'''
Transfer Learning

Test Train Split (Data Splitting half for traing and half for testing)

Training = 80%
Testing = 20%


IF (Training Accuracy > Testing Accuracy):
    THEN ==>There's Overfitting Problem


'''
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam


data = pd.read_csv('wh.csv')

x = data['Height']
y = data['Weight']

m = Sequential()
m.add(Dense(1, input_shape=(1, )))
m.compile(Adam(lr=0.8), 'mean_squared_error')



'''
m.fit(x,y,epochs=45)
yp = m.predict(x)
print(yp)

h = float(input("enter height in cm "))
hcm = h * 0.3937007874

w,b = m.get_weights()

yw = b+w*hcm
print(yw)
'''

from sklearn.model_selection import train_test_split

xtr,xts, ytr, yts = train_test_split(x,y,test_size=0.2)

m.fit(xtr,ytr,epochs=45)

ytrp = m.predict(xtr)
ytsp = m.predict(xts)

from sklearn.metrics import r2_score

print("{:0.3f}".format(r2_score(ytr,ytrp))) #Smaller
print("{:0.3f}".format(r2_score(yts,ytsp))) #Bigger

===================================================================


'''
Transfer Learning

Test Train Split (Data Splitting half for traing and half for testing)

Training = 80%
Testing = 20%


IF (Training Accuracy > Testing Accuracy):
    THEN ==>There's Overfitting Problem

pip install sklearn

Classification  =   Single      or     Multiple
                    Sigmoid            Softmax


'''
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam


data = pd.read_csv("visit.csv")

x = data['Time']
y = data['Buy']

m = Sequential()
m.add(Dense(1, input_shape=(1, ), activation='sigmoid'))
m.compile(Adam(lr=0.5), 'binary_crossentropy')