The Car Allowance Rebate System (CARS), otherwise known as Cash for Clunkers, was a program intended to provide economic incentives to United States residents to purchase a new and more fuel efficient vehicle when trading in a less full efficient vehicle. The program was promoted as providing stimulus to the economy by boosting auto sales, while putting safer, cleaner and more fuel efficient vehicles on the road.

This dataset contains features about the existing vehicle models. Before predict the new kind of car model, we want to determine which existing vehicles on the market are most like the new models, how vehicles can be grouped, which group is the most similar with the new models.

Assignment

Your notebooks must contain the following steps:

• Perform data cleaning and pre-processing.
  • What steps did you use in this process and how did you clean your data.
• Perform exploratory data analysis on the given dataset.
  • Explain each and every graphs that you make.
• Train a ml-model and evaluate it using different metrics.
  • Why did you choose that particular model? What was the accuracy?
• Hyperparameter optimization and feature selection is a plus.
• Model deployment and use of ml-flow is a plus.
• Perform model interpretation and show feature importance for your model.
  • Provide some explanation for the above point.
• Future steps. Note: try to have your notebooks as presentable as possible.

Dataset Description

CSV file - 19237 rows x 18 columns (Includes Price Columns as Target)

Attributes

ID Price: price of the care(Target Column) Levy Manufacturer Model Prod. year Category Leather interior Fuel type Engine volume Mileage Cylinders Gear box type Drive wheels Doors Wheel Color Airbags

Confused or have any doubts in the data column values? Check the dataset discussion tab!

Analysis of ‘Car Prices Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sidharth178/car-prices-dataset on 29 August 2021.

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

Context

With the rise in the variety of cars with differentiated capabilities and features such as model, production year, category, brand, fuel type, engine volume, mileage, cylinders, colour, airbags and many more, we are bringing a car price prediction challenge for all. We all aspire to own a car within budget with the best features available. To solve the price problem we have created a dataset of 19237 for the training dataset and 8245 for the test dataset.

Data Description

Train.csv - 19237 rows x 18 columns (Includes Price Columns as Target) - Attributes - ID - Price: price of the care(Target Column) - Levy - Manufacturer - Model - Prod. year - Category - Leather interior - Fuel type - Engine volume - Mileage - Cylinders - Gear box type - Drive wheels - Doors - Wheel - Color - Airbags Test.csv - 8245 rows x 17 columns

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

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.

Analysis of ‘cars.csv’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/huseyinrakun/carscsv on 28 January 2022.

--- No further description of dataset provided by original source ---

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

The steps listed below must be included in your notebooks:

1. Understand the problem statement.
2. Import required libraries and Data.
3. Check the Data
4. Pre-processing and data cleansing should be done.
5. Utilize the provided dataset to conduct exploratory data analysis. Each and every graph you create should be explained.
6. Feature Selection
7. Data splitting
8. Create an ML model, then test it using various metrics.

Data source - https://www.cardekho.com/used-car-details Cover image source - https://cdni.autocarindia.com/Utils/ImageResizer.ashx?n=https://cdni.autocarindia.com/Galleries/20200206032922_Tata-Harrier-BS6-5.jpg&w=872&h=578&q=75&c=1

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 ---

This dataset contains a set of damaged car images, each labeled with information about being fraudulent or non fraudulent with respect to damage claims in the csv file.

Analysis of ‘cars.csv’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/karimkeraani/carscsv on 28 January 2022.

--- No further description of dataset provided by original source ---

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

The dataset consists of information about 50,815 policyholders who are car owners.

Background

Battery electric vehicles (BEVs) are crucial for a sustainable transportation system. As more people adopt BEVs, it becomes increasingly important to accurately assess the demand for charging infrastructure. However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model. To address this issue, we simulate the travel and charging demand on three charging behavior archetypes. We use a large synthetic population of Sweden, including detailed individual characteristics, such as dwelling types (detached house vs. apartment) and activity plans (for an average weekday). This data repository aims to provide the BEV simulation's input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc.

