The two sample queries above show 15 rows each, and they cover a representative spread of the 300 rows. The sample includes students and professionals from many countries, several AI tools and a range of purposes. I'm not quoting any individual `Monthly_Income` values because that column is person-identifying. **What the sample shows** - **Each row is one surveyed user**, keyed by `User_ID`, with demographics, the AI tool and purpose, usage hours, and outcome scores (productivity, accuracy, satisfaction, time saved). - **Users:** students (such as Biology, Data Science and Mechanical Engineering students) and professionals (such as Software Developer, Technical Writer and Customer Support Specialist). - **Tools and purposes:** ChatGPT, Claude, Microsoft Copilot, Perplexity AI, Google Gemini and Meta AI, used for coding, studying, writing, research, customer support, content creation and data analysis. - **Outcome scores:** across the dataset, the average productivity score is about 5.23 on a range of 1.6 to 10.0, average satisfaction is about 6.15, and average accuracy rating is about 3.43. **Data-quality issues visible in the sample** - **Duplicates:** there are 300 rows but only 288 distinct `User_ID`s. For example, U0207 appears in both samples. - **Missing values:** - Productivity is missing in 16 rows, accuracy in 15 and satisfaction in 7. - Some rows have blank education, tool, purpose, gender, country or profession, even though the profile counts report these columns as fully populated. The blanks are probably empty strings rather than true nulls. - **Inconsistent text formats:** - Numbers carry units or symbols, such as "1.6 hrs", "16 yrs", "$20" and "6.5 hrs". - Category labels vary, such as "ChatGPT", "Chat GPT" and "chatgpt", or "copilot" and "Microsoft Copilot". - Gender appears as "Female", "F" and "FEMALE", and education as "Undergraduate" and "undergrad". - Country casing is inconsistent ("france", "canada"). - **Non-English entries:** some professions and tasks are in German, French or Portuguese. - **Implausible values:** some rows look odd, such as a 16-year-old with a Master's degree and a 17-year-old with a PhD. Most numeric columns (age, income, usage hours, AI cost, time saved, work/study hours) are stored as text, so they need cleaning before any averages or correlations. I can profile the dirty values, build a cleaned view, or produce a random sample with a different seed or filter.