I already enconde them <code>(monday = 1, tuesday = 2, and so...),</code> 
  also i hot encode them 
  <code>(monday = [1,0,0,0], tuesday = [0,1,0,0])</code>.</p>
  
  <p>My question is, How can i make a prediction with new values, somethig like this.</p>
  
  <pre><code>X=['Monday','January','South']
  </code></pre>
  
  <p>Here is the code</p>
  
  <pre><code># Classification template
  
  # Importing the libraries
  import numpy as np
  import pandas as pd
  
  # Importing the dataset
  
  dataset = pd.read_csv('clean.csv')
  
  X = dataset.iloc[:, [4,5,6,9,12,15,16]].values
  y = dataset.iloc[:, 14].values
  
  #Encoding categorical Data
  from sklearn.preprocessing import LabelEncoder, OneHotEncoder
  labelenconder_X = LabelEncoder()
  X[:,1] = labelenconder_X.fit_transform(X[:,1])
  
  labelenconder_X_2 = LabelEncoder()
  X[:,2] = labelenconder_X_2.fit_transform(X[:,2])
  
  labelenconder_X_7 = LabelEncoder()
  X[:,4] = labelenconder_X_7.fit_transform(X[:,4])
  
  labelenconder_X_9 = LabelEncoder()
  X[:,5] = labelenconder_X_9.fit_transform(X[:,5])
  
  labelenconder_X_10 = LabelEncoder()
  X[:,6] = labelenconder_X_10.fit_transform(X[:,6])
  
  onehotencoder = OneHotEncoder(categorical_features=[1,2,4,5,6])
  
  X = onehotencoder.fit_transform(X).toarray()
  
  X = X[:, 1:]
  
  
  
  # Splitting the dataset into the Training set and Test set
  from sklearn.cross_validation import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
  
  # Feature Scaling
  #from sklearn.preprocessing import StandardScaler
  #sc = StandardScaler()
  #X_train = sc.fit_transform(X_train)
  #X_test = sc.transform(X_test)
  
  # Fitting classifier to the Training set
  # Create your classifier here
  import keras
  from keras.models import Sequential
  from keras.layers import Dense
  
  
  classifier = Sequential()
  
                      #INPUT LAYER AND HIDDEN LAYER
  classifier.add(Dense(units = 5, kernel_initializer = 'uniform', activation = 'relu', input_dim =9))
  
                      #ADDING SECOND HIDDEN LAYER
  classifier.add(Dense(units = 5, kernel_initializer = 'uniform', activation =  'relu'))
  
                      #adding output node 
  classifier.add(Dense(units= 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
  
                      #Applygin Stochasting Gradient Descent
  
  classifier.compile(optimizer='adam', loss = 'binary_crossentropy', metrics=['accuracy'])
  
  classifier.fit(X_train, y_train, batch_size =28, epochs = 100)
  
  
  classifier.save('ANN2.h5')
  model = keras.models.load_model('ANN2.h5')
  y_predict = model.predict(X_test)
  y_predict = (y_predict &gt; 0.40)
  </code></pre>