Load the data set.

  Take features and target variables as an array.

   Because in deep learning with keras everything should be in array.

  Initialize sequence padding and one-hot encoder constructor from keras library.

  Fit the data into the constructor.

  Transform the independent and target variables.

#import necessary libraries
import pandas as pd
import numpy as np
import keras
from keras.preprocessing import sequence
from keras.utils import np_utils
from sklearn.model_selection import train_test_split

#load the sample data from csv file
data = pd.read_csv(‘/home/soft50/soft50/Sathish/practice/iris.csv’)

#Make it as a data frame
df = pd.DataFrame(data)

#feature selection
X = np.array(df.iloc[:,0:4])
y = np.array(df.iloc[:,5])

print(“Before padding\n\n”,X)

#Split the data into train and testing
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.1, random_state=0)

#Sequence padding
X_train = sequence.pad_sequences(X_train,maxlen=40)
X_test = sequence.pad_sequences(X_test,maxlen=40)

print(“After padding\n\n”,X_train,”\n\n”,X_test)

#One-hot encoding
Y_train = np_utils.to_categorical(Y_train,num_classes=3)
Y_test = np_utils.to_categorical(Y_test,num_classes=3)

print(“After Encoding\n\n”,Y_train,”\n\n”,Y_test)