Overview

This dataset consists of details on daily used Tesla cars sold in United States from Tesla. Data fields include vin, year, model, color, miles, trim, sold price, interior, wheels, features, country, location, metro, state, currency, sold date.

Fields

• vin
• year
• model
• color
• miles
• trim
• sold_price
• interior
• wheels
• features
• country
• location
• metro
• state
• currency
• sold_date

Sample data from May 2022

Download Sample Dataset

Specifications

• Dataset Format: CSV (comma-separated)
• Update Frequency: Monthly
• Geographic coverage: US

Data Source

From tesla.com

Subscription

• Email

Support

You can reach us at support@saturndatacloud.com for any questions on the dataset.

About Saturn Data

Saturn Data provides data mining solutions from public sources to deliver insights for enterprises and the market. If you are interested in acquiring other datasets or customized data mining service, email us at info@saturndatacloud.com.

Sales Analysis 2023 Summary for Auto Parts Manufacturer Company Total Sales and Profit: The company achieved a total sales of 56 million AED in 2023. Net profit for the year was 34 million AED. Monthly Highlights: October 2023 saw the highest sales, exceeding 5 million AED. The average unit price peaked in October 2023. Product Performance: Car Accessories: Most sold category due to their lower price and smaller size. Body Parts: Generated the highest revenue of approximately 13 million AED due to their higher price. Recorded the highest gross margin at 66%. Highest net profit in this category, amounting to 8 million AED. Wheels and Tires: Have the highest average unit price. Sales Volume: March 2023 recorded the maximum quantity sold, with 20,000 items. Key Insights: Revenue Distribution: Body parts are the main revenue drivers, contributing significantly due to their high price. Profit Margins: Body parts not only contribute the most to revenue but also have the highest gross margin, highlighting their profitability. Sales Trends: October is the peak month for sales and unit price, indicating potential seasonal factors or successful marketing strategies. Product Mix: Car accessories are the most popular items by volume, while body parts dominate in terms of revenue and profit. Sales Volume: March is notable for the highest sales volume, which could be leveraged for future sales planning and inventory management. This analysis provides a comprehensive view of the sales performance in 2023, highlighting key areas of success and opportunities for strategic focus in the future.

The global market for tire and wheel cleaning brushes is poised for significant expansion, driven by a growing automotive aftermarket and an increasing consumer focus on vehicle aesthetics and maintenance. With an estimated market size of USD 750 million in 2025, the industry is projected to witness a robust Compound Annual Growth Rate (CAGR) of 6.5% over the forecast period of 2025-2033. This growth is primarily fueled by the rising number of vehicle owners worldwide, a persistent demand for specialized cleaning tools that offer enhanced efficiency and superior results, and the expanding influence of online retail channels. Consumers are increasingly investing in products that not only maintain the visual appeal of their vehicles but also contribute to the longevity of tires and wheels, recognizing these components as critical to both performance and resale value. The proliferation of car care enthusiasts and the rise of detailing services further bolster demand for advanced cleaning solutions. The market is segmented into applications such as supermarkets and malls, catering to impulse buys and convenient access, and e-commerce, which dominates due to its vast product selection, competitive pricing, and direct-to-consumer reach. The "Others" category, encompassing automotive service centers and specialized detailing shops, also represents a significant segment. In terms of types, both manual and electric brushes play a crucial role, with electric brushes gaining traction due to their ability to reduce user effort and deliver more thorough cleaning. Key market players like 3M, Speedmaster, and Chemical Guys are actively innovating, introducing brushes with ergonomic designs, durable materials, and specialized bristle configurations to address diverse cleaning challenges. Despite the strong growth outlook, potential restraints include the availability of generic or lower-quality alternatives that may appeal to price-sensitive consumers, and the cyclical nature of discretionary spending, which could impact sales during economic downturns. However, the overarching trend towards premium vehicle care and the continuous development of new cleaning technologies are expected to outweigh these challenges, ensuring sustained market expansion. This in-depth report provides a strategic analysis of the global Tire and Wheel Cleaning Brush market, encompassing a detailed examination of its historical performance, current landscape, and future trajectory. With a study period spanning from 2019 to 2033, the report leverages a robust dataset, with the base year of 2025 serving as a pivotal point for estimations and forecasts. We delve into market dynamics, competitive strategies, and the evolving needs of consumers and industries. The market is projected to witness significant growth, with an estimated market size of XX million USD in the estimated year of 2025, further expanding to YY million USD by the end of the forecast period in 2033. This growth is underpinned by innovation, increasing consumer demand for effective automotive detailing solutions, and expanding distribution channels.

