### Q1: What is the primary objective of data cleaning in AI projects, and how does it impact model performance?  
### Q2: Describe a moderate technique for handling missing values in a dataset and explain its significance in data preprocessing.  
### Q3: What advanced approach would you recommend for treating outliers in datasets, and why is it crucial for AI projects?  

### Q4: How can data cleaning influence business decisions when analyzing customer feedback?  
### Q5: In what situations might you prefer to use pandas for data preprocessing over other tools like Tableau Prep?  
### Q6: Explain how encoding categorical variables can enhance the effectiveness of machine learning algorithms.  

### Q7: What are some effective communication strategies for presenting data preprocessing results to non-technical stakeholders?  
### Q8: How would you explain the process of normalization to a colleague unfamiliar with data preprocessing techniques?  
### Q9: What challenges might arise when discussing technical data cleaning methods with team members lacking a technical background, and how can you overcome them?