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

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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

Number of Cars: Privately Owned: Per 1000 Person: CF: City of Moscow data was reported at 282.272 Unit in 2022. This records a decrease from the previous number of 297.353 Unit for 2021. Number of Cars: Privately Owned: Per 1000 Person: CF: City of Moscow data is updated yearly, averaging 232.100 Unit from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 306.351 Unit in 2017 and a record low of 69.800 Unit in 1990. Number of Cars: Privately Owned: Per 1000 Person: CF: City of Moscow 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.

The data in this dataset comes from the Common Questionnaire for Transport Statistics, developed and surveyed by Eurostat in cooperation between the United Nations Economic Commission for Europe (UNECE) and the International Transport Forum (ITF) at OECD.

The Common Questionnaire is not supported by a legal act, but is based on a gentlemen's agreement with the participating countries; its completeness varies from country to country.

Eurostat’s datasets based on the Common Questionnaire cover annual data for the EU Member States, EFTA states and Candidate countries to the EU. Data for other participating countries are available through the ITF and the UNECE. In total, comparable transport data collected through the Common Questionnaire is available for close to 60 countries worldwide.

The Common Questionnaire collects aggregated annual data on:

• Railway transport
• Road transport
• Inland waterways transport
• Oil pipelines transport
• Road traffic - vehicle-kilometres
• Buses (and coaches)

For each mode of transport, the Common Questionnaire covers some or all of the following sub-modules (the number of questions/variables within each sub-module varies between the different modes of transport):

• Infrastructure (All modes)
• Transport equipment (Rail, Road and Inland Waterways)
• Enterprises, economic performance and employment (All modes)
• Traffic (Rail, Road and Inland Waterways)
• Transport measurement (All modes).

As its name suggests, the theme "Road traffic" focuses on "traffic" only, on road:

• of national vehicle stocks, on national and foreign territories, by type of road; and by vehicle category, fuel type and age. These data should rely on odometer readings;
• of national- and foreign-registered vehicles on national territories. These data should be issued from road censuses.

The theme “Buses and coaches” covers detailed information on road “traffic” (vkm) and “transport measurement” (passengers, passenger-km) performed by buses and coaches.

The data collection on Common Questionnaire was streamlined twice in the recent years:

• In 2013, among other changes, the collection of employment data was terminated;
• In 2019, among other changes, the collection of gas pipeline data was terminated, and the number of “Buses” indicators was reduced.

The Common Questionnaire is completed by the competent national authorities. The responsibility for completing specific modules (e.g. Transport by Rail) or part of modules (e.g. Road Infrastructure) may be delegated to other national authorities in charge of specific fields.

This dataset contains number of vehicles in GCC countries from 2008 - 2018 . Data from GCC Statistical center.Follow datasource.kapsarc.org for timely data to advance energy economics research.

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 is a cars data set which is used to perform a data analysis process about this data. This data set also used to predict the type of car based on the features input like MPG, Cylinders, Displacement, Horsepower, Weight, Acceleration, Year of publishing and Country of origin. Use this data set to practice and implement the new techniques.

Cars and how much fuels they use

• Cars 1 Data is collected from link ### Dataset of electric passenger cars with their specifications This dataset lists all fully electric passenger cars with their attributes (properties). The collection does not contain data on plug-in hybrid cars and electric cars from the so-called “range extenders”. Hydrogen cars were also not included in the dataset due to the insufficient number of mass-produced models and different (compared to EV) specificity of the vehicle, including the different charging methods. The dataset includes cars that, as of 2 December, 2020, could be purchased in Poland as new at an authorized dealer and those available in public and general presale, but only if a publicly available price list with equipment versions and full technical parameters was available. The list does not include discontinued cars that cannot be purchased as new from an authorized dealer (also when they are not available in stock). The subject of the study is only passenger cars, the main purpose of which is to transport people (inter alia without including vans intended for professional deliveries). The dataset of electric cars includes all fully electric cars on the primary market that were obtained from official materials (technical specifications and catalogs) provided by automotive manufacturers with a license to sell cars in Poland. These materials were downloaded from their official websites. In the event that the data provided by the manufacturer were incomplete, the information was supplemented with data from the SAMAR AutoCatalog (link can be found in References section). The database consisting of 53 electric cars (each variant of a model – which differs in terms of battery capacity, engine power, etc. – is treated as separate) and 22 variables (25 variables, including make, model and “car name” merging these two previous).
• FEV data are collected from link ### Electric vehicle population This dataset shows the Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) that are currently registered through Washington State Department of Licensing (DOL).
• Data collected from link

Title: “Car Sales Price Prediction Using ANN” Introduction: Problem Statement As a vehicle salesperson, the main objective is to estimate the overall amount that consumers would spend on buying a new vehicle. The main Objective of this model is to predict the net amount that a customer would likely spend.

