Enhancing Stock Market Predictions with LSTM Neural Networks: A Case Study on Google Stock
In this article, we delve into the application of Long Short-Term Memory (LSTM) neural networks for forecasting the stock prices of Google.
Utilizing a dataset that spans from 2006 to 2018, we employ various Python libraries such as Numpy, Pandas, Matplotlib, and PyTorch to process and visualize the data. The approach includes normalizing the stock prices, splitting the data into training and test sets, and implementing an LSTM model. We aim to demonstrate the effectiveness of this method in predicting stock trends, while also addressing the challenges of model overfitting. This technical exploration offers insights into leveraging deep learning techniques for financial time series forecasting.
So First download the dataset from the following link:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from tqdm import tqdm
%matplotlib notebook
This code loads important tools for working with data and making graphs, such as Numpy, Pandas, Matplotlib, and PyTorch. It uses PyTorch to build and teach neural networks, which are advanced for tackling tricky machine learning tasks. It also includes the Tqdm library to show training progress visually. By using %matplotlib notebook, it allows interactive graphs in a Jupyter notebook, making it simpler to look into data. The main goal of the code is to set up the right setup for analyzing data with neural networks.
There is no file to download, all code blocks and output are in the article itself, and I have provided the link to download the dataset.