About Dataset

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you are given dataset which contains information about automobiles. The dataset contains 399 rows of 9 features

DATA OVERVIEW:

The dataset consists of the following columns:

• Name: Unique identifier for each automobile.
• MPG: Fuel efficiency measured in miles per gallon.
• Cylinders: Number of cylinders in the engine.
• Displacement: Engine displacement, indicating its size or capacity.
• Horsepower: Power output of the engine.
• Weight: Weight of the automobile.
• Acceleration: Capability to increase speed, measured in seconds.
• Model Year: Year of manufacture for the automobile model.
• Origin: Country or region of origin for each automobile.

We made this dataset to make AI model which can predict car price depending on its various factors which includes Mileage, Engine capacity, Body type, Model, Make and others features of a car.

Dataset

This dataset was created by Arslan Abdul Ghaffar

Contents

Used Cars Dataset

Overview This dataset contains information about used cars in the Indian market, comprising 15,000 entries with 11 detailed attributes. The data appears to be collected up to November 2024, providing a comprehensive view of the second-hand car market in India.

Dataset Features

Brand: Car manufacturer (e.g., Volkswagen, Maruti Suzuki, Honda, Tata)
Model: Specific car model (e.g., Taigun, Baleno, Polo, WRV)
Year: Manufacturing year of the vehicle (ranging from older models to 2024)
Age: Age of the vehicle in years
kmDriven: Total kilometers driven by the vehicle
Transmission: Type of transmission (Manual or Automatic)
Owner: Ownership status (first or second owner)
FuelType: Type of fuel (Petrol, Diesel, Hybrid/CNG)
PostedDate: When the car listing was posted
AdditionalInfo: Extra details about the vehicle
AskPrice: Listed price in Indian Rupees (₹)

Dataset Statistics

• Total entries: 14,993
• Columns: 11
• Memory usage: 1.94+ MB
• Data types: Mixed (int64 and object)

Potential Use Cases

• Price prediction modeling for used cars in India
• Market analysis of different car segments
• Study of depreciation patterns
• Analysis of fuel type preferences
• Understanding transmission preferences in the Indian market
• Examining the relationship between kilometers driven and pricing
• Brand value retention analysis

This dataset would be valuable for data scientists, automotive market analysts, and machine learning practitioners interested in the Indian automotive sector.

The NHTSA Product Information Catalog and Vehicle Listing (vPIC) is a consolidated platform that presents data collected within the manufacturer reported data from CFR 49 Parts 551 - 574 for use in a variety of modern tools. NHTSA's vPIC platform is intended to serve as a centralized source for basic Vehicle Identification Number (VIN) decoding, Manufacturer Information Database (MID), Manufacturer Equipment Plant Identification and associated data. vPIC is intended to support the Open Data and Transparency initiatives of the agency by allowing the data to be freely used by the public without the burden of manual retrieval from a library of electronic documents (PDFs). While these documents will still be available online for viewing within the Manufacturer Information Database (MID) module of vPIC one can view and use the actual data through the VIN Decoder and Application Programming Interface (API) modules.

Labeled Car images suitable for training and evaluating computer vision and deep learning models.

The Car Model Variants and Images Dataset is a comprehensive collection of around 193k images across 3778 car model variants, obtained entirely through web scraping of the autoevolution.com website. Each model variant contains between 20 and 200 images in the size of 512x512, offering a diverse range of high-quality images that have been collected from a single reliable source.

The accompanying .csv file contains 44 columns of information about the car and the images that belong to them, making it easy to access and utilize the data. The information in the .csv file includes make, model, year, body type, engine type, transmission, and fuel type, among other specifications. Additionally, the file includes information on the image filenames and directories, providing quick access to the corresponding image data.

Some images might be missing due to being deleted as a bad format after resizing. However, despite the missing images, this dataset still provides a rich and diverse collection of car images that can be used for various machine learning tasks, such as image classification, object detection, and segmentation.

In conclusion, the Car Model Variants and Images Dataset is a reliable and comprehensive collection of high-quality car images and associated metadata, obtained through web scraping of the autoevolution.com website. The dataset is well-suited for use in a wide range of machine learning tasks, making it a valuable resource for researchers and practitioners in the computer vision field.

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

A dataset providing information of the vehicle types and counts in several locations in Leeds.

Purpose of the project

The aim of this work was to examine the profile of vehicle types in Leeds, in order to compare local emissions with national predictions.

Traffic was monitored for a period of one week at two Inner Ring Road locations in April 2016 and at seven sites around the city in June 2016. The vehicle registration data was then sent to the Department for Transport (Dft), who combined it with their vehicle type data, replacing the registration number with an anonymised ‘Unique ID’.

