Dataset

This dataset was created by neelaydoshi7

Contents

Dataset

This dataset was created by sodagar rajiv

Contents

Dataset

This dataset was created by Khabibullokhon Bakhshilloev

Released under Data files © Original Authors

Contents

Dataset

This dataset was created by Master Sniffer

Contents

Analysis of ‘Shopping Cart Database’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ruchi798/shopping-cart-database on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset contains synthetic data generated by me for one of my courses at Carnegie Mellon University.

Inspiration

Several deductions and analyses can be drawn from this data, including: - Which products were sold the most in the last month? - How have sales and revenue changed over the past few quarters? - Understanding Customer demographics and their preferences

--- Original source retains full ownership of the source dataset ---

The GigaFlexhicle dataset contains many cut-out photos of brands and models of vehicles (cars/trucks/buses), divided by generations. All models of vehicles produced in the same generation belong to the same class, regardless of the body, restyling or special edition. For example, Opel Astra J hatchback, station wagon, GTC are one class, regardless of the change in appearance during restyling. Opel Astra J and Opel Astra H are different classes.

Dataset structure: The images directory contains all vehicle brands. Inside folders with brands are folders with models of this brand. Inside each folder with the model there are possible generations of this model (usually they are listed as 1st, 2nd, etc., however, there may also be names of generations. Further deepening into the directory is considered as one generation). The annotation.csv file contains the class numbers and the relative path to each image. The class was assigned only if it contains 3 or more pictures. The number of classes is 9548. Directories with 2 or less pictures were left for further expansion and search for similar pictures.

For example, I need to take a look at all the photos of the BMW 2-series of the 2nd generation. This folder will contain such 2nd generation brands as 2-series, 2-series Active Tourer and 2-series Gran Tourer.

This dataset is not the final option and its structure will be improved, and it will be supplemented by itself. The dataset may include motorcycles, but their presence is not assumed.

The data used in this dataset has been copied from the website PlatesMania.com

Dataset

This dataset was created by DataWizard20

Contents

It contains the following files:

The OTTO session dataset is a large-scale dataset intended for multi-objective recommendation research. We collected the data from anonymized behavior logs of the OTTO webshop and the app. The mission of this dataset is to serve as a benchmark for session-based recommendations and foster research in the multi-objective and session-based recommender systems area. We also launched a Kaggle competition with the goal to predict clicks, cart additions, and orders based on previous events in a user session.

For additional background, please see the published OTTO Recommender Systems Dataset GitHub.

Key Features

• 12M real-world anonymized user sessions
• 220M events, consiting of clicks, carts and orders
• 1.8M unique articles in the catalogue
• Ready to use data in .jsonl format
• Evaluation metrics for multi-objective optimization

Dataset Statistics

Train/Test Split

Since we want to evaluate a model's performance in the future, as would be the case when we deploy such a system in an actual webshop, we choose a time-based validation split. Our train set consists of observations from 4 weeks, while the test set contains user sessions from the following week. Furthermore, we trimmed train sessions overlapping with the test period, as depicted in the following diagram, to prevent information leakage from the future:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4621388%2F94cead9aec2ef687490b1212e40f409a%2Ftrain_test_split.png?generation=1676645044801713&alt=media" alt="Train/Test Split">

Dataset

This dataset was created by Faisal Jamil

Released under Other (specified in description)

Contents

It contains the following files:

Context

The Donner Party were a group of emigrants moving to start a new life in California. But between 1846 and 1847, 45 out of the 87 people on the wagon train would die from sickness, starvation, murder, and cannibalism.

Content

The dataset has information gathered from reading Daniel James Brown, The Indifferent Stars Above, and overall reading online

Acknowledgements

James Brown, Daniel. The Indifferent Stars Above. New York: HarperCollins Publishers, 2015.

Inspiration

A question that I had while researching the Donner Party was what factors influenced their chances of survival(Age, Sex, etc). So this was made in an attempt to answer that question

Dataset

This dataset was created by Cart Vista

Released under CC0: Public Domain

Contents

Context

This Data set including all the Variables (choice, college,hsg2, coml5, typez, fuelz, pricez, speedz, pollutionz, sizez) from 2016 to 2018.I scrapped this data from www.qed.econ.queensu.ca

Content

Attribute Information:

1. choice-choice of a vehicle among 6 propositions
2. college-college education
3. hsg2-size of household greater than 2
4. coml5-commute lower than 5 miles a day
5. typez-body type, one of regcar (regular car), sportuv (sport utility vehicle), sportcar, stwagon (station wagon), truck, van, for each proposition z from 1 to 6
6. fuelz-fuel for proposition z, one of gasoline, methanol, cng (compressed natural gas), electric.
7. pricez-price of vehicle divided by the logarithm of income
8. speedz-highest attainable speed in hundreds of mph
9. pollutionz-tailpipe emissions as fraction of those for new gas vehicle
10. sizez- 0 for a mini, 1 for a subcompact, 2 for a compact and 3 for a mid–size or large vehicle

Source

McFadden, Daniel and Kenneth Train (2000) “Mixed MNL models for discrete response”, Journal of Applied Econometrics, 15(5), 447–470.

References

Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

Dataset

This dataset was created by Satya Mishra

Contents

Dataset containing vehicles sold in the US market of 2024-2025 year. Compares horsepower, torque, weight, and ratios of all makes and models sold in the US market of 2024-2025.

Data is taken from manufacturer website and Car & Driver where applicable.

I only compared data with vehicles designed, marketed, and sold as sedans or lower. Wagons were included where applicable. The Mercedes E-class wagon was excluded due to lack of data found. Data excludes vehicles sold and marketed as CUV and above (CUVs, SUVs, Trucks, Vans, etc.)

Description

This dataset contains traffic violation information from electronic traffic violations issued in the US. Any information that can be used to uniquely identify the vehicle, the vehicle owner or the officer issuing the violation is not included. Some features were removed from the original dataset and all remaining character features were recoded as nominal factor variables. All punctuation characters were removed from factor levels. The variable 'Violation.Type' is used as target by default. The smaller target categories 'SERO' and 'ESERO' were collapsed into one category labeled 'SERO'. Unused factor levels and a few almost constant features were dropped.

Features

• Description: Text description of the specific charge
• Belts: If seat belts were in use in accident cases or not?
• Personal Injury: If traffic violation involved Personal Injury or not?
• Property Damage: If traffic violation involved Property Damage or not?
• Commercial License: If the driver holds a Commercial Drivers License or not?
• Commercial Vehicle: If the vehicle committing the traffic violation is a commercial vehicle or not?
• State: State issuing the vehicle registration
• VehicleType: Type of vehicle (Examples: Automobile, Station Wagon, Heavy Duty Truck, etc.)
• Year: Year the vehicle was made
• Make: Manufacturer of the vehicle (Examples: Ford, Chevy, Honda, Toyota, etc.)
• Model: Model of the vehicle
• Color: Color of the vehicle
• Charge: Alphanumeric code for the specific charge
• Contributed To Accident: If the traffic violation was a contributing factor in an accident or not?
• Race: Race of the driver (Example: Asian, Black, White, Other, etc.)
• Gender: Gender of the driver (F = Female, M = Male)
• Driver City: City of the driver’s home address
• Driver State: State of the driver’s home address
• DL State: State issuing the Driver’s License
• Arrest Type: Type of Arrest (A = Marked, B = Unmarked, etc.)
• Violation Type: Type of Violation (Examples: Warning, Citation, SERO)

Please, provide an upvote👍if the dataset was useful for your task. It would be much appreciated😄

Voice Gender

Gender Recognition by Voice and Speech Analysis

This database was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz (human vocal range).

The Dataset

The following acoustic properties of each voice are measured and included within the CSV:

• meanfreq: mean frequency (in kHz)
• sd: standard deviation of frequency
• median: median frequency (in kHz)
• Q25: first quantile (in kHz)
• Q75: third quantile (in kHz)
• IQR: interquantile range (in kHz)
• skew: skewness (see note in specprop description)
• kurt: kurtosis (see note in specprop description)
• sp.ent: spectral entropy
• sfm: spectral flatness
• mode: mode frequency
• centroid: frequency centroid (see specprop)
• peakf: peak frequency (frequency with highest energy)
• meanfun: average of fundamental frequency measured across acoustic signal
• minfun: minimum fundamental frequency measured across acoustic signal
• maxfun: maximum fundamental frequency measured across acoustic signal
• meandom: average of dominant frequency measured across acoustic signal
• mindom: minimum of dominant frequency measured across acoustic signal
• maxdom: maximum of dominant frequency measured across acoustic signal
• dfrange: range of dominant frequency measured across acoustic signal
• modindx: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range
• label: male or female

Accuracy

Baseline (always predict male)

50% / 50%

Logistic Regression

97% / 98%

CART

96% / 97%

Random Forest

100% / 98%

SVM

100% / 99%

XGBoost

100% / 99%

Research Questions

An original analysis of the data-set can be found in the following article:

Identifying the Gender of a Voice using Machine Learning

The best model achieves 99% accuracy on the test set. According to a CART model, it appears that looking at the mean fundamental frequency might be enough to accurately classify a voice. However, some male voices use a higher frequency, even though their resonance differs from female voices, and may be incorrectly classified as female. To the human ear, there is apparently more than simple frequency, that determines a voice's gender.