Model We create a model that can estimate the overall amount that consumers would spend given the following characteristics: customer name, customer email, country, gender, age, annual salary, credit card debt, and net worth. We can use Machine Learning or Deep Learning Techniques, here we are using ANN for prediction. Here we are actually predicting using some random values, after that we are predicting using the training dataset. After that we are implementing and predicting using linear Regression and Ridge Regression. Then finally we are comparing the predicted amount with the actual amount.

Algorithm Neural Network is a series of algorithms that are trying to mimic the human brain and find the relationship between the sets of data. It is being used in various use-cases like in regression, classification, Image Recognition and many more.

As we have talked above, if neural networks try to mimic the human brain then there might be the difference as well as the similarity between them. Let us talk briefly about it.

Some major differences between them are biological neural network does parallel processing whereas the Artificial neural network does series processing also in the former one processing is slower (in millisecond) while in the latter one processing is faster (in a nanosecond).

A neural network has many layers and each layer performs a specific function, and as the complexity of the model increases, the number of layers also increases that why it is known as the multi-layer perceptron.

The purest form of a neural network has three layers input layer, the hidden layer, and the output layer. The input layer picks up the input signals and transfers them to the next layer and finally, the output layer gives the final prediction and these neural networks have to be trained with some training data as well like machine learning algorithms before providing a particular problem.

Working of ANN

At First, information is feed into the input layer which then transfers it to the hidden layers, and interconnection between these two layers assign weights to each input randomly at the initial point. And then bias is added to each input neuron and after this, the weighted sum which is a combination of weights and bias is passed through the activation function. Activation Function has the responsibility of which node to fire for feature extraction and finally output is calculated. This whole process is known as Forward Propagation. After getting the output model to compare it with the original output the error is known and finally, weights are updated in backward propagation to reduce the error and this process continues for a certain number of epochs (iteration). Finally, model weights get updated and prediction is done.

**Dataset Description **

The dataset was downloaded from Kaggle website. The website link is given below.it is about “Car Sales Price Prediction”. It includes five hundred rows and nine columns. It contains numeric, categorical and character. The five hundred rows consists of the data collected from five hundred people. The nine columns encompass various attributes for collecting the data. The nine attributes are customer name, customer email, country, gender, age, annual salary, credit card debt, net worth, car purchase amount.

Customer name-

The customer name includes the name of the customer. It contains the first name and last name of the customer. It is a character data set. It includes the names of the five hundred customers who are approaching the vehicle salesperson for getting the required information for buying a car.

Customer email-

This data set includes the Email address of the customer. It is the personal identifiable information of the customer. It is a varchar data set.

**Country- **

The country data set gives the information about which country the customer belongs to. It is a character data set .We got data from more than two hundred countries.

Gender–

This data set is a categorical (string) dataset. It includes the gender of the customer. It's given 1 for male and 0 for female. There are two hundred and fifty three males and two forty seven females.

Age-

It is a numerical data set which includes the age of the customer.

**salary- **

Different income ways is been plotted like basic pay, benefits, total pay etc....

Credit card debt-

Generally, credit card debt refers to the accumulated outstanding balances that many borrowers carry over from month to month. It is also a float data set.

Net worth-

It is a pers...

Introduction

Welcome to the Gujarati 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 Gujarati speakers from the FutureBeeAI Community.

- Regions: Ensures a balanced representation of Gujarat1 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 Gujarati voice assistant speech recognition models.

License

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

Enterprise Rent-A-Car Store Locations

Details of Enterprise Rent-A-Car Locations Across Various Regions

By Jeff [source]

About this dataset

This richly detailed dataset provides an extensive view into the various store locations of Enterprise Rent-A-Car, a prominent car rental company with multiple branches around the world. The information encapsulated within this dataset represents a comprehensive snapshot of different aspects related to each branch location making it indispensable for market researchers, data analysts and other professionals in need of such exhaustive data.