A journal paper published in Transportation Research Part D: Transport and Environment details the method to create the data (particularly Section 2.2 BEV simulation).

https://doi.org/10.1016/j.trd.2023.103645

Methodology

This data product is centered on the 1.7 million inhabitants of the Västra Götaland (VG) region, which includes the second largest city in Sweden, Gothenburg. We specifically simulated 284,000 car agents who live in VG, representing 35% of all car users and 18% of the total population in the region. They spend their simulation day (representing an average weekday) in a variety of locations throughout Sweden.

This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents' activity plans (1) and travel trajectories from MATSim simulation for the BEV simulation (2), in which we consider overnight charger access (3), car fleet composition referencing the current private car fleet in Sweden (4), and Swedish road network with slope information (5) with realistic BEV charging & discharging dynamics. For the BEV simulation, we tested ten scenarios of charging behavior archetypes and fast charging powers (6). The output includes the time history of travel trajectories and charging of the simulated BEVs across the different scenarios (7).

Data description

The current data product covers seven data files.

(1) Agents' experienced activity plans

File name: 1_activity_plans.csv

Column

Description

Data type

Unit

person

Agent ID

Integer

-

act_id

Activity index of each agent

Integer

-

deso

Zone code of Demographic statistical areas (DeSO)1

String

-

POINT_X

Coordinate X of activity location (SWEREF99TM)

Float

meter

POINT_Y

Coordinate Y of activity location (SWEREF99TM)

Float

meter

act_purpose

Activity purpose (work, home, other)

String

-

mode

Transport mode to reach the activity location (car)

String

-

dep_time

Departure time in decimal hour (0-23.99)

Float

hour

trav_time

Travel time to reach the activity location

String

hour:minute:second

trav_time_min

Travel time in decimal minute

Float

minute

speed

Travel speed to reach the activity location

Float

km/h

distance

Travel distance between the origin and the destination

Float

km

act_start

Start time of activity in minute (0-1439)

Integer

minute

act_time

Activity duration in decimal minute

Float

minute

act_end

End time of activity in decimal hour (0-23.99)

Float

hour

score

Utility score of the simulation day given by MATSim

Float

-

1 https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/

(2) Travel trajectories

File name: 2_input_zip

Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, …, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days.

Column

Description

Data type

Unit

time

Time in second in a simulation day (0-86399)

Integer

Second

type

Event type defined by MATSim simulation2

String

-

person

Agent ID

Integer

-

link

Nearest road link consistent with (5)

String

-

vehicle

Vehicle ID identical to person

Integer

-

2 One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)

(3) Overnight charger access

File name: 3_home_charger_access.csv

Column

Description

Data type

Unit

person

Agent ID

Integer

-

home_charger

Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)

Integer

-

(4) Car fleet composition

File name: 4_car_fleet.csv

Column

Description

Data type

Unit

person

Agent ID

Integer

-

income_class

Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K)

Integer

-

car

Car model class (B=40 kWh, C=60 kWh, D=100 kWh)

String

-

(5) Road network with slope information

File name: 5_road_network_with_slope.shp (5 files in total)

Column

Description

Data type

Unit

length

The length of road link

Float

meter

freespeed

Free speed

Float

km/h

capacity

Number of vehicles

Integer

-

permlanes

Number of lanes

Integer

-

oneway

Whether the segment is one-way (0=no, 1=yes)

Integer

-

modes

Transport mode (car)

String

-

link_id

Link ID

String

-

from_node

Start node of the link

String

-

to_node

End node of the link

String

-

count

Aggregated traffic (number of cars travelled per day)

Integer

-

slope

Slope in percent from -6% to 6%

Float

-

geometry

LINESTRING (SWEREF99TM)

geometry

meter

(6) Simulation scenarios specifying the parameter sets

File name: 6_scenarios.txt

Parameter set

(paraset)

Strategy 1

Strategy 2

Strategy 3

Fast charging power (kW)

Minimum parking time for charging (min)

Intermediate charging power (kW)

0

0.2

0.2

0.9

150

5

22

1

0.2

0.2

0.9

50

5

22

2

0.3

0.3

0.9

150

5

22

3

0.3

0.3

0.9

50

5

22

(7) Time history of travel trajectories and charging of the simulated BEVs

File name: 7_output.zip

Produced by the BEV simulation, the zip folder contains four files (parasetX.csv.gz, X=1, 2, 3, 4) corresponding to the four parameter sets specified in (6). They are the moving trajectories of the car agents with simulated energy and charging time history in their simulation days.

