Update app.py

app.py

@@ -1,70 +1,78 @@
-import numpy as np 
-import pandas as pd 
-import matplotlib.pyplot as plt 
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
 from sklearn.preprocessing import MinMaxScaler
 from keras.models import Sequential
-from keras.layers import Dense,LSTM,Dropout
+from keras.layers import Dense, LSTM, Dropout
+
 data = pd.read_csv('Google_train_data.csv')
-data["Close"]=pd.to_numeric(data.Close,errors='coerce')
+data["Close"] = pd.to_numeric(data.Close, errors='coerce')
 data = data.dropna()
-trainData = data.iloc[:,4:5].values
-sc = MinMaxScaler(feature_range=(0,1))
+trainData = data.iloc[:, 4:5].values
+
+sc = MinMaxScaler(feature_range=(0, 1))
 trainData = sc.fit_transform(trainData)
+
 X_train = []
 y_train = []
 
-for i in range (60,1149): #60 : timestep // 1149 : length of the data
-    X_train.append(trainData[i-60:i,0]) 
-    y_train.append(trainData[i,0])
+for i in range(60, 1149):  # 60: timestep // 1149: length of the data
+    X_train.append(trainData[i - 60:i, 0])
+    y_train.append(trainData[i, 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))
-model = Sequential()
+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))
 
-model.add(LSTM(units=100, return_sequences = True, input_shape =(X_train.shape[1],1)))
+model = Sequential()
+model.add(LSTM(units=100, return_sequences=True, input_shape=(X_train.shape[1], 1)))
 model.add(Dropout(0.2))
 
-model.add(LSTM(units=100, return_sequences = True))
+model.add(LSTM(units=100, return_sequences=True))
 model.add(Dropout(0.2))
 
-model.add(LSTM(units=100, return_sequences = True))
+model.add(LSTM(units=100, return_sequences=True))
 model.add(Dropout(0.2))
 
-model.add(LSTM(units=100, return_sequences = False))
+model.add(LSTM(units=100, return_sequences=False))
 model.add(Dropout(0.2))
 
-model.add(Dense(units =1))
-model.compile(optimizer='adam',loss="mean_squared_error")
-hist = model.fit(X_train, y_train, epochs = 20, batch_size = 32, verbose=2)
+model.add(Dense(units=1))
+
+model.compile(optimizer='adam', loss="mean_squared_error")
+hist = model.fit(X_train, y_train, epochs=20, batch_size=32, verbose=2)
+
 plt.plot(hist.history['loss'])
 plt.title('Training model loss')
 plt.ylabel('loss')
 plt.xlabel('epoch')
 plt.legend(['train'], loc='upper left')
 plt.show()
+
 testData = pd.read_csv('Google_test_data.csv')
-testData["Close"]=pd.to_numeric(testData.Close,errors='coerce')
+testData["Close"] = pd.to_numeric(testData.Close, errors='coerce')
 testData = testData.dropna()
-testData = testData.iloc[:,4:5]
-y_test = testData.iloc[60:,0:].values 
-#input array for the model
-inputClosing = testData.iloc[:,0:].values 
+testData = testData.iloc[:, 4:5]
+y_test = testData.iloc[60:, 0:].values
+
+# input array for the model
+inputClosing = testData.iloc[:, 0:].values
 inputClosing_scaled = sc.transform(inputClosing)
-inputClosing_scaled.shape
+
 X_test = []
 length = len(testData)
 timestep = 60
-for i in range(timestep,length):  
-    X_test.append(inputClosing_scaled[i-timestep:i,0])
+
+for i in range(timestep, length):
+    X_test.append(inputClosing_scaled[i - timestep:i, 0])
+
 X_test = np.array(X_test)
-X_test = np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))
 y_pred = model.predict(X_test)
 predicted_price = sc.inverse_transform(y_pred)
-plt.plot(y_test, color = 'red', label = 'Actual Stock Price')
-plt.plot(predicted_price, color = 'green', label = 'Predicted Stock Price')
+
+plt.plot(y_test, color='red', label='Actual Stock Price')
+plt.plot(predicted_price, color='green', label='Predicted Stock Price')
 plt.title('Google stock price prediction')
 plt.xlabel('Time')
 plt.ylabel('Stock Price')
 plt.legend()
 plt.show()
-