The dataset includes vital details about every single branch like its unique identifier number (loc_number), name of the location (loc_name), type of location such as whether it's situated at an airport or in city center (loc_type), and not just these; it additionally furnishes specific geographical coordinates mapped by longitude and latitude.

That's not all - the brand value is firmed up with specifics like complete street address (address_1) which also mentions the country and state where each branch stands tall. It even contains critical communication handle: phone number for every existing outlet.

What gives this truly international set its distinctive character is that it incorporates data down to postcode level granularity, hence providing intricate locational details. Also noted meticulously are group or branch identifier numbers that may represent unique numerical indications allotted to specific series or regions of stores/branches.

In terms of temporal updates on datasets, there are two significant timestamps in play; one posts recent updates (update_timestamp) while InsertUpdateTime records date/time when a new record was inserted or when any key feature edit took place.

Moreover, last column maintains brand identity constant across all records denoting 'Enterprise Rent-A-Car' as implied brand responsible for all enlisted locations - thereby ingraining more trust into this hard-to-attain detailed resource.

This curated file enterprise_station.csv efficiently manages enriched information about Enterprise’s extensive portfolio worldwide allowing interested users deeper dive into operational analytics. Therefore aspiring organizations can leverage potency made available through this dataset both at macro-region levels or individual touch-points.

This dataset is just a ping away for your customized operational, market research or academic needs! Feel free to connect with us at infobarkingdata.com for tailor-made data solutions catering to your distinctive orientation and requirements

How to use the dataset

Here are some steps guiding you to make optimum use of this dataset:

Understanding the Variables: The first step is to understand what each variable in the dataset represents. For example, 'loc_name' refers to the name of each Enterprise Rent-A-Car location while 'longitude' and 'latitude' provide geographical coordinates for these locations. Understanding these variables will help derive practical insights from your analysis.

Location Expansion Strategy: If you're using this data set as an enterprise decision-maker, it could support your planning for expansion or consolidation activities by providing insights into existing patterns like concentration areas and gaps in certain geographical areas.

Brand Analysis: Use the brand column to perform any comparative analysis or research on brand performance across different regions/states/countries.

Customer Accessibility Study: If you are researching from a customer perspective; observe trends in terms of proximity to airports or within city centers by categorizing them into types based on 'loc_type'. Geographical mapping can also be done using latitude and longitude values.

Predictive Models: With additional external datasets including socio-economic indicators per region/city/state/postal code could lead towards developing predictive models forecasting profitability or demand etc., thereby informing business-level decisions.

Remember that this dataset may be updated regularly so always ensure you have got the latest updated version before beginning any tracking/monitoring studies based on temporal factors. Always respect privacy regulations when handling personal identifying information where applicable. This guide does not encourage practices contravening such regulations

Research Ideas

• Market Analysis: This dataset can be used for studying the geographical distribution of Enterprise Rent-A-Car locations across various countries and states. It can help in identifying markets where the compa...

In this file there are statistics for a number of variables broken down by Malmö’s different areas over time. Sources Unless otherwise stated, the statistics in this database are retrieved from Statistics Sweden’s (SCB) regional database, Skånedatabasen or from Statistics Sweden’s area statistics database (OSDB). The Skåne database and OSDB show data from several different sources that Statistics Sweden has compiled on a geographical level. The statistics only cover persons who are part of the population registered in the population. Therefore, persons without a residence permit, such as asylum seekers, and persons who simply have not registered in the municipality are not included. Statistics Sweden does not provide statistics on which language residents speak, which religion you belong to or what ethnicity or political views you have. Therefore, such data is not available here either. However, the Electoral Authority reports election results per constituency on its website val.se. There are statistics from the last election as well as several previous elections available. Please note, however, that the constituencies do not necessarily follow the division of the city made here. Update The data is updated every spring as Statistics Sweden releases the figures to the municipality. Most variables are available for the year before. However, income and employment data are released with another year’s backlog. Unless otherwise stated, the date of measurement is 31 December of each year. Geographical breakdown Unless otherwise stated, the data is available for Malmö as a whole and broken down into urban areas (5 pieces), districts (10 pieces) and subareas (136 pieces). In addition to these, there is a residual post that contains the people who are not written in a specific place in the municipality, have protected identity and more. These people are also part of the total. In several of the subareas there are no or only a few registered population registers. Therefore, no data are reported for these areas. Examples of such sub-areas are parks such as Pildammsparken and Kroksbäcksparken and industrial areas such as Fosieby Industriområde and Spillepengen. Privacy clearance In order to protect the identity of individuals, the data is confidentially audited. This means that small values are suppressed, i.e. replaced by empty cells. However, the values are included in summaries. In general, the following rules apply: • No statistics are reported for geographical areas with very few housing. No cells with fewer than 5 individuals are reported. For data classified as sensitive (e.g. income and country of birth), larger values can also be suppressed. • In cases where a subcategory (e.g. a training category) is too small to be accounted for, all categories are often suppressed. Please use the numbers, but use “City Office, Malmö City” as the source.

