A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3
A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2
A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3
Study of Security Mechanisms to Create a Secure Cloud in a Virtual Environment with the Support of Cloud Service Providers
Volume-2 | Issue-3
Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2
Fault Diagnosis in Hybrid Renewable Energy Sources with Machine Learning Approach
Volume-3 | Issue-3
Secure and Optimized Cloud-Based Cyber-Physical Systems with Memory-Aware Scheduling Scheme
Volume-2 | Issue-3
Stochastic Geometry and Performance Analysis of Large Scale Wireless Networks
Volume-3 | Issue-3
Computer Vision on IOT Based Patient Preference Management System
Volume-2 | Issue-2
Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4
A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3
Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4
A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3
Speedy Detection Module for Abandoned Belongings in Airport Using Improved Image Processing Technique
Volume-3 | Issue-4
Deployment of Artificial Intelligence with Bootstrapped Meta-Learning in Cyber Security
Volume-4 | Issue-3
Design an Early Detection and Classification for Diabetic Retinopathy by Deep Feature Extraction based Convolution Neural Network
Volume-3 | Issue-2
Design of an Intelligent Approach on Capsule Networks to Detect Forged Images
Volume-3 | Issue-3
Future Challenges of the Internet of Things in the Health Care Domain - An Overview
Volume-3 | Issue-4
Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2
A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2
Volume - 5 | Issue - 2 | june 2023
Published
05 June, 2023
As traders, investors, and analysts try to decide whether to buy, sell, or hold Apple Inc. shares, Apple Stock Prediction is a crucial component of the financial market. It is difficult to forecast the future value of Apple's stock due to market volatility and a variety of unknown events, including shifts in customer tastes, political unpredictability, and global economic trends. Therefore, it is essential to use a variety of techniques to find the most effective strategy for forecasting Apple's stock price. To ascertain the intrinsic value of a stock, fundamental analysis examines financial statements, market patterns, and economic conditions. This method looks at the sales, profit margins, and cash flow of Apple Inc. as well as its overall financial performance. On the other hand, technical analysis examines past market data, such as price and volume, to spot patterns and trends that can predict future price movements. Charts, graphs, and other visual aids are used in this strategy to pinpoint potential entry and exit positions for trading Apple's stock. Multi-Layer Perceptron is a kind of artificial neural network that mimics the actions of the human brain and has been successfully used to analyze large amounts of complex data. In contrast, XGBoost is a machine learning algorithm that makes predictions using previous data, making it perfect for predicting the future movement of Apple's stock.
KeywordsStock prediction Apple stock price prediction Machine Learning research
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