2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 11 , Issue 1 , PP: 69-78, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Muddassar Sarfraz 1 * , Sana Ullah 2
Doi: https://doi.org/10.54216/AJBOR.110108
In the tough cell phone business, guessing phone prices right is a key but hard job for new companies. Joining different types of info to look at stock prices may help, but we need strong ways to see how phone things and their costs tie together. This study wants to make stock price checking better in the cell phone business by using ways to join info. The work looks for strong ties between many phone things like memory, camera details, and screen size and how they affect the price. To fix this, very careful work was done to clean and fix the info. The Quadratic Discriminant Analysis rule was then used, along with top classifiers, for saying what will happen. Our findings demonstrate the QDA model's ability to detect subtle patterns and nonlinear correlations in the mobile phone data set. The model's resilience and predictive ability are demonstrated through visualizations such as ROC AUC and Precision-Recall curves. Comparative analyses with current approaches highlight the higher performance of the suggested data fusion approach. The use of QDA in data fusion models demonstrates its versatility in capturing complicated interactions, resulting in nuanced insights into mobile phone price factors. This study adds an improved prediction framework for mobile phone price analysis, which is critical for new enterprises looking to gain a competitive advantage in the volatile mobile industry.
data fusion , Mobile Technology , Predictive Modeling , Price Estimation , Machine Learning , Market Dynamics , Decision Boundaries , Market Competitiveness.
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