Tesla Deaths

An In-depth Look into Driver, Occupant, and Pedestrian Deaths

By [source]

About this dataset

This dataset reveals an in-depth analysis of tragic Tesla vehicle accidents that have resulted in the death of a driver, occupant, cyclist, or pedestrian. It contains an extensive amount of information related to the fatal incidents including the date and location of each crash, model type involved and if Autopilot was enabled at the time. Every case is given its own unique identifier for easy reference and thorough review. Now is your chance to dive deep into these records to truly understand what happened during those tragic events and how we can prevent them from happening again

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For more datasets, click here.

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How to use the dataset

This dataset provides a comprehensive overview of the Tesla vehicle accidents that have resulted in fatalities. It includes details on the date and location of each incident, model involved, crash description, fatalities, and Autopilot usage. This dataset can be used to analyze the frequency and locations of these fatal accidents as well as gain valuable insights into potential safety risks associated with driving/operating Tesla vehicles.

To begin your analysis with this dataset, start by reading through the information contained in each column: Case # (unique identifier for each case), Year (year of incident), Date (date of incident), Country (country where the accident occurred), State (state where the accident occurred), Description (description of crash), Model (model of Tesla vehicle involved) Source(source). All columns are mandatory for analysis.

Once you have familiarized yourself with this data set, consider looking at how many fatal accidents there have been over time by creating line graphs to show trends over years or states. You may also decide to review incidents based on geographic location or model type to determine which locations or model types may require further investigation and testing in terms of Tesla's safety features. Additionally consider using descriptive analytics such as means and medians to determine if certain models are more prone to accidents than others compared against one another; while also exploring if Autopilot feature usage has any correlation to higher rates/ numbers involving fatalities .

Using this data set can help increase awareness about potential safety risk related issues associated with driving/ operating a Tesla vehicle allowing individuals involved production side decisions or investing decisions have a better understanding when entering such fields . We do recommend however that when conducting your analysis , it’s important understand proper ways for handling missing data points so that users can get an accurate picture related current issues surrounding vehicular mistakes involving teslas vehicles

Research Ideas

• Estimating the safety risk of Autopilot feature usage in different countries and states. By analyzing the differences in fatalities between Tesla vehicles operating with and without Autopilot, researchers can infer risks associated with Autopilot use.
• Examining the relation between driver / occupant fatalities and Tesla vehicle models over time. Through observation of trends in model-specific fatalities across years, engineers may be able to identify vulnerabilities or safety features that should be improved upon in the next version of a car model.
• Creating predictive models to assess crash probability per country or state based on uncontrollable factors such as road environment or traffic conditions by analyzing large numbers of reported accidents for which there were no fatalities but had similar characteristics (time of day, weather conditions, speed limit etc). Technological developments such as self-driving cars could potentially benefit from this type of predictive evaluation method to enhance their safety by improving preventive measures ahead of accidents occurring

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Tesla Deaths - Deaths (3).csv | Column name | Description ...

This Eurobarometer survey is about people's perception of robots, autonomous cars and drones. With these technologies becoming more mainstream, it is important to understand what people think and to assess the extent to which people will accept robots performing certain functions. It builds on a previous study conducted in 2012, and looks at ways in which attitudes may have changed over the last two years.

While this development is accelerating, most people appear unaware of how much responsibility we have already outsourced to machines and intelligent algorithms. Thus, the first goal of this study would be to determine the level of awareness among European citizens of this phenomenon.

