Data Analysis For Bikes Dataset Using Tableau
Bibliographic record
Abstract
This dataset contains 150 records of motorcycle sales across different locations. It includes 11 attributes, such as Bike_ID, Date, Location, Brand, Model, Bike_Name, CC (engine capacity), Dealer, Price, Units_Sold, and Total_Revenue. The dataset captures sales transactions from various motorcycle brands like KTM, Kawasaki, Harley-Davidson, Yamaha, and BMW, recorded in multiple cities, including London, Toronto, and New York. Each record represents a unique bike sale with details on pricing, the number of units sold, and the total revenue generated. Additionally, the dataset provides insights into customer preferences, popular bike models, and the impact of different pricing strategies on sales performance. It enables businesses to analyze dealership effectiveness, assess regional demand variations, and identify high-performing models. This dataset can be used for sales analysis, market trends, brand performance evaluation, and revenue forecasting, making it a valuable resource for motorcycle manufacturers, dealers, and market analysts looking to optimize their strategies and improve profitability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".