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!

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

jvbf2e/used-car-price-prediction dataset hosted on Hugging Face and contributed by the HF Datasets community

Gain valuable insights into the automotive market with our comprehensive Car Prices Dataset. Designed for businesses, analysts, and researchers, this dataset provides real-time and historical car pricing data to support market analysis, pricing strategies, and trend forecasting.

Dataset Features

Vehicle Listings: Access detailed car listings, including make, model, year, trim, and specifications. Ideal for tracking market trends and pricing fluctuations. Pricing Data: Get real-time and historical car prices from multiple sources, including dealerships, marketplaces, and private sellers. Market Trends & Valuations: Analyze price changes over time, compare vehicle depreciation rates, and identify emerging pricing trends. Dealer & Seller Information: Extract seller details, including dealership names, locations, and contact information for lead generation and competitive analysis.

Customizable Subsets for Specific Needs Our Car Prices Dataset is fully customizable, allowing you to filter data based on vehicle type, location, price range, and other key attributes. Whether you need a broad dataset for market research or a focused subset for competitive analysis, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Pricing Strategy: Track vehicle price trends, compare competitor pricing, and optimize pricing strategies for dealerships and resellers. Automotive Valuation & Depreciation Studies: Analyze historical pricing data to assess vehicle depreciation rates and predict future values. Competitive Intelligence: Monitor competitor pricing, dealership inventory, and promotional offers to stay ahead in the market. Lead Generation & Sales Optimization: Identify potential buyers and sellers, track demand for specific vehicle models, and enhance sales strategies. AI & Predictive Analytics: Leverage structured car pricing data for AI-driven forecasting, automated pricing models, and trend prediction.

Whether you're tracking car prices, analyzing market trends, or optimizing sales strategies, our Car Prices Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

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.

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

The Used Car Market size was valued at USD 1.76 trillion in 2023 and is projected to reach USD 2.66 trillion by 2032, exhibiting a CAGR of 6.1 % during the forecasts period. The Used Car Market entails the business of buying and selling second-hand cars which offer affordable options compared to brand-new cars. Some of the applications of the used car market include; offering cheap means of transport for individuals and firms during hard economic times or where specific models of cars are not being produced as new. Applications cover the zones of individual and business usage, adjusting to private cars, business fleets, and rental cars. Today’s trends in the market are the growing availability of online sources for purchasing used cars, the constantly growing popularity of certified pre-owned automobiles with warranties and inspection programs, and the increased demand for electric and hybrid used cars due to the advancing environmental consciousness and fairly affordable prices. However, with the change in customer preference towards value and sustainability, the upgraded category of the used car market experiences consistent growth with the factors of accessibility and availability of various models and brands across the world. Recent developments include: In June 2023, Jardine Cycle & Carriage Limited, the investment arm of Hong Kong-based Jardine Matheson collaborated with Southeast Asian car marketplace Carro. According to a joint statement by the companies, this collaboration will provide Carro with access to a broader selection of high-quality used cars while enabling Carro to improve Republic Auto's digital services and offerings. The alliance aims to strengthen both companies' positions in the market and enhance their customer experiences. , In August 2022, Lexus, a subsidiary of TOYOTA MOTOR CORPORATION, launched the Lexus Certified Programme in the Indian market. This strategic move by Lexus is geared towards providing current Lexus vehicle owners the avenue to secure an elevated resale valuation for their automobiles. Moreover, the initiative aims to augment the accessibility and affordability of Lexus models for prospective customers. By implementing this certified program, Lexus aims to amplify customer contentment while bolstering the brand's prominence within the Indian automotive sector. .

Description:

This dataset contains the latest information on car prices in Australia for the year 2023. It covers various brands, models, types, and features of cars sold in the Australian market. It provides useful insights into the trends and factors influencing the car prices in Australia. The dataset includes information such as brand, year, model, car/suv, title, used/new, transmission, engine, drive type, fuel type, fuel consumption, kilometres, colour (exterior/interior), location, cylinders in engine, body type, doors, seats, and price. The dataset has over 16,000 records of car listings from various online platforms in Australia.

