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diff --git a/Machine Learning Scripts /LSTM.png b/Machine Learning Scripts /LSTM.png
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diff --git a/Machine Learning Scripts /LSTM_model.py b/Machine Learning Scripts /LSTM_model.py
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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import yfinance as yf
+import math
+from keras import Sequential
+from sklearn.preprocessing import MinMaxScaler
+from keras.layers import LSTM, Dropout, Dense
+
+def LSTM_model(symbol):
+    stock = yf.Ticker(symbol)
+    df = stock.history(period="max")
+    df = df[['Open',  'High',  'Low',  'Close', 'Volume']]
+    train_length = math.floor(0.1*len(df)) 
+    train = df[:-train_length]
+    test = df[-train_length:]
+    training_set = train.iloc[:, 1: 2].values
+    #print(training_set)
+    sc = MinMaxScaler(feature_range = (0, 1))
+    training_set_scaled = sc.fit_transform(training_set)
+    #print(training_set_scaled)
+    X_train = []
+    y_train = []
+    for i in range(60, len(training_set_scaled)):
+        X_train.append(training_set_scaled[i-60: i, 0])
+        y_train.append(training_set_scaled[i, 0])
+    X_train, y_train = np.array(X_train), np.array(y_train) 
+    X_train = np.reshape(X_train, newshape = (X_train.shape[0], X_train.shape[1], 1))
+    regressor = Sequential()
+    regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
+    regressor.add(Dropout(rate = 0.2))
+    regressor.add(LSTM(units = 50, return_sequences = True))
+    regressor.add(Dropout(rate = 0.2))
+    ##add 3rd lstm layer
+    regressor.add(LSTM(units = 50, return_sequences = True))
+    regressor.add(Dropout(rate = 0.2))
+    ##add 4th lstm layer
+    regressor.add(LSTM(units = 50, return_sequences = False))
+    regressor.add(Dropout(rate = 0.2))
+    regressor.add(Dense(units = 1))
+    regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')
+    regressor.fit(x = X_train, y = y_train, batch_size = 32, epochs = 10)
+    real_stock_price = test.iloc[:, 1: 2].values
+    dataset_total = pd.concat((train['Open'],test['Open']), axis = 0)
+    inputs = dataset_total[len(dataset_total)-len(test)- 60: ].values
+    inputs = inputs.reshape(-1, 1)
+    inputs = sc.transform(inputs)
+    X_test = []
+    for i in range(60, len(inputs)): 
+        X_test.append(inputs[i-60: i, 0])
+    X_test = np.array(X_test)
+    X_test = np.reshape(X_test, newshape = (X_test.shape[0],X_test.shape[1], 1))
+    predicted_stock_price = regressor.predict(X_test)
+    predicted_stock_price = sc.inverse_transform(predicted_stock_price)
+    plt.plot(real_stock_price, color = 'red', label = 'Real price')
+    plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted price')
+    plt.title('{} price prediction'.format(symbol))
+    plt.xlabel('Time')
+    plt.ylabel('Price')
+    plt.legend()
+    plt.show()
+
+LSTM_model("AZPN")
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