Application and use cases

1 )Market Analysis: Evaluate overall trends and regional variations in car sales to assess manufacturer performance, model preferences, and demographic insights. 2) Seasonal Patterns and Competitor Analysis: Investigate seasonal and cyclical patterns in sales. 3) Forecasting and Predictive Analysis Use historical data for forecasting and predict future market trends. Support marketing, advertising, and investment decisions based on insights. 4) Supply Chain and Inventory Optimization: Provide valuable data for stakeholders in the automotive industry.

Dataset

This dataset was created by Amin.Bl

Contents

Dataset

This dataset was created by TejalKomb

Contents

Analysis of ‘CANADA CARS SALES FIGURES (2019-2021)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mohamedhanyyy/canada-cars-sales-figures-20192021 on 28 January 2022.

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

Intro:

I love cars so i was curious about knowing how many cars of different models is getting sold in some country for no reason i chose Canada

My resource is an online resource which is goodcarbadcar website

Data Content

We have here only one spreadsheet contain 237 Rows and 16 Columns

Columns:

1. model: which is the car model name
2. Jan :January
3. Feb:February
4. Mar:March
5. Apr:April
6. May:May
7. Jun:June
8. Jul:July
9. Aug:August
10. Sep:September
11. Oct:October
12. Nov:November
13. Dec:December
14. Sumofsales : is the sum of sale of each car model
15. Category : Contain 5 categories of cars MLS :MIDSIZE LUXURY SUV SALES FIGURES SLS:SMALL LUXURY SUV SALES FIGURES LLS: LARGE LUXURY SUV SALES FIGURES LS:LARGE SUV SALES FIGURES MS:MIDSIZE SUV SALES FIGURES
16. Year : the year of sales

Thanks for all people who support me !

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

Analysis of ‘car_sales.csv’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/smritisingh1997/car-salescsv on 14 February 2022.

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

Context

This data contains data related to Car Sales

Content

The data is required for the basic Linear Regression model. It can be used to explore all the basic Linear Regression assumptions, which are required if one wants to apply Linear Regression on the given data

Acknowledgements

We wouldn't be here without the help of others. I would especially like to thanks @Udemy, @Coursera, and @KhanAcademy

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

Dataset

This dataset was created by Othniel Che

Contents

Dataset

This dataset was created by Shahriar Hossain

Contents

It contains the following files:

Dataset

This dataset was created by Shivangi_Garg

Contents

Dataset

This dataset was created by Hugo Herrera

Contents

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).

Dataset

This dataset was created by V.Radhina

Released under CC0: Public Domain

Contents

Analysis of ‘BMW Pricing Challenge’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/danielkyrka/bmw-pricing-challenge on 14 February 2022.

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

Context

Estimating the value of a used car is one of the main everyday challenges in automotive business. We believe that the sales price of a car is not only based on the value of the product itself, but is also heavily influenced by things like market trends, current availability and politics. With this challenge we hope to raise some interest in this exciting topic and also gain some insight in what the main factors are that drive the value of a used car.

Content

The data provided consists of almost 5000 real BMW cars that were sold via a b2b auction in 2018. The price shown in the table is the highest bid that was reached during the auction.

We have already done some data cleanup and filtered out cars with engine damage etc. However there may still be minor damages like scratches, but we do not have more information about that.

We have also extracted 8 criteria based on the equipment of car that we think might have a good impact on the value of a used car. These criteria have been labeled feature_1 to feature_8 and are shown in the data below.

Inspiration

We would like to find a good statistical model to describe the value of a used car depending on the basic description and the 8 provided features. The following questions are of special interest to us:

1. How much impact does each of features have on the estimate value of the car?

2. How does the estimated value of a car change over time? Can you detect any patterns? (e.g. the price of a convertible should be higher in summer than in winter)

3. How big is the influence of the factors not represented in the data on the price? Or, in other words, what is the estimated variance included in your statistical model?

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

Dataset

This dataset was created by Adarsh Nayak

Contents

Dataset

This dataset was created by 3dward

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by somaktukai

Contents

Everyone wants to know the price of his car. This file was created for everyone who want to know the price of his car (in Ukraine). Data in the dataset are not "clean". I didn't anything do with data because everyone has his own think about feature engineering.

Have a great time!

Dataset

This dataset was created by nile98

Contents

Electric vehicles (EVs) have undergone significant transformations from early innovations in the 19th century to remarkable advancements in the 21st century. Initially popular in the early 1900s, EVs lost market share to gasoline-powered cars but saw renewed interest during the 1970s oil crisis. Today, they represent a growing segment of the automotive industry, driven by technological advancements, environmental concerns, and market dynamics.

Early electric vehicles, such as those built by Robert Anderson in the 1830s, were powered by non-rechargeable batteries. By the early 1900s, electric vehicles like those from the Detroit Electric Car Company held a significant market share, favored for their quiet operation and ease of use. However, the rise of affordable gasoline cars, facilitated by innovations like the electric starter and Henry Ford's mass production techniques, led to a decline in EV popularity.

The 1970s oil crisis renewed interest in alternative energy vehicles, leading to experimental models like the Sinclair C5 and General Motors EV1. The Sinclair C5, launched in 1985, was a small electric vehicle intended for urban commuting but failed commercially due to its limited speed and range. The GM EV1, produced in the late 1990s, was praised for its technology but discontinued due to high costs and limited infrastructure.

The 21st century marked a resurgence for EVs, with advancements in battery technology and increasing environmental awareness. Companies like Tesla played a pivotal role in popularizing EVs, and major automotive manufacturers followed suit, leading to a diverse range of electric vehicles on the market today.

There are three main types of electric vehicle powertrains:

Battery Electric Vehicles (BEVs): Fully electric vehicles powered by batteries, offering a range of 100 to 400+ miles on a single charge. BEVs are environmentally friendly with zero tailpipe emissions and require less maintenance due to fewer moving parts. Examples include the Tesla Model 3 and Nissan Leaf.

Fuel Cell Electric Vehicles (FCEVs): Vehicles that use hydrogen fuel cells to generate electricity, emitting only water vapor. FCEVs have fast refueling times and long ranges (300-400+ miles), but they face challenges with hydrogen infrastructure and production costs. Examples include the Toyota Mirai and Hyundai Nexo.

Plug-in Hybrid Electric Vehicles (PHEVs): Vehicles that combine an electric motor with a traditional internal combustion engine. PHEVs can operate in electric-only mode for short trips and switch to hybrid mode for longer distances. They offer flexibility and reduced emissions but still rely on fossil fuels. Examples include the Toyota Prius Prime and Mitsubishi Outlander PHEV.

The future of electric mobility is promising, with continued advancements expected to drive sustainability and innovation in the transportation sector. The growing adoption of EVs, supported by improvements in battery technology, charging infrastructure, and governmental incentives, highlights their potential to reshape the automotive landscape.

Dataset

This dataset was created by Gospel Teddy

Released under Apache 2.0

Contents

Context

Craigslist is the world's largest collection of used vehicles for sale, yet it's very difficult to collect all of them in the same place. I built a scraper for a school project and expanded upon it later to create this dataset which includes every used vehicle entry within the United States on Craigslist.

Content

This data is scraped every few months, it contains most all relevant information that Craigslist provides on car sales including columns like price, condition, manufacturer, latitude/longitude, and 18 other categories. For ML projects, consider feature engineering on location columns such as long/lat. For previous listings, check older versions of the dataset.

See https://github.com/AustinReese/UsedVehicleSearch