This dataset is designed for the detection of persons and cars in surveillance camera footage. It can be utilized for various useful applications, including:

• Security Systems: Enhancing security measures by accurately detecting and tracking persons and cars in real-time surveillance videos.
• Traffic Monitoring: Analyzing traffic patterns, estimating congestion levels, and optimizing traffic flow by detecting and counting cars on roads or at intersections.
• Safety Enhancement: Identifying potential hazards and ensuring public safety by detecting unauthorized access or suspicious activities involving persons and cars.
• Crowd Management: Monitoring crowded areas and public events to ensure safety, identify crowd density, and estimate crowd movement by detecting and tracking persons.
• Parking Systems: Optimizing parking lot management by detecting available parking spots and monitoring the entry and exit of vehicles.
• Smart Cities: Contributing to the development of smart city infrastructure by integrating the detection of persons and cars into intelligent systems for efficient urban planning and management.

This dataset is based on images collected from various sources, including:

https://universe.roboflow.com/radoslaw-kawczak/virat-ve02s

https://universe.roboflow.com/seminar-object-detection/cars-o1ljf

With this dataset, you can train and develop machine learning models capable of accurately detecting persons and cars, thus empowering surveillance and security systems with advanced object recognition capabilities.

Context:

This dataset contains over 7000+ true value cars data across all major tier 1 and tier 2 cities in India which is ready to accept a different owner. The information includes car manufacturer, model, fuel type, year of manufacture to mention a few. Are you ready to showcase your skills and build a model which can predict the true value of an used car? Let's get started!

If you find this dataset useful kindly upvote as a gesture of encouragement. This motivates me to bring in more knowledge to the community.

Content:

• id: Unique ID for every car
• car_name: Name of a car
• yr_mfr: Car manufactured year
• fuel_type: Type of fuel car runs on
• kms_run: Number of kilometers run
• body_type: Car body type. Ex: Sedan, hatchback etc.
• transmission: Type of transmission. Ex: Manual, Automatic
• variant: Car variant
• make: Car manufacturing company
• model: Car model name
• is_hot: Is it a top selling car? Indicates the demand for a car.
• car_availability: Car availability status
• total_owners: How many owners have already owned it?
• car_rating: How good is the car to buy?
• fitness_certificate: Does the car have fitness certificate?
• source: Method of selling a car
• registered_city: City where the car is registered
• registered_state: State where the car is registered
• rto: Regional Transport Office where the car is registered
• city: City where the car is being sold
• times_viewed: Number of times people have shown interest for the car
• assured_buy: Broker assured car
• broker_quote: Price quoted for previous owner (in INR)
• original_price: Original price of a car (in INR)
• emi_starts_from: Opting for EMI? Monthly EMI for the car starts from! (in INR)
• booking_down_pymnt: Decided to buy? Please pay the down payment (in INR)
• ad_created_on: Listed date for selling a car
• reserved: Car reserved status
• warranty_avail: Warranty availability status
• sale_price: Selling price of a car (in INR)

Inspiration:

I've recently started dwelling into machine learning, and web scrapping and I wanted to implement it in real time to understand how these technologies work! To my amazement, Kaggle is a one-stop solution where I can improve my skills. So, here I'm presenting the curated dataset so that anyone who is interested can work on, publish their kernels and compete.

Acknowledgments:

The dataset is curated thoughtfully after scrapping the information from https://www.cars24.com/. This data contains car information only and anything which is not related to the price prediction is removed.

Note: This dataset is created for educational purposes only. Any suggestions on improvement in the quality of the dataset is highly appreciated!

Context

Automobile data holds immense importance as it offers insights into the functioning and efficiency of the automotive industry. It provides valuable information about car models, specifications, sales trends, consumer demographics, and preferences, which car manufacturers and dealerships can leverage to optimize their operations and enhance customer experiences. By analyzing data on vehicle reliability, fuel efficiency, safety ratings, and resale values, the automotive industry can identify trends and implement strategies to produce more reliable and environmentally friendly vehicles, improve safety standards, and enhance the overall value of cars for consumers. Moreover, regulatory bodies and policymakers rely on this data to enforce regulations, set emissions standards, and make informed decisions regarding automotive policies and environmental impacts. Researchers and analysts use car purchase data to study market trends, assess the environmental impact of various vehicle types, and develop strategies for sustainable growth within the industry. In essence, car purchase data serves as a foundation for informed decision-making, operational efficiency, and the overall advancement of the automotive sector.