Secondly, we want to determine to what extent people are open to these technological advances. Are they willing to use autonomous vehicles? Do they mind planes flying more or less on their own? To advance with our research in this area and to faciliate uptake, we have to have a better understanding of what know about autonomous systems and how they feel about them.

While it is admittedly difficult for people to really assess how they are going to react to future technologies, answers to these and similar questions can at least provide an indication of people's feelings.

Thirdly, this study would emphasise DG CONNECT's stake in this important, forward-looking topic. Autonomoy will become a key regulatory challenge in the future, and DG CONNECT can become the worldwide frontrunner in this field.

To reach these objectives, the idea is to conduct a Eurobarometer survey in all 28 EU MS.

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

AU: Passenger Cars: Per One Million Units of Current USD GDP data was reported at 10.747 Ratio in 2020. This records an increase from the previous number of 10.686 Ratio for 2019. AU: Passenger Cars: Per One Million Units of Current USD GDP data is updated yearly, averaging 12.420 Ratio from Dec 1994 (Median) to 2020, with 26 observations. The data reached an all-time high of 26.972 Ratio in 2001 and a record low of 8.189 Ratio in 2012. AU: Passenger Cars: Per One Million Units of Current USD GDP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Australia – Table AU.OECD.ITF: Motor Vehicles Statistics: OECD Member: Annual. PASSENGER CARS The stock of road motor vehicles is the number of road motor vehicles registered at a given date in a country and licenced to use roads open to public traffic. This includes road vehicles exempted from annual taxes or licence fee; it also includes imported second-hand vehicles and other road vehicles according to national practices. It should not include military vehicles.; PASSENGER CARS A passenger car is a road motor vehicle, other than a moped or a motorcycle, intended for the carriage of passengers and designed to seat no more than nine people (including the driver). It refers to category M1 of the UN Consolidated Resolution on the Construction of Vehicles. Passenger cars, vans designed and used primarily for transport of passengers, taxis, hire cars, ambulances and motor homes are not included. Light goods road vehicles, motor-coaches and buses and mini-buses/mini-coaches are not included. Microcars (needing no permit to be driven), taxis and passenger hire cars, provided that they have fewer than ten seats, are included.

Dataset Source: https://machinehack.com/hackathon/predict_accident_risk_score_for_unique_postcode/data

Data set is for private consumption for the competition.

According to IBEF “Domestic automobiles production increased at 2.36% CAGR between FY16-20 with 26.36 million vehicles being manufactured in the country in FY20.Overall, domestic automobiles sales increased at 1.29% CAGR between FY16-FY20 with 21.55 million vehicles being sold in FY20”.The rise in vehicles on the road will also lead to multiple challenges and the road will be more vulnerable to accidents.Increased accident rates also leads to more insurance claims and payouts rise for insurance companies.

In order to pre-emptively plan for the losses, the insurance firms leverage accident data to understand the risk across the geographical units e.g. Postal code/district etc.

In this challenge, we are providing you the dataset to predict the “Accident_Risk_Index” against the postcodes.Accident_Risk_Index (mean casualties at a postcode) = sum(Number_of_casualities)/count(Accident_ID)

Working example:

Train Data (given)
Accident_ID Postcode Number_of_casualities 1 AL1 1JJ 2 2 AL1 1JP 3 3 AL1 3PS 2 4 AL1 3PS 1 5 AL1 3PS 1 Modelling Train Data (Rolled up at Postcode level)
Postcode Derived_feature1 Derived_feature2 Accident_risk_Index AL1 1JJ _ _ 2 AL1 1JP _ _ 3 AL1 3PS _ _ 1.33 The participants are required to predict the 'Accident_risk_index' for the test.csv and against the postcode on the test data.

Then submit your 'my_submission_file.csv' on the submission tab of the hackathon page.

Pro-tip: The participants are required to perform feature engineering to first roll-up the train data at postcode level and create a column as “accident_risk_index” and optimize the model against postcode level.

Few Hypothesis to help you think: "More accidents happen in the later part of the day as those are office hours causing congestion"

"Postal codes with more single carriage roads have more accidents"

(***In the above hypothesis features such as office_hours_flag and #single _carriage roads can be formed)

Additionally, we are providing you with road network data (contains info on the nearest road to a postcode and it's characteristics) and population data (contains info about population at area level). This info are for augmentation of features, but not mandatory to use.

