Introduction to E-Commerce Product Recommendation
- 1. Introduction to E- Commerce Product Recommendation Product recommendation engines are at the heart of modern e- commerce. By analyzing customer behavior and preferences, these systems can suggest relevant products to shoppers, driving increased engagement and sales.
- 2. Team Members: T Gnaneswar (21MIA1013) K Siva Hitesh (21MIA1018) M Amulya (21MIA1030) G Praneetha (21MIA1047) Neha Savale (21MIA1145) Raja Venkat Sai (21MIA1060)
- 3. Age = Ratio Scale Title = Typographic Scale Rating = Ordinal Scale Positive-Feedback-Count = Numerical value Division-Name = Nominal Scale Department-Name = Nominal Scale Class-Name = Nominal Scale Recommended_IND = Nominal Scale Ratio scale = A ratio scale is a type of quantitative scale that features a true zero point and equal intervals between adjacent values. This means that a zero on a ratio scale indicates a complete absence of the variable being measured. Typographic scale = A typographic scale is a system used by designers to create harmony among different font sizes, line heights, and text spacing within a project. It’s similar to a musical scale for musicians, providing a structured approach to typography that ensures consistency and visual harmony Ordinal scale = An ordinal scale is a level of measurement that allows data to be categorized and ranked in order of magnitude. However, it does not specify the exact differences between the ranks Numerical value = A numerical value refers to a quantifiable quantity represented by a number, which can be used to denote the magnitude of a particular quantity. It can be a whole number, fractional, decimal, negative, or positive Dataset attributes explanations, scales
- 4. Handling Null Values 1 Identifying Gaps Recognize missing data points that could impact the accuracy of product recommendations. 2 Imputation Techniques Employ statistical methods like mean/median imputation or machine learning models to fill in the blanks. 3 Robust Modeling Ensure recommendation algorithms can handle incomplete data and still provide valuable suggestions.
- 6. Hypothesis Testing Identify Opportunities Analyze data to uncover potential areas for improvement in the recommendation system. Formulate Hypotheses Develop testable hypotheses about changes that could enhance the customer experience. A/B Testing Rigorously test hypotheses through controlled experiments to validate their impact.
- 7. Hypothesis Testing Null Hypothesis (H0): There is no relationship between “Age” and “Recommended_IND” Alternate Hypothesis (H1): There is relationship between “Age” and “Recommended_IND” Null Hypothesis (H0): There is no relationship between “Rating” and “Recommended_IND” Alternate Hypothesis (H1): There is relationship between “Rating” and “Recommended_IND” Null Hypothesis (H0): There is no relationship between “Positive_Feedback_Count” and “Recommended_IND” Alternate Hypothesis (H1): There is relationship between “Positive_Feedback_Count” and “Recommended_IND” Null Hypothesis (H0): There is no relationship between “Division_Name” and “Recommended_IND” Alternate Hypothesis (H1): There is relationship between “Division_Name” and “Recommended_IND” Null Hypothesis (H0): There is no relationship between “Department_Name” and “Recommended_IND” Alternate Hypothesis (H1): There is relationship between “Department_Name” and
- 10. Conclusion Effective product recommendation engines are a cornerstone of modern e-commerce, delivering personalized suggestions that enhance the customer experience and drive business growth. As technology and consumer behavior continue to evolve, agile and innovative recommendation systems will be crucial for e- commerce success.
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