I pulled two 15-row samples, and they show the dataset's structure and its messiness. **What a row looks like.** Each row is one surveyed user, a student or a professional, keyed by `User_ID`. It records: - **Who they are:** age, gender, country, education level and profession. - **How they use AI:** the AI tool, the purpose, daily usage hours, and a free-text list of tasks performed. - **Outcomes:** productivity, accuracy and satisfaction scores, time saved per week, and whether they would recommend the tool. The samples show a mix such as: - a 16-year-old Philippine student using Claude for content creation, - a Pakistani content strategist using ChatGPT for writing, - an Indian senior software developer using Microsoft Copilot for coding, - a German customer-support worker whose profession and tasks are written in German. The second sample also included `Monthly_Income`. I'm not quoting individual values because it is person-identifying data. **Quality issues visible in the sample.** These matter for any analysis: - **Duplicate IDs:** the table has 300 rows but only 288 distinct `User_ID`s. For example, U0207 appears in both samples. - **Gender labels:** there are 12 spellings of what should be a handful of categories. For instance, "Male", "male", "MALE" and "M" are all used, and 10 rows are blank. - **Tool names:** there are 17 variants, such as "ChatGPT", "Chat GPT", "chatgpt", "copilot" and "Microsoft Copilot". Some rows are blank (289 of 300 are filled). - **Education levels:** there are 17 variants, for example "undergrad" and "Undergraduate". - **Units in text:** values such as "16 yrs", "1.6 hrs", "$20" and "6.5 hrs" sit in text columns alongside plain numbers. `Age`, `AI_Usage_Hours_Per_Day`, `Monthly_AI_Cost`, `Time_Saved_Hours_Per_Week` and `Work_or_Study_Hours_Per_Day` all need cleaning before they can be averaged. - **Missing scores:** productivity is filled in 284 of 300 rows, accuracy in 285 and satisfaction in 293. - **Inconsistent country casing and language:** "france" and "india" appear in lowercase, and some professions are in German, French or Portuguese. **Overall scores.** The averages across filled rows are productivity 5.23 (range 1.6–10.0), satisfaction 6.15 and accuracy 3.43. I can run a cleaned version, with normalized gender, tool and education labels and numeric hours and cost, and then break the scores down by tool or purpose.