LSTM Model For Apple Stock Prediction

In this technical exploration, we apply a Long Short-Term Memory (LSTM) model to predict Apple Inc.’s stock prices, leveraging Python’s robust data science ecosystem. Key steps involve importing libraries like Numpy, Pandas, and hvplot for data handling and visualization, followed by data preprocessing which includes reading, cleaning, and feature extraction from Apple’s stock data (AAPL.csv) with Twitter sentiment scores. We then employ window-based feature engineering, split the data for training and testing, and standardize it using MinMaxScaler. The LSTM model, built using Keras, undergoes training and evaluation using metrics such as Root Mean Squared Error and R-squared. The process culminates in a visual comparison of predicted versus actual stock values, showcasing LSTM’s efficacy in financial time series prediction.

#Importing Libraries
import numpy as np
import pandas as pd
import hvplot.pandas
%matplotlib inline
from sklearn import metrics

The Python code snippet is a preparatory step in setting up an environment for algorithmic trading analysis. It begins by importing various libraries and functionalities that are pivotal for data manipulation, analysis, and visualization. First, numpy imported as np is a fundamental package for numerical computing in Python. Its widely used in algorithmic trading for handling large multidimensional arrays and matrices, alongside a collection of mathematical functions to operate on these arrays. Next, pandas imported as pd is a library providing high-performance, easy-to-use…