Questions

• What other features differ between male and female voices?
• Can we find a difference in resonance between male and female voices?
• Can we identify falsetto from regular voices? (separate data-set likely needed for this)
• Are there other interesting features in the data?

CART Diagram

http://i.imgur.com/Npr2U7O.png" alt="CART model">

Mean fundamental frequency appears to be an indicator of voice gender, with a threshold of 140hz separating male from female classifications.

References

The Harvard-Haskins Database of Regularly-Timed Speech

Telecommunications & Signal Processing Laboratory (TSP) Speech Database at McGill University, Home

VoxForge Speech Corpus, Home

Festvox CMU_ARCTIC Speech Database at Carnegie Mellon University

This dataset provides comprehensive details on used car listings, including vehicle specifications, features, pricing, and more. It's valuable for analyzing car prices, trends, and customer preferences in the automotive market.

Columns Description

• make_model: The brand and model of the vehicle (e.g., 'Audi A1').
• body_type: The body style of the vehicle, such as Sedan, Compact, or Station Wagon.
• price: The listed price of the car in currency.
• vat: Indicates the VAT status for the vehicle's price (e.g., VAT deductible, Price negotiable).
• km: The total mileage (in kilometers) of the vehicle, indicating its usage.
• Type: Condition of the vehicle, whether it's 'Used' or 'New'.
• Fuel: Type of fuel the vehicle uses, such as 'Diesel', 'Benzine', etc.
• Gears: The number of gears in the vehicle's transmission.
• Comfort_Convenience: Comfort and convenience features, such as 'Air conditioning', 'Leather steering wheel', 'Cruise control', and more.
• Entertainment_Media: Media features available in the vehicle, including 'Bluetooth', 'MP3', 'Radio', etc.
• Extras: Additional features like 'Alloy wheels', 'Sport suspension', etc.
• Safety_Security: Safety features like 'ABS', 'Airbags', 'Electronic stability control', 'Isofix', etc.
• age: Age of the car (calculated based on the model year).
• Previous_Owners: The number of previous owners the car has had.
• hp_kW: Engine power in kilowatts (kW), indicating the performance capacity of the engine.
• Inspection_new: Indicates whether the car has recently undergone an inspection (1 for yes, 0 for no).
• Paint_Type: The type of paint on the car, such as 'Metallic', 'Matte', etc.
• Upholstery_type: The material used for the interior upholstery, such as 'Cloth', 'Leather', etc.
• Gearing_Type: The type of transmission the car uses, either 'Automatic' or 'Manual'.
• Displacement_cc: The engine displacement in cubic centimeters (cc), indicating the size of the engine.
• Weight_kg: The total weight of the vehicle in kilograms.
• Drive_chain: The type of drivetrain, indicating whether it's 'Front' or 'Rear' wheel drive.
• cons_comb: The combined fuel consumption in liters per 100 kilometers.

Key Features

• Vehicle Specifications: Covers details like make, model, body type, fuel type, and more.
• Comfort & Safety Features: Includes information on air conditioning, safety features, and other convenience options.
• Performance Metrics: Provides data on mileage, engine power, weight, and fuel consumption.
• Pricing Information: Insights into vehicle pricing, VAT status, and other cost-related details.

Ideal Use Cases

• Price Prediction: Model car prices based on features like mileage, fuel type, and performance.
• Market Analysis: Explore trends and preferences in the used car market, by type, region, or other metrics.
• Customer Segmentation: Segment the dataset to analyze different customer preferences for car types, features, or price ranges.
• Feature Importance: Identify the most important factors influencing car prices (e.g., fuel type, mileage, age).

This dataset is ideal for machine learning, data analysis, and business intelligence applications in the automotive industry.

Context

Dataset is about cars from back in 85. It's raw and messy.

Content

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/specialty, 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.

Number of Instances: 205

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

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.

Acknowledgements

"Automobile Data Set" from the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data.

Inspiration

I used these data for data cleaning/analysis purposes.