omchaithanyav

from __future__ import print_function
import pandas as pd
import numpy as np
import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation
from sklearn.metrics import (precision_score, recall_score,f1_score, accuracy_score)
from sklearn.preprocessing import Normalizer
import h5py

omchaithanyav

traindata = pd.read_csv('https://cainvas-static.s3.amazonaws.com/media/user_data/vomchaithany/Training.csv', header=None)
testdata = pd.read_csv('https://cainvas-static.s3.amazonaws.com/media/user_data/vomchaithany/Testing.csv', header=None)
traindata = traindata.sample(30000)
testdata = testdata.sample(6000)

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# Class count
count_class_1, count_class_0 = traindata[0].value_counts()

# Divide by class
df_train_class_0 = traindata[traindata[0] == 0]
df_train_class_1 = traindata[traindata[0] == 1]

# Oversample 0-class and concat the DataFrames of both classes
df_train_class_0_over = df_train_class_0.sample(count_class_1, replace=True)
df_train_over = pd.concat([df_train_class_1, df_train_class_0_over], axis=0)

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import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'

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X = df_train_over.iloc[:,1:5]
Y = df_train_over.iloc[:,0]
C = df_test_over.iloc[:,0]
T = df_test_over.iloc[:,1:5]

omchaithanyav

scaler = Normalizer().fit(X)
trainX = scaler.transform(X)

scaler = Normalizer().fit(T)
testT = scaler.transform(T)

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y_train = np.array(Y)
y_test = np.array(C)


X_train = np.array(trainX)
X_test = np.array(testT)

omchaithanyav

batch_size = 64
model = Sequential()
model.add(Dense(1000,input_dim=4,activation='relu'))  
model.add(Dropout(0.5))
model.add(Dense(700,activation='relu'))  
model.add(Dropout(0.5))
model.add(Dense(300,activation='relu'))  
model.add(Dropout(0.5))
model.add(Dense(100,activation='relu'))  
model.add(Dropout(0.5))

omchaithanyav

from matplotlib import pyplot as plt
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()

omchaithanyav

from matplotlib import pyplot as plt
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()