The data is provided in three folders:-

• Raw Data – contains the data in the format it was received, and a sample of each format.
• Processed Data – the data after processing by LCC, lookup tables, and sample data.
• Outputs – Excel spreadsheets summarising the data for each site, for various time/dates.

Initially a dataset was received for the Inner Ring Road (see file “IRR ANPR matched to DFT vehicle type list.csv”), with vehicle details, but with missing / uncertain data on the vehicles emissions Eurostandard class. Of the 820,809 recorded journeys, from the pseudo registration number field (UniqueID) it was determined that there were 229,891 unique vehicles, and 31,912 unique “vehicle types” based on the unique concatenated vehicle description fields.

It was therefore decided to import the data into an MS Access database, create a table of vehicle types, and to add the necessary fields/data so that combined with the year of manufacture / vehicle registration, the appropriate Eurostandard could be determined for the particular vehicle.

The criteria for the Eurostandards was derived mainly from www.dieselnet.com and summarised in a spreadsheet (“EuroStandards.xlsx”). Vehicle types were assigned to a “VehicleClass” (see “Lookup Tables.xlsx”) and “EU class” with additional fields being added for any modified data (Gross Vehicle Weight – “GVM_Mod”; Engine capacity – “EngineCC_mod”; No of passenger seats – “PassSeats”; and Kerb weight – “KerbWt”). Missing data was added from the internet lookups, extrapolation from known data, and by association – eg 99% of cars with an engine size

Additional data was then received from the Inner Ring Road site, giving journey date/time and incorporating the Taxi data for licensed taxis in Leeds.

Similar data for Sites 1-7 was also then received, and processed to determine the “VehicleClass” and “EU class”.

A mixture of Update queries, and VBA processing was then used to provide the Level 1-6 breakdown of vehicle types (see “Lookup Tables.xlsx”). The data was then combined into one database, so that the required Excel spreadsheets could be exported for the required time/date periods (see “outputs” folder).

Dataset

This dataset was created by Mariam_Kedr

Contents

The car_price.csv file contains a dataset of various car-models.

The dataset contains 205 rows and 26 columns(features) of which 25 are independent features. Below shows a detailed information of feature names with its labels and datatypes.

It is a regression problem where with the various features we are expected to predict the price of a car.

The dataset doesn't contain any null values.

Independent features:

Features Labels Datatype

symboling 6 int64 fueltype 2 object aspiration. 2 object doornumber. 2 object carbody 5 object drivewheel 3 object enginelocation 2 object wheelbase 53 float64 carlength 75 float64 carwidth 44 float64 carheight 49 float64 curbweight 171 int64 enginetype 7 object cylindernumber 7 object enginesize 44 int64 fuelsystem 8 object boreratio 38 float64 stroke 37 float64 compressionratio 32 float64 horsepower 59 int64 peakrpm 23 int64 citympg 29 int64 highwaympg 30 int64

**Target/Dependent variable: ** For the dataset we have price as our dependent feature with its datatype float64, hence using Regression Models we are expected to predict the value of price

Features Labels Datatype

price 189 float64

This dataset contains information about vehicles (or units as they are identified in crash reports) involved in a traffic crash. This dataset should be used in conjunction with the traffic Crash and People dataset available in the portal. “Vehicle” information includes motor vehicle and non-motor vehicle modes of transportation, such as bicycles and pedestrians. Each mode of transportation involved in a crash is a “unit” and get one entry here. Each vehicle, each pedestrian, each motorcyclist, and each bicyclist is considered an independent unit that can have a trajectory separate from the other units. However, people inside a vehicle including the driver do not have a trajectory separate from the vehicle in which they are travelling and hence only the vehicle they are travelling in get any entry here. This type of identification of “units” is needed to determine how each movement affected the crash. Data for occupants who do not make up an independent unit, typically drivers and passengers, are available in the People table. Many of the fields are coded to denote the type and location of damage on the vehicle. Vehicle information can be linked back to Crash data using the “CRASH_RECORD_ID” field. Since this dataset is a combination of vehicles, pedestrians, and pedal cyclists not all columns are applicable to each record. Look at the Unit Type field to determine what additional data may be available for that record. The Chicago Police Department reports crashes on IL Traffic Crash Reporting form SR1050. The crash data published on the Chicago data portal mostly follows the data elements in SR1050 form. The current version of the SR1050 instructions manual with detailed information on each data elements is available here. Change 11/21/2023: We have removed the RD_NO (Chicago Police Department report number) for privacy reasons.