Content

This dataset comprises diverse parameters relating to car purchases and ownership on a global scale. The dataset prominently incorporates fields such as 'First Name', 'Last Name', 'Country', 'Car Brand', 'Car Model', 'Car Color', 'Year of Manufacture', and 'Credit Card Type'. These columns collectively provide comprehensive insights into customer demographics, vehicle details, and payment information. Researchers and industry experts can leverage this dataset to analyze trends in car purchasing behavior, optimize the customer car-buying experience, evaluate the popularity of car brands and models, and understand payment preferences within the automotive industry.

Dataset Glossary (Column-wise)

• First Name - The first name of the car purchaser.
• Last Name - The last name of the car purchaser.
• Country - The country of residence of the car purchaser.
• Car Brand - The brand or manufacturer of the purchased car.
• Car Model - The specific model of the purchased car.
• Car Color - The color of the purchased car.
• Year of Manufacture - The year the car was manufactured.
• Credit Card Type - The type of credit card used for the car purchase.

Structure of the Dataset

https://i.imgur.com/olZpXsT.png" alt="">

Acknowledgement

The dataset provided here is a simulated example and was generated using the online platform found at Mockaroo. This web-based tool offers a service that enables the creation of customizable mock datasets that closely resemble real data. It is primarily intended for use by developers, testers, and data experts who require sample data for a range of uses, including testing databases, filling applications with demonstration data, and crafting lifelike illustrations for presentations and tutorials. To explore further details, you can visit their website.

Cover Photo by: Freepik

Thumbnail by: Car icons created by Freepik - Flaticon

Average weekly household expenditure on goods and services in the UK. Data are shown by region, age, income (including equivalised) group (deciles and quintiles), economic status, socio-economic class, housing tenure, output area classification, urban and rural areas (Great Britain only), place of purchase and household composition.

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.

This dataset contains the file of vehicle, snowmobile and boat registrations in NYS. Registrations expired more than 2 years are excluded. Records that have a scofflaw, revocation and/or suspension are included with indicators specifying those kinds of records.

The City Traffic and Vehicle Behavior Dataset is a collection of data regarding various factors related to city traffic and vehicle behavior. Here's a description of each column in the dataset:

1. City: The name of the city where the data was collected. 2. Vehicle Type: The type of vehicle involved in the traffic (e.g., car, truck, bus, motorcycle). 3. Weather: The prevailing weather conditions at the time of data collection (e.g., sunny, rainy, snowy). 4. Economic Condition: The economic conditions prevailing in the city (e.g., booming, recession, stable). 5. Day Of Week: The day of the week when the data was collected (e.g., Monday, Tuesday, etc.). 6. Hour Of Day:The hour of the day when the data was collected, typically represented in 24-hour format. 7. Speed: The speed of the vehicles in the traffic, measured in miles per hour (mph) or kilometers per hour (km/h). 8. Is Peak Hour: A binary indicator (0 or 1) indicating whether the data was collected during peak traffic hours. 9. Random Event Occurred: A binary indicator (0 or 1) indicating whether any random event (e.g., accident, road closure) occurred during the data collection period. 10. Energy Consumption: The energy consumption of vehicles, typically measured in fuel consumption or electricity usage.

This dataset can be used for various purposes such as analyzing traffic patterns, studying the impact of weather and economic conditions on traffic, evaluating energy consumption trends, and predicting traffic congestion. Researchers and transportation planners may find this dataset valuable for understanding and improving urban mobility.

People And Cars

## Overview

People And Cars is a dataset for object detection tasks - it contains Cars Lpr People annotations for 854 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This data contains information about people involved in a crash and if any injuries were sustained. This dataset should be used in combination with the traffic Crash and Vehicle dataset. Each record corresponds to an occupant in a vehicle listed in the Crash dataset. Some people involved in a crash may not have been an occupant in a motor vehicle, but may have been a pedestrian, bicyclist, or using another non-motor vehicle mode of transportation. Injuries reported are reported by the responding police officer. Fatalities that occur after the initial reports are typically updated in these records up to 30 days after the date of the crash. Person data can be linked with the Crash and Vehicle dataset using the “CRASH_RECORD_ID” field. A vehicle can have multiple occupants and hence have a one to many relationship between Vehicle and Person dataset. However, a pedestrian is a “unit” by itself and have a one to one relationship between the Vehicle and Person table. 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.

Russia Number of Cars: Privately Owned: Per 1000 Person data was reported at 304.962 Unit in 2017. This records an increase from the previous number of 293.977 Unit for 2016. Russia Number of Cars: Privately Owned: Per 1000 Person data is updated yearly, averaging 155.950 Unit from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 304.962 Unit in 2017 and a record low of 58.500 Unit in 1990. Russia Number of Cars: Privately Owned: Per 1000 Person data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.RAD005: Number of Cars Privately Owned per 1000 Persons.

This dataset captures the growth of India's population and registered vehicles over the years 2015 to 2022. It highlights the rising number of vehicles alongside population trends, offering insights into urbanization, mobility patterns, and transportation demand.