Rigid solid body dynamics is a key element of the undergraduate mechanical engineering curriculum. In a context of reverse engineering and/or sustainable development, being able to analyze the mechanical and material properties of a system without damaging it is a required skill. In this dataset, a wheel with displaced mass rolling over horizontal path without sliding is studied. Four generations of last year bachelor students in mechanical engineering, representing a hundred people a year, followed a total of 12 hours of practical sessions working on such systems. This work aims at showing how computer tools can help and improve a rigid solid body dynamics course.

Flash-Attention3-wheel

Flash Attention 3 wheels on commit 0e60e39473e8df549a20fb5353760f7a65b30e2d. https://windreamer.github.io/flash-attention3-wheels/ much more banger!

  Build using H100

For PyTorch 2.6.0 12.6, 2.7.0 12.6, 2.7.0 12.8, 2.7.1 12.6, 2.7.1 12.8, minimum Python 3.9.

  Build using GH200 ARM64

For PyTorch 2.7.0 12.8, 2.7.1 12.8, minimum Python 3.9.

  Installation

make sure rename or else your PIP is screaming

eg remove -2.7.1-12.8… See the full description on the dataset page: https://huggingface.co/datasets/malaysia-ai/Flash-Attention3-wheel.

Deception in Roulette: A Look at Random Number Manipulation

Roulette has been a cornerstone in the study of randomness and statistics since its invention, influencing not only physical casinos but also online platforms. I have created a unique dataset that simulates a roulette wheel, not only to explore the random generation of numbers but also to illustrate how certain techniques can be easily employed by online casinos for fraudulent activities.

Dynamic Factors at Play:

-Temporal and Climatic Variables: Each spin is precisely recorded, integrating sports results and weather conditions that influence fraud techniques.

-Dynamic Fraud Techniques: I have created 53 different fraud techniques, including 5 advanced hybrid techniques that combine various manipulation methods. I select and change fraud techniques daily, adjusting them according to the 'peak hours' of casino traffic to reflect realistic manipulation methods.

-Influence of Historical Results: I use spin histories to determine 'hot' (more frequent) and 'cold' (less frequent) numbers, which are key to deciding the fraud techniques at any given moment.

-Distributions and Biases: The distributions of resulting numbers are adjusted based on these analyses, showing how historical information can be used to manipulate future results.

-Majority of Legitimate Spins: Almost 95% of the spins in this dataset are completely legitimate, without any manipulation, reflecting the normal operation of a roulette wheel.

-Fraud Concentrated During Peak Hours, Weeks, Months, and Days: The remaining 5% corresponds to fraudulent spins, strategically distributed during peak hours, weeks, months, and days, covering a period of one year. This proportion highlights the importance of thoroughly auditing these high-activity periods.

I would love to see more studies on this database, so I encourage everyone who reads this post to share the insights you discover.

Here is the list of strategies used in the dataset (some of them are not as intuitive as they might seem by their names):

0 == No Fraud 1. 'number_bias' 2. 'predictable_sequences' 3. 'color_omission' 4. 'low_range_bias' 5. 'sequence_repetition' 6. 'cyclic_alteration' 7. 'day_night_bias' 8. 'altered_zero_frequency' 9. 'random_alterations' 10. 'temporal_bias' 11. 'day_hour_bias' 12. 'day_of_week_bias' 13. 'day_of_month_bias' 14. 'bimodal_distribution' 15. 'fibonacci_bias' 16. 'parity_alteration' 17. 'prime_sequence' 18. 'double_sinusoidal_distribution' 19. 'normal_distribution' 20. 'time_series_patterns' 21. 'adaptive_variation' 22. 'wear_simulation' 23. 'advanced_hybrid_1' 24. 'advanced_hybrid_2' 25. 'advanced_hybrid_3' 26. 'advanced_hybrid_4' 27. 'advanced_hybrid_5' 28. 'previous_result_sum_bias' 29. 'special_dates_bias' 30. 'weighted_global_events_distribution' 31. 'previous_winning_combinations_bias' 32. 'sentiment_analysis_alteration' 33. 'weighted_day_of_month_bias' 34. 'weather_patterns_bias' 35. 'weighted_hour_of_day_distribution' 36. 'sports_events_bias' 37. 'lunar_cycles_modulation' 38. 'high_range_bias' 39. 'inverse_prime_sequence' 40. 'alternate_parity_bias' 41. 'zero_series_frequency' 42. 'game_history_bias' 43. 'gaussian_noise_modulation' 44. 'time_weighted_distribution_bias' 45. 'last_digit_bias' 46. 'cumulative_temporal_bias' 47. 'hidden_previous_results_patterns' 48. 'weighted_hot_cold_oscillation' 49. 'adaptive_hot_cold_sequence' 50. 'cold_number_mirage' 51. 'hot_number_evasion' 52. 'false_cold' 53. 'hot_deviation'

