click here to download project abstract/base paper of supervised machine learning
At data pro , we provide final year projects with source code in python for computer science students in Hyderabad , Visakhapatnam.
ABSTRACT
In the dynamic landscape of e-commerce, identifying the most effective promotional platform is paramount for enhancing revenue generation. This study is to predict the optimal e-commerce promotional platform, crucial for adapting to evolving consumer behaviors influenced by technology. Understanding user preferences is crucial for strategic decision-making in e-commerce industries.
The study employs supervised machine learning, particularly regression models, to analyze a diverse dataset including user engagement, product preferences, and promotional platform effectiveness. Utilizing historical data on interactions like click-through rates and purchase patterns, it aims to reveal patterns influencing promotional platform success.
The methodology involves the careful curation and preprocessing of e-commerce data, ensuring its relevance and reliability.Employing regression models, the study utilizes linear regression and potentially more sophisticated algorithms to establish predictive models. Iterative processes fine-tune these models, taking into account factors such as user visit frequency, demographic information, and historical promotional performance.
Thus the results of this investigation offer actionable insights for e-commerce businesses, providing a data-driven approach to strategically promote products on platforms that yield the highest returns. The research contributes to the existing knowledge base by showcasing the effectiveness of supervised machine learning in forecasting optimal promotional platforms.
In conclusion, this research aligns with the ever-evolving nature of e-commerce by employing supervised machine learning to forecast the efficient promotional platform. The findings aim to empower e-commerce industries with informed decision-making capabilities, facilitating both targeted promotional strategies and ultimately optimizing revenue generation. The adaptability of the approach suggests its potential applicability in diverse e-commerce scenarios, providing a foundation for future studies in this domain.