The dataset reveals a consistent increase in both population and vehicle ownership, with two-wheelers and cars showing the most significant growth. Additionally, the sex ratio indicates a gradual decline in the number of males per 100 females.

This dataset is useful for trend analysis, policy-making, transportation research, and urban planning, helping to understand how demographic changes influence vehicle ownership in India.

Introduction

Welcome to the US English Language In-car Speech Dataset, a comprehensive collection of audio recordings designed to facilitate the development of speech recognition models specifically tailored for in-car environments. This dataset aims to support research and innovation in automotive speech technology, enabling seamless and robust voice interactions within vehicles for drivers and co-passengers.

Speech Data

This dataset comprises over 5,000 high-quality audio recordings collected from various in-car environments. These recordings include scripted wake words and command-type prompts.

Participant Diversity:

- Speakers: 50+ native English speakers from the FutureBeeAI Community.

- Regions: Ensures a balanced representation of United States of America1 accents, dialects, and demographics.

- Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.

Recording Nature: Scripted wake word and command type of audio recordings.

- Duration: Average duration of 5 to 20 seconds per audio recording.

- Formats: WAV format with mono channels, a bit depth of 16 bits. The dataset contains different data at 16kHz and 48kHz.

Dataset Diversity

Apart from participant diversity, the dataset is diverse in terms of different wake words, voice commands, and recording environments.

Different Automobile Related Wake Words: Hey Mercedes, Hey BMW, Hey Porsche, Hey Volvo, Hey Audi, Hi Genesis, Hey Mini, Hey Toyota, Ok Ford, Hey Hyundai, Ok Honda, Hello Kia, Hey Dodge.

Different Cars: Data collection was carried out in different types and models of cars.

Different Types of Voice Commands:

- Navigational Voice Commands

- Mobile Control Voice Commands

- Car Control Voice Commands

- Multimedia & Entertainment Commands

- General, Question Answer, Search Commands

Recording Time: Participants recorded the given prompts at various times to make the dataset more diverse.

- Morning

- Afternoon

- Evening

Recording Environment: Various recording environments were captured to acquire more realistic data and to make the dataset inclusive of various types of noises. Some of the environment variables are as follows:

- Noise Level: Silent, Low Noise, Moderate Noise, High Noise

- Parking Location: Indoor, Outdoor

- Car Windows: Open, Closed

- Car AC: On, Off

- Car Engine: On, Off

- Car Movement: Stationary, Moving

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

Participant Metadata: Unique identifier, age, gender, country, state, district, accent, and dialect.

Other Metadata: Recording transcript, recording environment, device details, sample rate, bit depth, file format, recording time.

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of English voice assistant speech recognition models.

License

This US English In-car audio dataset is created by FutureBeeAI and is available for commercial use.

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for Leicester and compare this with national statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsCar availabilityThis dataset provides Census 2021 estimates on the number of cars or vans available to members of households for England and Wales. The estimates are as at Census Day, 21 March 2021.Definition: The number of cars or vans owned or available for use by household members.Vehicles included:pick-ups, camper vans and motor homesvehicles that are temporarily not working vehicles that have failed their MOTvehicles owned or used by a lodgercompany cars or vans if they're available for private useVehicles not included:motorbikes, trikes, quad bikes or mobility scootersvehicles that have a Statutory Off Road Notification (SORN)vehicles owned or used only by a visitor vehicles that are kept at another address or not easily accessedThe number of cars or vans in an area relates only to households. Cars or vans used by communal establishment residents are not counted.Households with 10 to 20 cars or vans are counted as having only 10.Households with more than 20 cars or vans were treated as invalid and a value imputed.This dataset includes data for Leicester city and England overall.

Total vehicle registration counts per month by county

This Mikrozensus special survey consists of two parts of the traffic statistics: motor vehicles and driving licenses The first part is a repetition of the Mikrozensus special survey from September 1971 (Mikrozensus MZ7103) on motor vehicles and their road performance. The results of this survey were the basis for studies and measure in the fields of traffic policy, road safety and the general transport. By repeating this special survey, new data for these fields is collected. Moreover, changes due to the strong increase in the number of vehicles are are evaluated. More attention, than in the study from 1971, is also given to the energy consumption resulting from the performance of the vehicle. The questions are only on certain types of vehicles which are of special interest due to their road performance (passenger cars, estate cars, motorcycles, mopeds). Preliminary, important vehicle data and personal data of its owner are are collected. Then the questions are on the mileage at the time the vehicle was bought and at the time of the survey, as well as on the last working day’s and last weekend’s mileage. Owner’s of passenger- or estate cars are also asked how many people usually drive the car (as driver or passenger) from Monday to Friday as well as on the weekends and for what what purpose the car is mainly used. Up until now, statistics on driving licenses have only been conducted in some states on varying form (and therefore not really comparable). The results of this survey should provide information for the whole federal territory on the number of people with driving licenses, the data of the acquiring of the licence and the groups these licenses refer to.