Attached is an example of analysis for a specific hour using a specific strategy, in this case, "double_sinusoidal_distribution":

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F9698182%2Ff536eaa650aeebb5737a9d9a2ec53665%2Foutputexample.png?generation=1720566276284440&alt=media" alt="">

Trash Wheel Collection Data

This dataset is from Trash Wheel Collection Data from the Mr. Trash Wheel Baltimore Healthy Harbor initiative.

Mr. Trash Wheel is a semi-autonomous trash interceptor that is placed at the end of a river, stream or other outfall. Far too lazy to chase trash around the ocean, Mr. Trash Wheel stays put and waits for the waste to flow to him. Sustainably powered and built to withstand the biggest storms, Mr. Trash Wheel uses a unique blend of solar and hydro power to pull hundreds of tons of trash out of the water each year.

The Healthy Harbor initiative has four Trash Wheels collecting trash. Mr. Trash Wheel was the first to start, and since then three more have joined the family. The Trash Wheel Family has collected more than 2,362 tons of trash. See more about how Mr. Trash Wheel works.

Data collection methodology

1. When crew members are on the machine during the time when a dumpster is being filled, they will manually count the number of each of the item types listed on a single conveyor paddle. This process is repeated several times during the dumpster filling process. An average is then calculated for number of each item per paddle. The average is then multiplied by the paddle rate and then by the elapsed time to fill the dumpster.

Example: * Paddle #1- 9 plastic bottles * Paddle #2- 14 plastic bottles * Paddle #3- 5 plastic bottles * Paddle #4- 12 plastic bottles * Average = 10 plastic bottles/paddle

Conveyor speed = 2.5 paddles per minute therefore an average of 25 plastic bottles are loaded each minute. If it takes 100 minutes to fill the dumpster, we estimate that there are 2,500 bottles in that dumpster.

1. If no crew is present during the loading, we will take random bushel size samples of the collected material and count items in these samples. A full dumpster contains approximately 325 bushels. Therefore, if an average bushel sample from a dumpster contains 3 polystyrene containers, we estimate that the dumpster contains 975 polystyrene containers.

1. Periodically “dumpster dives” are held where volunteers count everything in an entire dumpster. These events help validate our sampling methods and also look at what materials are dumpster. present that are not included in our sampling categories.

What type of trash is collected the most? Do the different Trash Wheels collect different sets of trash? Are there times of the year when more or less trash is collected?

Data Dictionary

trashwheel.csv

There are numerous car datasets available that provide information on various aspects of vehicles. Here is a general description of the common types of information you may find in car datasets:

Make and Model: The brand and model name of the car. Year: The manufacturing year of the vehicle. Price: The price at which the car was listed or sold. Mileage: The number of miles the car has been driven. Fuel Efficiency: The car's average fuel consumption or MPG (Miles Per Gallon) rating. Horsepower: The power output of the car's engine. Number of Cylinders: The number of cylinders in the car's engine. Transmission: The type of transmission system in the car (e.g., automatic, manual). Drivetrain: The configuration of the car's drivetrain (e.g., front-wheel drive, rear-wheel drive, all-wheel drive). Body Type: The category or style of the car (e.g., sedan, SUV, truck, coupe). Engine Displacement: The capacity or size of the car's engine. Dimensions: Information about the car's length, width, height, and weight. Safety Ratings: Data on the car's safety features and crash test ratings. Features: Additional features and specifications such as navigation system, infotainment system, sunroof, etc

Description:

This dataset contains the latest information on car prices in Australia for the year 2023. It covers various brands, models, types, and features of cars sold in the Australian market. It provides useful insights into the trends and factors influencing the car prices in Australia. The dataset includes information such as brand, year, model, car/suv, title, used/new, transmission, engine, drive type, fuel type, fuel consumption, kilometres, colour (exterior/interior), location, cylinders in engine, body type, doors, seats, and price. The dataset has over 16,000 records of car listings from various online platforms in Australia.

