Stock Market Predictions with LSTM in Python

Stock Market Predictions with LSTM in Python is a tutorial that shows how to create a three-layer LSTM model using TensorFlow and Keras. Here is a snippet of how to define the model:

# Define the LSTM model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(LSTM(units=50))
model.add(Dense(1)) 

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

Stock Market Analysis + Prediction using LSTM is a notebook that shows how to load stock market data from Yahoo finance using yfinance module, how to explore and visualize time-series data using pandas, matplotlib, and seaborn, how to measure the correlation between stocks, how to measure the risk of investing in a particular stock, and how to use LSTM (Long Short-Term Memory) model for predicting future stock prices. Here is a snippet of how to create and train an LSTM model for Tesla stock price prediction:

# Create an LSTM network
lstm_model = Sequential()
lstm_model.add(LSTM(units=50,
                    return_sequences=True,
                    input_shape=(x_train.shape[1], 1)))
lstm_model.add(Dropout(0.2))
lstm_model.add(LSTM(units=50))
lstm_model.add(Dropout(0.2))
lstm_model.add(Dense(1))

# Compile and fit the model
lstm_model.compile(loss='mean_squared_error', optimizer='adam')
lstm_model.fit(x_train,y_train,
               epochs=100,
               batch_size=32,
               verbose=2)

Stock Market Prediction using CNN-LSTM is another notebook that shows how to use CNN-LSTM approach for predicting stock market prices. It uses Convolutional Neural Network (CNN) layers for feature extraction and Long Short-Term Memory (LSTM) layers for capturing temporal dependencies. Here is a snippet of how to define and compile the CNN-LSTM model:

# Define CNN-LSTM Model
cnn_lstm = Sequential()
cnn_lstm.add(Conv1D(filters = 64,kernel_size = 3,input_shape = (X_train.shape[1],X_train.shape[2])))
cnn_lstm.add(MaxPooling1D(pool_size = 2))
cnn_lstm.add(Flatten())
cnn_lstm.add(RepeatVector(y_train.shape[1]))
cnn_lstm.add(LSTM(200 , activation = 'relu' , return_sequences=True))
cnn_lstm.add(TimeDistributed(Dense(100 , activation='relu')))
cnn_lstm.add(TimeDistributed(Dense(1)))

# Compile CNN-LSTM Model
cnn_lstm.compile(optimizer='adam', loss='mse')

If you have any questions about this code, you can drop a line in comment.

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