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for Leicester MSOAs and compare this with Leicester overall statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsCar availabilityThis dataset provides Census 2021 estimates on the number of cars or vans available to members of households for England and Wales. The estimates are as at Census Day, 21 March 2021.Definition: The number of cars or vans owned or available for use by household members.Vehicles included:pick-ups, camper vans and motor homesvehicles that are temporarily not working vehicles that have failed their MOTvehicles owned or used by a lodgercompany cars or vans if they're available for private useVehicles not included:motorbikes, trikes, quad bikes or mobility scootersvehicles that have a Statutory Off Road Notification (SORN)vehicles owned or used only by a visitor vehicles that are kept at another address or not easily accessedThe number of cars or vans in an area relates only to households. Cars or vans used by communal establishment residents are not counted.Households with 10 to 20 cars or vans are counted as having only 10.Households with more than 20 cars or vans were treated as invalid and a value imputed.This dataset includes data for Leicester city MSOAs.

We update the data monthly, so it"s accurate up to the end of the previous month. Registration is the process where we add a vehicle’s details to the MVR and issue its number plates. It is not the same thing as vehicle licensing, also called ‘rego’. To give you a quick overview of the data, see the charts in the ‘Attributes’ section below. These will give you information about each of the attributes (variables) in the dataset.Each chart is specific to a variable, and shows all data (without any filters applied).Motor Vehicle Register data - field descriptionsDue to the size of the data we recommend using the following for direct downloads of the data.Download this data as zipped CSV filesFuel consumption (litres / 100km) is for cars driving in urban areas (Column FC_Urban), on motorways (Column FC_Extra_Urban) and a combination of both (Column FC_Combined). Values range from 1 to 60.Greenhouse gas emissions are the SGGs (synthetic greenhouse gases) that airconditioning units produce.The data is added to the MVR when a new or used vehicle is first registered in New Zealand.Description of attributes for fuel consumption (27) and synthetic greenhouse gas (34)Data reuse caveatsAs per license.We’ve taken reasonable care in compiling this information, and provide it on an ‘as is, where is’ basis. We are not liable for any action taken on the basis of the information. For further information see the Waka Kotahi website, as well as the terms of the CC-BY 4.0 International license under which we publish this data.CC-BY 4.0 International licence detailsVariables in the dataset are formatted for analytical use. This can result in attribute charts that may not appear meaningful, and are not suitable for broader analysis or use. In addition, some variables are not mutually exclusive and should not be considered in isolation. As such, these charts should not be taken and used directly as analysis of the overall data.Data quality statement:This data relates to vehicles, not people.An entry certifier enters the data manually into the MVR when someone first registers a new or used vehicle in New Zealand.We have included some information about vehicle registered owners live. This is based on the most recent information we have about their physical address. To make sure it isn’t possible to identify a person in the data, we have provided this at Territorial Authority (TA) level. A TA is a broad geographical area defined under the Local Government Act 2002 as a city council or district council. There are 67 TAs consisting of 12 city councils, 53 districts, Auckland Council and Chatham Island Council.We haven’t included vehicles that belong to people with a confidential listing.We have restricted the Vehicle Identification Number (VIN) to the first 11 characters – these are generic and don’t identify specific vehicles.Data quality caveats:Many of the fields in the (MVR) are free text fields, which means there may be spelling mistakes and other human errors. The data is verified at time of entry, but there is potential for data to be entered incorrectly.We have algorithmically cleaned the data to correct identified errors (particularly with respect to a vehicle’s make and model). However, due to the large number of vehicles on the Register we may not have corrected some information.Additionally, some variables may be subject to differences in how people have recorded details – for example, manufacturers release a variety of sub-models and these may not be referred to, or put into the system, in the same way.We have made our cleaning code open source.Vehicle make and model cleansing code (GitHub)The below links are used to determine fuel consumption and CO2 emissions that are then entered when registering vehicle. This is mandatory and not optional. Data is first added to landata.importer.fuelsaver.govt.nz/certifier/www.greenvehicleguide.gov.au/www.fueleconomy.gov/www.vcacarfueldata.org.uk/ UPDATE: The Motor Vehicle Register (MVR) dataset now contains information on fuel consumption and greenhouse gas emissions.

Wheelchair People Cars

## Overview

Wheelchair People Cars is a dataset for object detection tasks - it contains Wheelchair People Cars annotations for 3,190 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).