Column

Description

Data type

Unit

person

Agent ID

Integer

-

home_charger

Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)

Integer

-

car

Car model class (B=40 kWh, C=60 kWh, D=100 kWh)

String

-

seq

Sequence ID of time history by agent

Integer

-

time

Time (0-86399)

Integer

Second

purpose

Valid for activities (home, work, school,

Our automotive datasets provide comprehensive insights into the global vehicle market, covering a wide range of data points related to car listings, pricing trends, vehicle specifications, and market demand. These datasets are ideal for businesses, analysts, and developers looking to enhance automotive research, optimize pricing strategies, or improve vehicle inventory management.

Key Features:

  Vehicle Listings & Specifications: Access detailed information on cars, trucks, SUVs, motorcycles, and electric vehicles, 
    including make, model, year, trim, mileage, fuel type, and transmission.
  Pricing & Market Trends: Analyze historical and real-time pricing data to track market fluctuations, assess vehicle depreciation, 
    and optimize pricing strategies.
  Dealer & Private Seller Insights: Gain visibility into vehicle listings from dealerships and private sellers, including contact details, 
    location, and availability.
  Vehicle Condition & Features: Identify key attributes such as accident history, service records, safety features, and additional specifications.
  Regional & Global Coverage: Access datasets segmented by country, state, or city to analyze local and international automotive markets.


Use Cases:

  Market Research & Competitive Analysis: Monitor automotive trends, track competitor pricing, and assess consumer demand.
  Pricing Optimization: Adjust vehicle pricing based on real-time market data to maximize profitability and sales efficiency.
  Inventory & Fleet Management: Improve vehicle sourcing, inventory tracking, and fleet management for dealerships and rental companies.
  Automotive AI & Machine Learning: Train predictive models for vehicle valuation, demand forecasting, and fraud detection.
  Consumer Insights & Lead Generation: Identify potential buyers, analyze purchasing behavior, and enhance targeted marketing efforts.



  Our automotive datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
  API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Gain valuable insights into the automotive industry with high-quality, structured data tailored to your needs.

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).

This dataset was hosted on IBM Cloud object

You can find the "Automobile Dataset" from the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data.

I cleaned the data myself, you can check notebook "Used Car Pricing Data Wrangling" for details.

Source/Credit: Michael Grogan https://github.com/MGCodesandStats https://github.com/MGCodesandStats/datasets/blob/master/cars.csv

Sample dataset for regression analysis. Given 5 attributes (age, gender, miles driven per day, debt, and income) predict how much someone will spend on purchasing a car. All 5 of the input attributes have been scaled to be in 0 to 1 range. Training set has 723 training examples. Test set has 242 test examples.

This dataset will be used in an upcoming Galaxy Training Network tutorial (https://training.galaxyproject.org/training-material/topics/statistics/) on use of feedforward neural networks for regression analysis.

This is a curated collection of 3D car models derived from Objaverse-XL described in MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific Generative Modeling. The MeshFleet dataset provides metadata for 3D car models, including their SHA256 from Objaverse-XL, vehicle category, and size. The core dataset is available as a CSV file: meshfleet_with_vehicle_categories_df.csv. You can easily load it using pandas: import pandas as pd

meshfleet_df =… See the full description on the dataset page: https://huggingface.co/datasets/DamianBoborzi/MeshFleet.

Table 4.1 (f) - First Registration of Light Goods Vehicles by Make, First Registration Vehicle Status, Fuel Type and Body Type(Simplified Chinese)