Key Features:

• Brand: Name of the car manufacturer
• Year: Year of manufacture or release
• Model: Name or code of the car model
• Car/Suv: Type of the car (car or suv)
• Title: Title or description of the car
• UsedOrNew: Condition of the car (used or new)
• Transmission: Type of transmission (manual or automatic)
• Engine: Engine capacity or power (in litres or kilowatts)
• DriveType: Type of drive (front-wheel, rear-wheel, or all-wheel)
• FuelType: Type of fuel (petrol, diesel, hybrid, or electric)
• FuelConsumption: Fuel consumption rate (in litres per 100 km)
• Kilometres: Distance travelled by the car (in kilometres)
• ColourExtInt: Colour of the car (exterior and interior)
• Location: Location of the car (city and state)
• CylindersinEngine: Number of cylinders in the engine
• BodyType: Shape or style of the car body (sedan, hatchback, coupe, etc.)
• Doors: Number of doors in the car
• Seats: Number of seats in the car
• Price: Price of the car (in Australian dollars)

Potential Use Cases:

• Price prediction: Predict the price of a car based on its features and location using machine learning models.
• Market analysis: Explore the market trends and demand for different types of cars in Australia using descriptive statistics and visualization techniques.
• Feature analysis: Identify the most important features that affect the car prices and how they vary across different brands, models, and locations using correlation and regression analysis.

If you find this dataset useful, your support through an upvote would be greatly appreciated ❤️🙂
Thank you

Analysis of ‘Vehicle dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nehalbirla/vehicle-dataset-from-cardekho on 28 January 2022.

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

This dataset contains information about used cars. This data can be used for a lot of purposes such as price prediction to exemplify the use of linear regression in Machine Learning. The columns in the given dataset are as follows:

1. name
2. year
3. selling_price
4. km_driven
5. fuel
6. seller_type
7. transmission
8. Owner

For used motorcycle datasets please go to https://www.kaggle.com/nehalbirla/motorcycle-dataset

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

Dataset

This dataset was created by Tahfim Juwel

Contents

Dataset

This dataset was created by Saumya

Released under CC0: Public Domain

Contents

It contains the following files:

Snapshot img

Used Car Market Size 2025-2029

The used car market size is forecast to increase by USD 885.3 billion, at a CAGR of 7.4% between 2024 and 2029.

The market is experiencing dynamic shifts, driven by intensifying competition leading to an escalating launch of new car models and increasing consumer preferences for alternative mobility solutions. These trends are reshaping the market landscape, presenting both opportunities and challenges for stakeholders. Competition in the market is escalating, prompting automakers to introduce new models at a faster pace to maintain market share. This trend, in turn, is increasing the availability of pre-owned vehicles, providing consumers with a wider range of options. Meanwhile, consumer preferences are evolving, with a growing demand for car subscription services and car-sharing solutions.
These services cater to consumers seeking flexible, cost-effective mobility solutions, particularly in urban areas. However, this shift towards alternative mobility models poses a challenge for traditional used car dealers, requiring them to adapt and innovate to remain competitive. Digital marketing, including social media, mobile apps, and data analytics, helps sellers reach a wider audience. The market is undergoing significant transformation, fueled by increasing competition and evolving consumer preferences. Companies seeking to capitalize on opportunities and navigate challenges effectively must stay abreast of these trends and adapt their strategies accordingly. This may involve exploring new business models, such as car subscription services, or enhancing their offerings to cater to the changing needs of consumers.

What will be the Size of the Used Car Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market activities unfolding across various sectors. Internal combustion engines power the majority of the market, but the emergence of electric vehicles is reshaping the landscape. Steering systems and suspension systems ensure optimal vehicle handling, while safety features such as backup cameras, parking sensors, and blind spot monitoring are becoming increasingly essential. Title transfer and engine displacement are crucial components of the sales process, with customer service and fuel efficiency key differentiators for dealers. Inventory management and pricing strategies are critical for wholesale auctions and online auto dealers, who must navigate the complex interplay of supply and demand. Vehicle registration and title transfer processes can be streamlined through digital means, and car refurbishment and connected car technology enhance safety and convenience.

Car loans and auto auctions offer financing options for buyers, while certified pre-owned vehicles and vehicle history reports provide transparency and value assurance. Adaptive cruise control and lane departure warning systems are among the advanced technologies enhancing the driving experience. Fuel efficiency and body panels are essential considerations for buyers, with infotainment systems and navigation systems adding convenience and value. The market's continuous evolution underscores the importance of staying informed and adaptable to changing consumer preferences and industry trends.

How is this Used Car Industry segmented?

The used car industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Vehicle Type

  Compact
  SUV
  Mid size


Channel

  Organized
  Unorganized


Fuel Type

  Diesel
  Petrol


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Vehicle Type Insights

The Compact segment is estimated to witness significant growth during the forecast period. The compact car segment in the used automobile market experiences significant growth due to increasing consumer preference for personal mobility and the availability of advanced features in compact vehicles. APAC and Europe lead the market, contributing a substantial share to the compact segment. Compact cars, which sit between subcompact and mid-size vehicles, offer easier handling in traffic congestion and lower emissions. Popular pre-owned compact models include the Fiat Panda and Volkswagen Golf in Europe. Inventory management plays a crucial role in the market, ensuring a steady supply of various models. Used car dealers source vehicles from private sellers, wholesale auctions, and trade-ins.

