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Introduction

In today’s competitive market, businesses are constantly seeking ways to enhance their customer experience and increase sales. One popular method is through personalized product recommendations, which can lead to higher customer engagement and conversion rates. Machine learning, a subset of artificial intelligence, has emerged as a powerful tool for creating predictive models that can recommend products to customers based on their preferences and behavior. This thesis explores the use of machine learning for predictive product recommendations and aims to provide insights into how businesses can leverage this technology to improve their marketing strategies.

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter Two: Literature Review
2.1 Overview of Machine Learning
2.2 Predictive Product Recommendations
2.3 Personalization in Marketing
2.4 Collaborative Filtering
2.5 Content-Based Filtering
2.6 Hybrid Recommender Systems
2.7 Evaluation Metrics for Recommender Systems
2.8 Case Studies on Predictive Product Recommendations
2.9 Challenges and Future Directions in Recommender Systems

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Engineering
3.5 Model Selection
3.6 Model Training
3.7 Model Evaluation
3.8 Performance Metrics

Chapter Four: Discussion of Findings
4.1 Analysis of Predictive Models
4.2 Comparison of Different Recommender Systems
4.3 Impact of Personalization on Customer Engagement
4.4 Business Implications of Predictive Product Recommendations
4.5 Addressing Challenges in Implementing Recommender Systems

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Businesses
5.4 Future Research Directions
5.5 Conclusion

Thesis Overview:

Machine learning has revolutionized the way businesses operate, especially in the realm of marketing and customer engagement. This thesis focuses on the application of machine learning in predictive product recommendations, where algorithms are used to suggest products to customers based on their preferences and behavior. The objective of this study is to explore the effectiveness of different recommender systems in improving customer engagement and driving sales.

Chapter One provides an introduction to the research topic, outlining the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter Two conducts a comprehensive review of the existing literature on machine learning, predictive product recommendations, personalization in marketing, collaborative filtering, content-based filtering, hybrid recommender systems, evaluation metrics, and case studies.

Chapter Three delves into the research methodology, detailing the research design, data collection, preprocessing, feature engineering, model selection, training, evaluation, and performance metrics. Chapter Four presents a thorough discussion of the findings, analyzing predictive models, comparing recommender systems, evaluating the impact of personalization on customer engagement, discussing business implications, and addressing challenges in implementation.

Chapter Five concludes the thesis with a summary of findings, contributions to the field, implications for businesses, future research directions, and a final conclusion. Overall, this thesis aims to provide valuable insights into the use of machine learning for predictive product recommendations and offer practical recommendations for businesses looking to enhance their marketing strategies.

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