The sample rows are already rendered above (the two 15-row pulls). Here is what they show about the dataset. **What a row looks like** - Each row is one surveyed user, either a student or a professional, keyed by `User_ID`. It records demographics (age, gender, country, education, profession), the AI tool and purpose, usage hours, free-text tasks, monthly AI cost, and outcome scores (productivity, accuracy, satisfaction, time saved, recommend). - The sample mixes students (for example a 16-year-old digital media student using Claude for content creation) and professionals (for example a senior software developer using Copilot for coding, or a customer support specialist using ChatGPT). - Countries include the Philippines, Canada, the US, Pakistan, Germany, India, the UK, Australia and Brazil. - Purposes include Coding, Studying, Research, Writing, Customer Support, Content Creation, Data Analysis and Office Work. **Data-quality issues visible in the sample** - **Duplicate IDs:** There are 300 rows but only 288 distinct `User_ID`s. U0207 appears in both pulls, and the count shows about 12 repeated IDs. - **Numbers stored as text:** `Age`, `AI_Usage_Hours_Per_Day`, `Monthly_AI_Cost`, `Time_Saved_Hours_Per_Week` and `Work_or_Study_Hours_Per_Day` are text columns. Some values carry units or symbols, such as "16 yrs", "1.6 hrs", "$20 " and "6.5 hrs". They need cleaning before any maths. - **Inconsistent categories:** - Gender appears as "F", "FEMALE" and "male". - Country appears as "france", "india" and "canada". - Education appears as "undergrad" and "Undergraduate". - AI tool appears as "Chat GPT", "chatgpt" and "copilot", alongside "Microsoft Copilot". - **Mixed languages:** Some professions are in German, French or Portuguese, for example "Kundenbetreuerin mit KI-Unterstützung" and "Développeur Full Stack". - **Missing values:** - Productivity is missing in 16 of 300 rows (284 present), accuracy in 15 (285 present) and satisfaction in 7 (293 present). - Individual rows also have blanks in `Gender`, `Country`, `AI_Tool`, `Education_Level`, `Profession` and `Monthly_AI_Cost`. - **Plausibility:** Some rows look implausible, such as a 16-year-old with a Master's degree and a 17-year-old with a PhD. **Overall scores:** Average productivity is about 5.23 (range 1.6–10.0), average satisfaction about 6.15 and average accuracy about 3.43. I left `Monthly_Income` out of this summary because it is person-identifying, so I only describe it in aggregate. Standardising the text fields, deduplicating `User_ID` and fixing the numeric columns would be the first steps before analysis. I can run a deduplicated, cleaned version of the sample if you want.