Resources from the City of Bradford Metropolitan District Council (CBMDC) Parking service. Car park locations a simple csv containing name and location including latitude / longitude Car park current status. API that returns a csv dataset of the current status of 8 Bradford city centre car parks. The dataset returns capacity, empty places, status together with location details. The dataset is updated every 3 minutes for a live view of spaces in these car parks. Car park historic status API that returns a csv dataset building up the historic status of the 8 city centre car parks. The dataset is updated every 30 minutes. Further information beta map viewer Car parks across Bradford district Bradford car parks current status

Dataset

This dataset was created by Saja Abdalaal

Contents

Refer to Readme.pdf or Readme.md for information on this dataset.Test SummaryTable 1: Test Summary CodeVehicle typeYearMakeModelTypeG-HK-FICEV2020HyundaiKona SELFree burnE-CB-FEV2021ChevyBolt EV LTFree burnE-NL-FEV2019NissanLeaf SFree burnE-HI-FEV2019HyundaiIoniq LimitedFree burnE-TM-FEV2022TeslaModel 3 Long RangeFree burnE-FM-FEV2022FordMustang Mach E GTFree burnE-HK-FEV2020HyundaiKona SELFree burnG-HK-F2ICEV2020HyundaiKona SELFree burnG-TR-FICEV2019ToyotaRAV4 XLE AWDFree burnE-HK-SEV2019HyundaiKona SELSuppressionE-CB-SEV2021ChevyBolt EV LTSuppressionE-TM-SEV2021TeslaModel 3 Long RangeSuppressionE-FM-SEV2022FordMustang Mach E GTSuppressionE-FM-BEV2022FordMustang Mach E GTBlanketE-TM-BEV2022TeslaModel 3 Long RangeBlanketE-FM-SAEV2022FordMustang Mach E GTSuppression(a)E-FM-BSEV2021FordMustang Mach E GTBlanket(b)E-TM-BSEV2022TeslaModel 3 Long RangeBlanket(b)(a) Suppression experiment with encapsulator agent(b) Blanket experiments with water suppressionRepository StructureThe repository contains several subdirectories and file types. There are five files in the root directory which contain information pertinent to the entire dataset.Root Filesreadme.md: Contains information about the data repository and experiments.readme.pdf: Identical pdf copy of the readme.md file.instrumentation.pdf: Plan view of the laboratory space and instrumentation used in most experiments.instrumentation.csv: Details of the instrumentation used in each experiment.vehicle_info.md: Details of the vehicles used in each experiment.Experiment DirectoriesThere are 18 zip files (one per experiment) that contain the datasets for each experiment. The name of each zip file (subfolder) corresponds to the experiment ID in Table 1.Note: Some datasets are only present for a subset of experiments, depending on the objectives and instrumentation used in the experiment. For example, the mass-loss load cells were not used during suppression experiments; therefore, mass data is not present for these experiments. Details of the instruments used in each experiment are included in instrumentation.csv.information.md: A plain-text file containing general information about the experiment, including details of the vehicle and initiating fire.events.csv: CSV file where: • Col 1: description of the event • Col 2: time of the event (format hh:mm:ss)All time-series data are shifted relative to ignition, where ignition is set at t = 0 s.data_timeseries.csv: Contains time-series measurements: • Col 1: time stamp (shifted relative to ignition) • Remaining columns: sensor readings including heat-flux gauges, thermocouples, flow meters, water-additive load cell, and mass-flow controller.data_massloss.csv: Contains vehicle mass data: • Col 1: time step • Col 2: total mass • Col 3: calculated mass-loss rate • Col 4: calculated heat-release rate.data_heatflux.zip: ZIP archive containing 12 HDF5 files (readable via Python h5py). Six files (T_XX) record temperature and six (HF_XX) record incident radiative heat flux to plate sensors located along the driver and passenger sides of the vehicle. Here “XX” indicates panel position: DF, DM, DR, PF, PM, PR (driver/passenger & front/middle/rear).data_ftir.zip: ZIP archive containing FTIR gas-spectra data: a) Info-file.txt: FTIR parameters b) log file: gas cell pressure (hPa) and temperature (K) vs time after ignition c) Spectra/: CSVs with columns: • 1 = wavenumber (cm⁻¹) • 2 = transmitted intensityFile names follow data_point_after_burner_ignition_(timestamp), where timestamp is yyyy_mm_dd_hr_mm_ss_sss (milliseconds = sss).Cabin Fluoride Filter Results.pdf: Report containing analytical results for particulate fluoride and hydrofluoric acid collected from the vehicle cabin fluoride filter.data_airsampling.csv: Summarizes compound masses from air samples: • Col 1: test name • Col 2: sampling location • Cols 3–47: measured compound and unitMass values below detection limit are listed as ; samples not collected are labeled NA.data_airsamplingtime.csv: Contains sampling-pump run times: • Col 1: test name • Col 2: sampling locationEach subsequent column lists pump duration (min). Missing samples are labeled NA.

