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[notice] A new release of pip is available: 23.2 -> 23.2.1
[notice] To update, run: python.exe -m pip install --upgrade pip
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
# Replace 'AAPL.csv' with the CSV file containing the historical stock price data of the company you want to predict.
# The CSV file should have a column 'Date' and 'Close' containing the date and closing prices respectively.
path="C:\\Users\\Anitha\\Downloads\\archive (2)\\AAPL.csv"
data = pd.read_csv(path)
data['Date'] = pd.to_datetime(data['Date'])
data.set_index('Date', inplace=True)
# Normalize the data
scaler = MinMaxScaler()
data['Close'] = scaler.fit_transform(data['Close'].values.reshape(-1, 1))
# Split the data into training and test sets
train_size = int(len(data) * 0.8)
train_data, test_data = data[:train_size], data[train_size:]
# Create sequences of data for LSTM training
def create_sequences(data, sequence_length):
sequences = []
for i in range(len(data) - sequence_length):
sequences.append(data[i:i+sequence_length])
return np.array(sequences)
sequence_length = 10 # You can adjust this value based on the number of time steps to consider for predictions
train_sequences = create_sequences(train_data, sequence_length)
test_sequences = create_sequences(test_data, sequence_length)
# Split input and target data
X_train, y_train = train_sequences[:, :-1], train_sequences[:, -1]
X_test, y_test = test_sequences[:, :-1], test_sequences[:, -1]
# Build the LSTM model
model = Sequential()
model.add(LSTM(50, input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.summary()
# Train the model
model.fit(X_train, y_train, epochs=50, batch_size=16, verbose=2)
# ... (previous code)
# Make predictions on the test data
y_pred = model.predict(X_test)
# Rescale the predictions and actual prices back to the original scale
y_pred_rescaled = scaler.inverse_transform(y_pred)
y_test_rescaled = scaler.inverse_transform(y_test)
# Get the index of the test data
test_index = data.index[train_size + sequence_length : train_size + sequence_length + len(y_test_rescaled)]
# Plot the actual vs. predicted prices
plt.figure(figsize=(12, 6))
plt.plot(test_index, y_test_rescaled, label='Actual Price')
plt.plot(test_index, y_pred_rescaled, label='Predicted Price')
plt.legend()
plt.xlabel('Date')
plt.ylabel('Stock Price')
plt.title('Stock Price Prediction using LSTM')
plt.show()
Model: "sequential_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_4 (LSTM) (None, 50) 11400
dense_4 (Dense) (None, 1) 51
=================================================================
Total params: 11451 (44.73 KB)
Trainable params: 11451 (44.73 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
Epoch 1/50
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Epoch 2/50
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Epoch 3/50
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Epoch 4/50
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Epoch 5/50
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Epoch 6/50
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Epoch 7/50
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Epoch 8/50
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Epoch 9/50
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Epoch 10/50
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Epoch 11/50
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Epoch 12/50
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Epoch 13/50
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Epoch 14/50
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Epoch 15/50
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Epoch 16/50
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Epoch 18/50
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Epoch 19/50
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Epoch 20/50
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Epoch 21/50
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Epoch 22/50
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Epoch 23/50
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Epoch 24/50
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Epoch 25/50
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Epoch 26/50
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Epoch 27/50
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Epoch 28/50
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Epoch 29/50
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Epoch 30/50
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Epoch 31/50
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Epoch 32/50
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Epoch 33/50
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Epoch 34/50
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Epoch 35/50
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Epoch 36/50
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Epoch 37/50
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Epoch 38/50
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Epoch 39/50
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Epoch 40/50
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Epoch 41/50
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Epoch 42/50
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Epoch 43/50
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Epoch 44/50
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Epoch 45/50
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Epoch 46/50
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Epoch 47/50
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Epoch 48/50
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Epoch 49/50
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Epoch 50/50
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66/66 [==============================] - 0s 1ms/step
