Phoenix Media Stock Price Forecasting: Econometric Case Study | Chapter 11 | Theoretical Key Issues and Practical Development Trends of China’s Digital Economy

With the rapid development of information technology and the high openness of financial markets, investment activities have become more complex and diversified. In this complex and changeable investment environment, predicting the rise and fall of stock prices has become one of the important tasks of investors and financial institutions. However, the volatility and uncertainty of the stock market make the stock price prediction unusually difficult. This paper aims to empirically analyze the prediction of the rise and fall of Phoenix Media stock through the analysis method based on the random forest model. In this study, we developed a random forest model and trained and optimized the historical Phoenix Media stock data, and then applied the model to the new data set to predict the rise and fall of Phoenix Media stock. The effectiveness and performance of the random forest model in predicting Phoenix Media stock are verified by evaluating the accuracy and stability of its prediction results and comparing them with those of traditional stock prediction methods. This study aims to make contributions from both aspects of theoretical and practical application. The structure of the article first outlines the background and objectives of the research and clarifies its value to relevant academic fields at home and abroad. Subsequently, the study conducted an in-depth system requirements analysis to clarify the requirements of the system. Then, the development environment and the tools selected were described in detail, and four key modules were successfully realized, including data preprocessing, random forest model construction, model training and verification, and prediction results presentation. In the conclusions, the study objectives, implementation steps and challenges were reviewed, and corresponding conclusions were drawn. After empirical analysis, the method based on the random forest model has certain accuracy and reliability for the rise and fall prediction of Phoenix Media stock, which provides a valuable reference for research in this field.

 

Author (s) Details

Jiming Yang
Saxo Fintech Business School, University of Sanya, Sanya, 572000, China.

 

Wenbo Lyu
Saxo Fintech Business School, University of Sanya, Sanya, 572000, China.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-48388-89-6/CH11

Chemical and Biological Profiling of Narmada River: A Random Forest Model-Based Water Quality Analysis | Chapter 2 | Recent Developments in Chemistry and Biochemistry Research Vol. 9

Water quality is a critical indicator of the health of aquatic ecosystems and the sustainability of water resources for human use. In this study, the water quality of the Narmada River was analyzed by examining key physicochemical and biological parameters that impact the river's ecosystem. Utilizing a secondary dataset spanning from 1990 to 2012, collected from the Hoshangabad district of Madhya Pradesh, seven water quality parameters were evaluated, including pH, dissolved oxygen, biochemical oxygen demand, and nitrate concentrations. The objective of this study is to assess the condition of the Narmada River water in relation to the Surface Water Quality Standards for Indian Rivers and to provide insights into the degradation trends over time.

To achieve this, a Random Forest algorithm, a robust machine learning technique, was implemented using the RapidMiner analytical tool to classify and predict water quality. This model effectively identifies patterns in the dataset, enabling a thorough understanding of the factors contributing to the declining water quality. Our findings indicate that the river’s water quality has steadily deteriorated, primarily due to increasing domestic sewage and industrial effluent discharge into the river, particularly in urban areas along its course. The results of this analysis present a critical alert to environmental policymakers and water resource managers, emphasizing the urgent need for improved water treatment facilities and regular monitoring protocols to mitigate further pollution.

This study highlights the efficacy of data-driven approaches like Random Forest in environmental monitoring and underscores the importance of integrating machine learning techniques with traditional water quality assessments to enable more informed decision-making for sustainable water management.

 

 

Author (s) Details

Mamta Gour
Department of Chemistry, Medicaps University, Indore, India.

 

Sanjeev Gour
Department of Computer Science, Medicaps University, Indore, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/rdcbr/v9/3146