<jupyter_start><jupyter_text>Stock Market Prediction And Forecasting Using Stacked LSTM<jupyter_code>### Keras and Tensorflow >2.0
### Data Collection
import pandas_datareader as pdr
key=""
import pandas as pd
df=pd.read_csv('HDFC.csv')
df.head()
df.tail()
df1=df.reset_index()['Close']
df1
import matplotlib.pyplot as plt
plt.plot(df1)
### LSTM are sensitive to the scale of the data. so we apply MinMax scaler 
import numpy as np
df1
from sklearn.preprocessing import MinMaxScaler
scaler=MinMaxScaler(feature_range=(0,1))
df1=scaler.fit_transform(np.array(df1).reshape(-1,1))
print(df1)
##splitting dataset into train and test split
training_size=int(len(df1)*0.65)
test_size=len(df1)-training_size
train_data,test_data=df1[0:training_size,:],df1[training_size:len(df1),:1]
training_size,test_size
train_data
import numpy
# convert an array of values into a dataset matrix
def create_dataset(dataset, time_step=1):
	dataX, dataY = [], []
	for i in range(len(dataset)-time_step-1):
		a = dataset[i:(i+time_step), 0]   ###i=0, 0,1,2,3-----99   100 
		dataX.append(a)
		dataY.append(dataset[i + time_step, 0])
	return numpy.array(dataX), numpy.array(dataY)
# reshape into X=t,t+1,t+2,t+3 and Y=t+4
time_step = 100
X_train, y_train = create_dataset(train_data, time_step)
X_test, ytest = create_dataset(test_data, time_step)
print(X_train.shape), print(y_train.shape)
print(X_test.shape), print(ytest.shape)
# reshape input to be [samples, time steps, features] which is required for LSTM
X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)
X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)
### Create the Stacked LSTM model
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
model=Sequential()
model.add(LSTM(50,return_sequences=True,input_shape=(100,1)))
model.add(LSTM(50,return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error',optimizer='adam')

model.summary()
model.summary()
model.fit(X_train,y_train,validation_data=(X_test,ytest),epochs=100,batch_size=64,verbose=1)
import tensorflow as tf
tf.__version__
### Lets Do the prediction and check performance metrics
train_predict=model.predict(X_train)
test_predict=model.predict(X_test)
##Transformback to original form
train_predict=scaler.inverse_transform(train_predict)
test_predict=scaler.inverse_transform(test_predict)
### Calculate RMSE performance metrics
import math
from sklearn.metrics import mean_squared_error
math.sqrt(mean_squared_error(y_train,train_predict))
### Test Data RMSE
math.sqrt(mean_squared_error(ytest,test_predict))
### Plotting 
# shift train predictions for plotting
look_back=100
trainPredictPlot = numpy.empty_like(df1)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict
# shift test predictions for plotting
testPredictPlot = numpy.empty_like(df1)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(train_predict)+(look_back*2)+1:len(df1)-1, :] = test_predict
# plot baseline and predictions
plt.plot(scaler.inverse_transform(df1))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()
len(test_data)
x_input=test_data[1993:].reshape(1,-1)
x_input.shape

temp_input=list(x_input)
temp_input=temp_input[0].tolist()
temp_input
# demonstrate prediction for next 10 days
from numpy import array

lst_output=[]
n_steps=120
i=0
while(i<30):
    
    if(len(temp_input)>100):
        #print(temp_input)
        x_input=np.array(temp_input[1:])
        print("{} day input {}".format(i,x_input))
        x_input=x_input.reshape(1,-1)
        x_input = x_input.reshape((1, n_steps, 1))
        #print(x_input)
        yhat = model.predict(x_input, verbose=0)
        print("{} day output {}".format(i,yhat))
        temp_input.extend(yhat[0].tolist())
        temp_input=temp_input[1:]
        #print(temp_input)
        lst_output.extend(yhat.tolist())
        i=i+1
    else:
        x_input = x_input.reshape((1, n_steps,1))
        yhat = model.predict(x_input, verbose=0)
        print(yhat[0])
        temp_input.extend(yhat[0].tolist())
        print(len(temp_input))
        lst_output.extend(yhat.tolist())
        i=i+1
    

print(lst_output)
day_new=np.arange(1,121)
day_pred=np.arange(121,151)
import matplotlib.pyplot as plt
len(df1)
plt.plot(day_new,scaler.inverse_transform(df1[5918:]))
plt.plot(day_pred,scaler.inverse_transform(lst_output))
df3=df1.tolist()
df3.extend(lst_output)
plt.plot(df3[3700:])

df3=scaler.inverse_transform(df3).tolist()
plt.plot(df3)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13370957238984155, 'fraction_numerical': 0.029737356197091384, 'mean_word_length': 6.100154083204931, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 7, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '80671', 'license': 'MIT', 'n_tokens_mistral': 1794, 'n_tokens_neox': 1698, 'n_words': 308, 'path': 'HDFC.ipynb', 'repo_name': 'soms98/Stock-Price-Prediction-Time-Series-LSTM-Model-Keras-Tensorflow', 'size': '4607'}