chatGPT for College Assignment: Predicting Stock Prices using LSTM Model

Accurately predicting stock prices can be challenging, as it involves understanding and analyzing various market trends and factors. In this blog post, we will explore how chatGPT, a large language model developed by OpenAI, can be used to predict the stock price of Indonesian Astra Otoparts (AUTO:IDX) based on its historical price data for the past 5 years (2016-2021).

I have a college assignment on “Business Intelligence” subject to use machine learning and deep learning model to predict certain outcomes. The professor stated that the data has lasted a long time and is still updated until now.

With the help of some literature, I tried to complete the assignment by creating a model to predict stock prices based on historical prices. I followed the guide at https://www.analyticsvidhya.com for the code example and explanations. The example was easy enough to follow, but I am facing a challenge in predicting prices for tomorrow, the day after, a week, or a month from now. To get a code example, I asked for input from chatGPT from OpenAI.

First, let’s start by understanding what chatGPT is and how it works. chatGPT is a variant of the GPT (Generative Pre-training Transformer) language model, which was trained on a dataset of billions of words and is capable of generating human-like text. It can be used to generate text in various languages and fine-tuned for specific tasks such as translation, question answering, and language modeling.

Here is the result:

Question and Answer

Here is a sample python code that demonstrates how chatGPT can be used to predict stock prices using the historical data for Indonesian Astra Otoparts (AUTO:IDX):

Sample code:

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM

# Load the stock data
df = pd.read_csv('Tesla_stock_data.csv')

# Select the 'Close' column as the feature to predict
X = df[['Close']].values

# Scale the data using MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
X = scaler.fit_transform(X)

# Create a function to split the data into training and testing sets
def split_data(X, split=0.8):
    # Calculate the split index
    split_index = int(X.shape[0] * split)
    # Split the data
    X_train, X_test = X[:split_index], X[split_index:]
    return X_train, X_test

# Split the data into training and testing sets
X_train, X_test = split_data(X)

# Convert the data into a 3D array to feed into the LSTM model
def create_dataset(X, window_size=1):
    X_3d = []
    for i in range(X.shape[0] - window_size):
        X_3d.append(X[i:i+window_size])
    return np.array(X_3d)

X_train = create_dataset(X_train)
X_test = create_dataset(X_test)

# Define 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')

# Train the model on the training data
model.fit(X_train, X_train, epochs=100, batch_size=32, shuffle=False)

# Use the model to predict the next week's stock prices
predictions = model.predict(X_test)

# Inverse transform the predictions to get the actual stock prices
predictions = scaler.inverse_transform(predictions)

# Print the predictions
print(predictions)

and the instruction:

Because I don’t have TESLA stock price data (although it’s easily obtained), but I do have data for one stock in Indonesia, AUTO:IDX (Astra Otoparts), I am using this data as a sample to process and test whether the python code is valid or not.

And running sample Python code using Google colab:

The example shows that chatGPT can be used to help the learning process. Not just to copy and paste but to serve as a basis for understanding and further knowledge development. I may often use chatGPT to help me learn many things and accelerate my understanding, especially for college assignments 😊.