Predicting Stock Price with help of Machine Learning in Python

StockPricePrediction.py

# First install libraries of Python
# ---Libraries--- : numpy , matplotlib , pandas , pandas_datareader , tensorflow , scikit-learn
# Import files in our Code
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas.core.algorithms import mode
import pandas_datareader as web
import datetime as dt

from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense , Dropout , LSTM

# Fetching Stock Data
stock = 'FB' # You can search for other Stocks also Like: --TSLA-- , --AAPL--
start = dt.datetime(2015,1,1)
end = dt.datetime(2021,1,1)

data = web.DataReader(stock , 'yahoo', start , end)

# Prepare Data
scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1,1))

prediction_days = 60

x_train = []
y_train = []

for x in range(prediction_days,len(scaled_data)):
    x_train.append(scaled_data[x-prediction_days:x, 0])
    y_train.append(scaled_data[x,0])

x_train , y_train = np.array(x_train) , np.array(y_train)
x_train = np.reshape(x_train,(x_train.shape[0] , x_train.shape[1] , 1))

# Build The Model
model = Sequential()
model.add(LSTM(units=50 , return_sequences=True , input_shape=(x_train.shape[1] ,1 )))
model.add(Dropout(0.2))
model.add(LSTM(units=50 , return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50))
model.add(Dropout(0.2))
model.add(Dense(units=1)) # Prediction of next closing value

model.compile(optimizer='adam',loss='mean_squared_error')
model.fit(x_train,y_train,epochs=25,batch_size=32)


''' Test The Model Accuracy on Existing Data '''

# Load Test Data
test_start = dt.datetime(2020,1,1)
test_end = dt.datetime.now()

test_data = web.DataReader(stock , 'yahoo' , test_start , test_end)
actual_prices = test_data['Close'].values

total_dataset = pd.concat((data['Close'] , test_data['Close']), axis=0)

model_inputs = total_dataset[len(total_dataset) - len(test_data) - prediction_days:].values
model_inputs = model_inputs.reshape(-1,1)
model_inputs = scaler.transform(model_inputs)


# Make Predictions on Test Data
x_test = []

for x in range(prediction_days , len(model_inputs)):
    x_test.append(model_inputs[x-prediction_days:x, 0])


x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1] , 1))

predicted_prices = model.predict(x_test)
predicted_prices = scaler.inverse_transform(predicted_prices)

# Plot The Test Predictions
plt.plot(actual_prices,color="black",label=f"Actucal {stock} Price")
plt.plot(predicted_prices, color="green", label=f"Predicted {stock} Price")
plt.title(f"{stock} Share Price")
plt.xlabel('Time')
plt.ylabel(f'{stock} Share Price')
plt.legend()
plt.show()


# Predict Next Day

real_data = [model_inputs[len(model_inputs) + 1 - prediction_days:len(model_inputs+1) , 0]]
real_data = np.array(real_data)
real_data = np.reshape(real_data, (real_data.shape[0] , real_data.shape[1],1))

prediction = model.predict(real_data)
prediction = scaler.inverse_transform(prediction)
print(f"Prediction: {prediction}")