PYTHON MODEL

import pandas as pd
import tensorflow as tf
import numpy as np
import os
print(os.listdir())
data = pd.read_csv('SHOPERSTOP.NS.csv')
data.head()
data = data.drop(['Date'] ,1)
#data = data.drop(['Volume'] ,1)
data.head(100)
X = data.iloc[0:150,1:5]
X_test = data.iloc[201: , 1:5]
X_test.head()
Y_test = data.iloc[201: , 4]
Y_test.head()
X.shape
Y =data.iloc[0:150 , 4]
X.head(7)
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler()
X = sc.fit_transform(X)
from tensorflow.keras.layers import Dense , Activation
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Dense(1000, input_dim=4 , activation = 'sigmoid')),
model.add(Dense(1000 , activation= 'sigmoid'))
model.add(Dense(1))
model.add(Activation('linear'))
model.compile(loss='mse',optimizer='adam' , metrics=['mse'])

history = model.fit(X,Y,validation_data = (X_test,Y_test),epochs=1500,verbose=2)
import matplotlib.pyplot as plt
plt.plot(history.history['loss'])