The provided dataset contains the following files:

Train: 4,84,042 rows x 27 columns

Test: 1,15,958 rows x 27 columns

train.csv & test.csv:

'Accident_ID', 'Police_Force', 'Number_of_Vehicles', 'Number_of_Casualties', 'Date', 'Day_of_Week', 'Time', ‘Local_Authority_(District)', 'Local_Authority_(Highway)', '1st_Road_Class', '1st_Road_Number', 'Road_Type', 'Speed_limit', '2nd_Road_Class', '2nd_Road_Number', 'Pedestrian_Crossing-Human_Control', 'Pedestrian_Crossing-Physical_Facilities', 'Light_Conditions', ‘'Weather_Conditions', 'Road_Surface_Conditions', 'Special_Conditions_at_Site', 'Carriageway_Hazards', 'Urban_or_Rural_Area', 'Did_Police_Officer_Attend_Scene_of_Accident', 'state', 'postcode', 'country'

Population: 8,035 rows x 10 columns

population.csv:

​​'postcode', 'Rural Urban', 'Variable: All usual residents; measures: Value', 'Variable: Males; measures: Value', 'Variable: Females; measures: Value', ‘Variable: Lives in a household; measures: Value', ‘Variable: Lives in a communal establishment; measures: Value', 'Variable: Schoolchild or full-time student aged 4 and over at their non term-time address; measures: Value', 'Variable: Area (Hectares); measures: Value', 'Variable: Density (number of persons per hectare); measures: Value'

Road Network: 91,566 rows x 8 columns

roads_network.csv:

'WKT', 'roadClassi', ‘roadFuncti', 'formOfWay', 'length', 'primaryRou', 'distance to the nearest point on rd', 'postcode’

Overview Swiss Re is one of the largest reinsurers in the world headquartered in Zurich with offices in over 25 countries. Swiss Re’s core expertise is in underwriting in life, health, as well as the property and casualty insurance space whereas its tech strategy focuses on developing smarter and innovative solutions for clients’ value chains by leveraging data and technology.

The company’s vision is to make the world more resilient. Swiss Re believes in applying fresh perspectives, knowledge and capital to anticipate and manage risk to create smarter solutions and help the world rebuild, renew and move forward.About 1300 professionals that work in the Swiss Re Global Business Solutions Center (BSC), Bangalore combine experience, expertise and out-of-the-box thinking to bring Swiss Re's core business to life by creating new business opportunities.

Egypt Number of Registered Vehicles: Private Cars data was reported at 5,229,787.000 Unit in 2023. This records an increase from the previous number of 5,111,892.000 Unit for 2022. Egypt Number of Registered Vehicles: Private Cars data is updated yearly, averaging 2,437,543.000 Unit from Dec 1995 (Median) to 2023, with 29 observations. The data reached an all-time high of 5,238,260.000 Unit in 2019 and a record low of 1,052,786.000 Unit in 1995. Egypt Number of Registered Vehicles: Private Cars data remains active status in CEIC and is reported by Ministry of Interior. The data is categorized under Global Database’s Egypt – Table EG.TA001: Number of Registered Vehicles: Annual.

Worldwide car sales grew to around ** million automobiles in 2024, up from around **** million units in 2023. Throughout 2020 and 2021, the sector experienced a downward trend on the back of a slowing global economy, while COVID-19 and the Russian war on Ukraine contributed to shortages in the automotive semiconductor industry and further supply chain disruptions in 2022. Despite these challenges, 2023 and 2024 sales surpassed pre-pandemic levels and are forecast to keep rising through 2025 and 2026. Covid-19 hits car demand It had been estimated pre-pandemic that international car sales were on track to reach ** million. While 2023 sales are still far away from that goal, this was the first year were car sales exceeded pre-pandemic values. The automotive market faced various challenges in 2023, including supply shortages, automotive layoffs, and strikes in North America. However, despite these hurdles, the North American market was among the fastest-growing regions in 2024, along with Eastern Europe and Asia, as auto sales in these regions increased year-on-year. Chinese market recovers After years of double-digit growth, China's economy began to lose steam in 2022, and recovery has been slow through 2023. China was the largest automobile market based on sales with around **** million units in 2023. However, monthly car sales in China were in free-fall in April 2022 partly due to shortages, fears over a looming recession, and the country grappling with the COVID-19 pandemic. By June of that same year, monthly sales in China were closer to those recorded in 2021.

