Stock price trend forecasting using machine learning
By: Vishali, M
.
Publisher: Gurugram IOSR - International Organization of Scientific Research 2022Edition: Vol.24(5), Sep-Oct.Description: 3-8p.Subject(s): Computer EngineeringOnline resources: Click here
In:
IOSR Journal of Computer Engineering (IOSR-JCE)Summary: Generally, predicting how the stock market will perform is one of the most difficult things to do. It can be
described as one of the most critical process to predict that. This is a very complex task and has uncertainties.
To prevent this problem in One of the most interesting (or perhaps most profitable) time series data using
machine learning techniques. Hence, stock price prediction has become an important research area. The aim is
to predict machine learning based techniques for stock price prediction results in best accuracy. The analysis of
dataset by supervised machine learning technique(SMLT) to capture several information’s like, variable
identification, uni-variant analysis, bi-variant and multi-variant analysis, missing value treatments and analyze
the data validation, data cleaning/preparing and data visualization will be done on the entire given dataset. To
propose a machine learning-based method to accurately predict the stock price Index value by prediction results
in the form of stock price increase or stable state best accuracy from comparing supervise classification
machine learning algorithms. Additionally, to compare and discuss the performance of various machine
learning algorithms from the given transport traffic department dataset with evaluation of GUI based user
interface stock price prediction by attributes. Dataset with evaluation classification report, identify the
confusion matrix and to categorizing data from priority and the result shows that the effectiveness of the
proposed machine learning algorithm technique can be compared with best accuracy with precision, Recall and
F1 Score.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2023-0073 |
Generally, predicting how the stock market will perform is one of the most difficult things to do. It can be
described as one of the most critical process to predict that. This is a very complex task and has uncertainties.
To prevent this problem in One of the most interesting (or perhaps most profitable) time series data using
machine learning techniques. Hence, stock price prediction has become an important research area. The aim is
to predict machine learning based techniques for stock price prediction results in best accuracy. The analysis of
dataset by supervised machine learning technique(SMLT) to capture several information’s like, variable
identification, uni-variant analysis, bi-variant and multi-variant analysis, missing value treatments and analyze
the data validation, data cleaning/preparing and data visualization will be done on the entire given dataset. To
propose a machine learning-based method to accurately predict the stock price Index value by prediction results
in the form of stock price increase or stable state best accuracy from comparing supervise classification
machine learning algorithms. Additionally, to compare and discuss the performance of various machine
learning algorithms from the given transport traffic department dataset with evaluation of GUI based user
interface stock price prediction by attributes. Dataset with evaluation classification report, identify the
confusion matrix and to categorizing data from priority and the result shows that the effectiveness of the
proposed machine learning algorithm technique can be compared with best accuracy with precision, Recall and
F1 Score.
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