Key Features:

• Brand: Name of the car manufacturer
• Year: Year of manufacture or release
• Model: Name or code of the car model
• Car/Suv: Type of the car (car or suv)
• Title: Title or description of the car
• UsedOrNew: Condition of the car (used or new)
• Transmission: Type of transmission (manual or automatic)
• Engine: Engine capacity or power (in litres or kilowatts)
• DriveType: Type of drive (front-wheel, rear-wheel, or all-wheel)
• FuelType: Type of fuel (petrol, diesel, hybrid, or electric)
• FuelConsumption: Fuel consumption rate (in litres per 100 km)
• Kilometres: Distance travelled by the car (in kilometres)
• ColourExtInt: Colour of the car (exterior and interior)
• Location: Location of the car (city and state)
• CylindersinEngine: Number of cylinders in the engine
• BodyType: Shape or style of the car body (sedan, hatchback, coupe, etc.)
• Doors: Number of doors in the car
• Seats: Number of seats in the car
• Price: Price of the car (in Australian dollars)

Potential Use Cases:

• Price prediction: Predict the price of a car based on its features and location using machine learning models.
• Market analysis: Explore the market trends and demand for different types of cars in Australia using descriptive statistics and visualization techniques.
• Feature analysis: Identify the most important features that affect the car prices and how they vary across different brands, models, and locations using correlation and regression analysis.

If you find this dataset useful, your support through an upvote would be greatly appreciated ❤️🙂
Thank you

This dataset provides detailed information on car listings collected from an online marketplace where users buy and sell cars. It includes various features that describe each car, along with metadata such as the number of views each listing received and how many users added the car to their favorites.

Data Dictionary

Variables Notes

post_info - contains information about date when announcement was created or updated since 08.10.2024. So if you want to get date you need to subtract days/weeks/months in column from 08.10.2024.

1. Title: 1985 Auto Imports Database

2. Source Information: -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) -- Date: 19 May 1987 -- Sources: 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook. 2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037

3. Past Usage: -- Kibler,~D., Aha,~D.~W., & Albert,~M. (1989). Instance-based prediction of real-valued attributes. {\it Computational Intelligence}, {\it 5}, 51--57. -- Predicted price of car using all numeric and Boolean attributes -- Method: an instance-based learning (IBL) algorithm derived from a localized k-nearest neighbor algorithm. Compared with a linear regression prediction...so all instances with missing attribute values were discarded. This resulted with a training set of 159 instances, which was also used as a test set (minus the actual instance during testing). -- Results: Percent Average Deviation Error of Prediction from Actual -- 11.84% for the IBL algorithm -- 14.12% for the resulting linear regression equation

4. Relevant Information: -- Description This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process "symboling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.

   The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc...), and represents the average loss per car per year.

   -- Note: Several of the attributes in the database could be used as a "class" attribute.

5. Number of Instances: 205

6. Number of Attributes: 26 total -- 15 continuous -- 1 integer -- 10 nominal

7. Attribute Information:
   Attribute: Attribute Range:

   1. symboling: -3, -2, -1, 0, 1, 2, 3.
   2. normalized-losses: continuous from 65 to 256.
   3. make: alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz, mercury, mitsubishi, nissan, peugot, plymouth, porsche, renault, saab, subaru, toyota, volkswagen, volvo
   4. fuel-type: diesel, gas.
   5. aspiration: std, turbo.
   6. num-of-doors: four, two.
   7. body-style: hardtop, wagon, sedan, hatchback, convertible.
   8. drive-wheels: 4wd, fwd, rwd.
   9. engine-location: front, rear.
   10. wheel-base: continuous from 86.6 120.9.
   11. length: continuous from 141.1 to 208.1.
   12. width: continuous from 60.3 to 72.3.
   13. height: continuous from 47.8 to 59.8.
   14. curb-weight: continuous from 1488 to 4066.
   15. engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor.
   16. num-of-cylinders: eight, five, four, six, three, twelve, two.
   17. engine-size: continuous from 61 to 326.
   18. fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi.
   19. bore: continuous from 2.54 to 3.94.
   20. stroke: continuous from 2.07 to 4.17.
   21. compression-ratio: continuous from 7 to 23.
   22. horsepower: continuous from 48 to 288.
   23. peak-rpm: continuous from 4150 to 6600.
   24. city-mpg: continuous from 13 to 49.
   25. highway-mpg: continuous from 16 to 54.
   26. price: continuous from 5118 to 45400.

8. Missing Attribute Values: (denoted by "?") Attribute #: Number of instances missing a value:

   1. 41
   2. 2
   3. 4
   4. 4
   5. 2
   6. 2
   7. 4