Key information about Nigeria Registered Motor Vehicles

• Nigeria Registered Motor Vehicles was reported at 11,605.207 Unit th in Dec 2020.
• This records an increase from the previous number of 3,750.000 Unit th for Dec 2015.
• Nigeria Registered Motor Vehicles data is updated yearly, averaging 3,090.000 Unit th from Dec 2005 to 2020, with 12 observations.
• The data reached an all-time high of 11,605.207 Unit th in 2020 and a record low of 1,747.000 Unit th in 2005.
• Nigeria Registered Motor Vehicles data remains active status in CEIC and is reported by International Organization of Motor Vehicle Manufacturers. The data is categorized under World Trend Plus’s Association: Automobile Sector – Table RA.OICA.VIU: Vehicle In Use: by Country (Discontinued).

Kenya Road Transport: Number of Motor Vehicles: Registered data was reported at 4,973,017.000 Unit in 2024. This records an increase from the previous number of 4,784,156.000 Unit for 2023. Kenya Road Transport: Number of Motor Vehicles: Registered data is updated yearly, averaging 2,210,907.000 Unit from Dec 2004 (Median) to 2024, with 21 observations. The data reached an all-time high of 4,973,017.000 Unit in 2024 and a record low of 711,142.000 Unit in 2004. Kenya Road Transport: Number of Motor Vehicles: Registered data remains active status in CEIC and is reported by Kenya National Bureau of Statistics. The data is categorized under Global Database’s Kenya – Table KE.TA: Road Transport: Number of Motor Vehicles: Registered.

Denmark DK: Passenger Cars: per One Thousand Inhabitants data was reported at 454.779 Ratio in 2020. This records an increase from the previous number of 446.132 Ratio for 2019. Denmark DK: Passenger Cars: per One Thousand Inhabitants data is updated yearly, averaging 378.106 Ratio from Dec 1994 (Median) to 2020, with 27 observations. The data reached an all-time high of 454.779 Ratio in 2020 and a record low of 307.832 Ratio in 1995. Denmark DK: Passenger Cars: per One Thousand Inhabitants data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Denmark – Table DK.OECD.ITF: Motor Vehicles Statistics: OECD Member: Annual. PASSENGER CARS Data are based on information from the national licencing database.; PASSENGER CARS The stock of road motor vehicles is the number of road motor vehicles registered at a given date in a country and licenced to use roads open to public traffic. This includes road vehicles exempted from annual taxes or licence fee; it also includes imported second-hand vehicles and other road vehicles according to national practices. It should not include military vehicles.; PASSENGER CARS A passenger car is a road motor vehicle, other than a moped or a motorcycle, intended for the carriage of passengers and designed to seat no more than nine people (including the driver). It refers to category M1 of the UN Consolidated Resolution on the Construction of Vehicles. Passenger cars, vans designed and used primarily for transport of passengers, taxis, hire cars, ambulances and motor homes are not included. Light goods road vehicles, motor-coaches and buses and mini-buses/mini-coaches are not included. Microcars (needing no permit to be driven), taxis and passenger hire cars, provided that they have fewer than ten seats, are included.; PASSENGER CARS In 2011 there has been a shift in information about light goods road vehicles where one group was previously included in passenger cars, that creates a break in the series.

Croatia Number of Registered Vehicles: ow Passenger Cars data was reported at 1,910,131.000 Unit in 2023. This records an increase from the previous number of 1,840,767.000 Unit for 2022. Croatia Number of Registered Vehicles: ow Passenger Cars data is updated yearly, averaging 1,448,299.000 Unit from Dec 1993 (Median) to 2023, with 31 observations. The data reached an all-time high of 1,910,131.000 Unit in 2023 and a record low of 646,210.000 Unit in 1993. Croatia Number of Registered Vehicles: ow Passenger Cars data remains active status in CEIC and is reported by Croatian Bureau of Statistics. The data is categorized under Global Database’s Croatia – Table HR.TA003: Number of Vehicle Registrations.