The main dataset is a 9 MB file of trajectory data (I294_L2_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) and non-automated vehicles on a highway in a suburban environment. Supporting files include aerial reference images for twelve distinct data collection “Runs” (I294_L2_Run_X_ref_image_with_lanes.png, where X equals 5, 28, 30, 36, 38, and 42 for southbound runs and 23, 29, 31, 33, 35, and 41 for northbound runs). Associated centerline files are also provided for each “Run” (I-294-L2-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I294 L2.csv” for more details). The dataset defines eight lanes (four lanes in each direction) using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The southbound lanes are shown visually in I294_L2_lane-2.png through I294_L2_lane-5.png and the northbound lanes are shown visually in I294_L2_lane2.png through I294_L2_lane5.png.

This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed two SAE Level 2 ADAS-equipped vehicles through automated lane change maneuvers and as part of a string once the desired lane was achieved and ACC was enabled. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to automated lane changes and as part of a string. The road segment has four lanes in each direction and covers a major on-ramp and one off-ramp in the southbound direction and one on-ramp as well as two off-ramps in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day.

As part of this dataset, the following files were provided:

• I294_L2_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the L2 test vehicles ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.
• I294_L2_Run_X_ref_image_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound and southbound lanes) for each run X.
• I294_L2_Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. T

We present a dataset tailored for monitoring vehicle activity in a rural environment, specifically the Barranco de Poqueira region, covering the municipalities of Pampaneira, Bubión, and Capileira within the Sierra Nevada National Park, Granada, Spain. The dataset is generated by four Hikvision License Plate Recognition (LPR) cameras, capturing vehicle entries and exits in each village. To enrich the dataset, we include additional contextual details such as vacation calendars, vehicle origins, and socio-demographic information. Spanning from February 2022 to August 2023, the dataset is organized into three files: one with raw data directly from the cameras, another aggregated at the visit level with contextual information, and a third aggregated by vehicles with context details. With potential applications in mobility studies, urban planning, tourism, and socio-demographic analysis, the dataset is structured into three distinct files, encompassing a total of 43 different variables.

The RAW_SMART_POQUEIRA.csv file contains information about 4 variables: num_plate_ID, camera_ID, date, and direction.

The file VEHICLES_SMART_POQUEIRA.csv contains information about 33 variables: num_plate_ID, visit_time, distance, num_holiday, num_workday, num_high_season, num_low_season, entry_in_high_season, entry_in_holiday, nights, visits_dif_weeks, visits_dif_months, total_entries, avg_visit, std_visit, avg_nights, std_nights, avg_holiday, std_holiday, avg_workday, std_workday, avg_high_season, std_high_season, avg_low_season, std_low_season, route, country, km_to_dest, population, avg_gross_income, avg_disposable_income, autonomous_community, and province.

The file VISITS_SMART_POQUEIRA.csv contains information about 26 variables: num_plate_ID, entry_cam, entry_date, entry_time, exit_cam, exit_date, exit_time, visit_time, route, distance, num_holiday, num_workday, num_high_season, num_low_season, nights, visits_dif_weeks, visits_dif_months, entry_in_holiday, entry_in_high_season, country, km_to_dest, population, avg_gross_income, avg_disposable_income, autonomous_community, and province.

This dataset provides comprehensive information about used cars available for sale in the United States. It includes detailed data on various aspects of each vehicle, making it a valuable resource for car buyers, sellers, and data enthusiasts. The dataset contains the following key attributes:

• Brand: The brand or manufacturer of the car.
• Model: The specific model of the car.
• Mileage: The number of miles the car has been driven.
• Year: The manufacturing year of the car.
• Status: Indicates whether the car is new, used, or certified pre-owned.
• Dealer: Information about the dealer or seller offering the car.
• Price: The listed price of the car in USD.

This dataset is ideal for data analysis, machine learning projects, and market research related to the used car industry in the United States. Whether you are interested in predicting car prices, understanding market trends, or simply searching for your next vehicle, this dataset provides a wealth of information to explore.

Data Source: More info on my GitHub repository

Data Format: CSV