Evaluation of Machine Learning Model through Stock Price Prediction Research | Chapter 7 | Contemporary Research in Business, Management and Economics Vol. 9

 

This research holds paramount importance in advancing our utilization of artificial intelligence to predict economic factors, notably within the dynamic domain of the stock market. The primary objectives focus on determining the optimal performance among the seven machine learning models employed. Stock investment prices are never still; they are always changing. It is important to stay informed on the upward or downward trends of the market to make future investments. To accustom the machine learning (ML) predictor to the multitude of possibilities that could categorize stock patterns, 7 different ML models were trained on 1250 pieces of open stock market data dating to the last 5 years by assigning weight values to all the models based on their accuracy. The neural network ends up predicting the stock price with its given data at a mediocre level at best, with MSE averages of 29.93 and 26.85 respectively. Its highest weight, tesla, ends up with only 0.013% of the total weightage. Results showed that two of the ML models, specifically the Linear Regression and the Random Sample Consensus (RANSAC) Regressor models consistently outperformed the other 5 models, both ending up with the highest weight values of around 0.5 when predicting for Amazon, Apple, and Tesla. Therefore, the RANSAC and Linear Regression models are the best models to rely on when predicting open stock market prices using ML. Future endeavors must continue this trajectory by expanding model capacities, incorporating richer data sources, and embracing AI-driven advancements to propel stock market predictability into new realms.

 

 

Author(s) Details

Navye Vedant

Inspirit AI, Sammamish, WA, USA.

 

Please see the link:- https://doi.org/10.9734/bpi/crbme/v9/892

 

Evaluation of Machine Learning Model through Stock Price Prediction Research | Chapter 7 | Contemporary Research in Business, Management and Economics Vol. 9

This research holds paramount importance in advancing our utilization of artificial intelligence to predict economic factors, notably within the dynamic domain of the stock market. The primary objectives focus on determining the optimal performance among the seven machine learning models employed. Stock investment prices are never still, they are always changing. It is important to stay informed on the upward or downward trends of the market to make future investments. To accustom the machine learning (ML) predictor to the multitude of possibilities that could categorize stock patterns, 7 different ML models were trained on 1250 pieces of open stock market data dating to the last 5 years by assigning weight values to all the models based on their accuracy. The neural network ends up predicting the stock price with its given data at a mediocre level at best, with MSE averages of 29.93 and 26.85 respectively. Its highest weight, tesla, ends up with only 0.013% of the total weightage. Results showed that two of the ML models, specifically the Linear Regression and the Random Sample Consensus (RANSAC) Regressor models consistently outperformed the other 5 models, both ending up with the highest weight values of around 0.5 when predicting for Amazon, Apple, and Tesla. Therefore, the RANSAC and Linear Regression models are the best models to rely on when predicting open stock market prices using ML. Future endeavors must continue this trajectory by expanding model capacities, incorporating richer data sources, and embracing AI-driven advancements to propel stock market predictability into new realms.

 

Author (s) Details

Navye Vedant,
Inspirit AI, Sammamish, WA, USA.

 

Please see the book here:- https://doi.org/10.9734/bpi/crbme/v9/892

Generalized Power Transformed Robust Ratio Type Estimator: An Application to COVID-19| Chapter 2 | Research Highlights in Mathematics and Computer Science Vol. 4

 The use of capacity transformed estimator results in greater precision in the estimation as these estimators determine a lesser MSE. Such accuracy is much appreciated when the culture under study is rare or secret clustered as at this moment population, securing a representative sample is difficult and when the precision of the estimate is principal. To deal with aforementioned a situation, in this place article we have proposed the first statement power metamorphosis robust type percentage estimator and from this, we have proposed sixteen power shift robust type percentage estimators. Further, the performance of these estimators has happened studied in estimating the average cases of COVID-19 in the Indian merger territory of Andaman and Nicobar islets and the state of Goa concerning some related estimators. The projected estimators resulted in larger precision.

Author(s) Details:

Rajesh Singh,
Department of Statistics, Institute of Science, Banaras Hindu University, India.

Rohan Mishra,
Department of Statistics, Institute of Science, Banaras Hindu University, India.

Please see the link here: https://stm.bookpi.org/RHMCS-V4/article/view/9156

Forecasting of Losses Due to Pod Borer, Pod Fly and Yield of Pigeonpea (Cajanus cajan) for Central Zone (CZ) of India by Using Artificial Neural Network | Chapter 08 | Current Topics in Agricultural Sciences Vol. 5

 Pigeonpea (Cajanus cajan L.) is a valuable food legume that may be cultivated with minimal inputs under rainfed conditions. Starch, protein, calcium, manganese, crude fibre, fat, trace elements, and minerals abound in pigeonpea. The need of having a timely forecast of productivity and pod damage caused by key insect-pests in pigeonpea has become a serious issue due to high domestic consumption and large losses due to major insect-pests. The built Artificial Neural Network (ANN) model for forecasting productivity (Kg/ha.) and percent pod damage by two important insect-pests, Helicoverpa armigera and Melanagromyza obtusa, of medium ripening pigeonpea in the Central Zone (CZ) of India, is reported in this paper. The model's performance was evaluated using mean squared error values, and it was determined to be suitable for the task at hand.

Author(S) Details 

Prity Kumari
Section of Agricultural Statistics, Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi-221005, India.

G. C. Mishra
Section of Agricultural Statistics, Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi-221005, India.

C. P. Srivastava
Department of Entomology and Agricultural Zoology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi-221005, India.


View Book:- https://stm.bookpi.org/CTAS-V5/article/view/5261