Vehicle history reports help assess the con

Dataset

This dataset was created by Shrikanth PV

Contents

The statistic shows a prediction for the world market shares of new car types in 2020. In that year, according to this forecast, electric cars will have a world market share of 8 percent.

EUCA dataset description Associated Paper: EUCA: the End-User-Centered Explainable AI Framework

Authors: Weina Jin, Jianyu Fan, Diane Gromala, Philippe Pasquier, Ghassan Hamarneh

Introduction: EUCA dataset is for modelling personalized or interactive explainable AI. It contains 309 data points of 32 end-users' preferences on 12 forms of explanation (including feature-, example-, and rule-based explanations). The data were collected from a user study on 32 layperson participants in the Greater Vancouver city area in 2019-2020. In the user study, the participants (P01-P32) were presented with AI-assisted critical tasks on house price prediction, health status prediction, purchasing a self-driving car, and studying for a biological exam [1]. Within each task and for its given explanation goal [2], the participants selected and rank the explanatory forms [3] that they saw the most suitable.

1 EUCA_EndUserXAI_ExplanatoryFormRanking.csv

Column description:

Index - Participants' number Case - task-explanation goal combination accept to use AI? trust it? - Participants response to whether they will use AI given the task and explanation goal require explanation? - Participants response to the question whether they request an explanation for the AI 1st, 2nd, 3rd, ... - Explanatory form card selection and ranking cards fulfill requirement? - After the card selection, participants were asked whether the selected card combination fulfill their explainability requirement.

2 EUCA_EndUserXAI_demography.csv

It contains the participants demographics, including their age, gender, educational background, and their knowledge and attitudes toward AI.

EUCA dataset zip file for download

More Context for EUCA Dataset [1] Critical tasks There are four tasks. Task label and their corresponding task titles are: house - Selling your house car - Buying an autonomous driving vehicle health - Personal health decision bird - Learning bird species

Please refer to EUCA quantatative data analysis report for the storyboard of the tasks and explanation goals presented in the user study.

[2] Explanation goal End-users may have different goals/purposes to check an explanation from AI. The EUCA dataset includes the following 11 explanation goals, with its [label] in the dataset, full name and description

[trust] Calibrate trust: trust is a key to establish human-AI decision-making partnership. Since users can easily distrust or overtrust AI, it is important to calibrate the trust to reflect the capabilities of AI systems.

[safe] Ensure safety: users need to ensure safety of the decision consequences.

[bias] - Detect bias: users need to ensure the decision is impartial and unbiased.

[unexpect] Resolve disagreement with AI: the AI prediction is unexpected and there are disagreements between users and AI.

[expected] - Expected: the AI's prediction is expected and aligns with users' expectations.

[differentiate] Differentiate similar instances: due to the consequences of wrong decisions, users sometimes need to discern similar instances or outcomes. For example, a doctor differentiates whether the diagnosis is a benign or malignant tumor.

[learning] Learn: users need to gain knowledge, improve their problem-solving skills, and discover new knowledge

[control] Improve: users seek causal factors to control and improve the predicted outcome.

[communicate] Communicate with stakeholders: many critical decision-making processes involve multiple stakeholders, and users need to discuss the decision with them.

[report] Generate reports: users need to utilize the explanations to perform particular tasks such as report production. For example, a radiologist generates a medical report on a patient's X-ray image.

[multi] Trade-off multiple objectives: AI may be optimized on an incomplete objective while the users seek to fulfill multiple objectives in real-world applications. For example, a doctor needs to ensure a treatment plan is effective as well as has acceptable patient adherence. Ethical and legal requirements may also be included as objectives.

[3] Explanatory form The following 12 explanatory forms are end-user-friendly, i.e.: no technical knowledge is required for the end-user to interpret the explanation.

Feature-Based Explanation Feature Attribution - fa
Note: for tasks that has image as input data, the feature attribution is denoted by the following two cards: ir: important regions (a.k.a. heat map or saliency map) irc: important regions with their feature contribution percentage

Feature Shape - fs

Feature Interaction - fi

Example-Based Explanation

Similar Example - se Typical Example - te

Counterfactual Example - ce

Note: for contractual example, there were two visual variations used in the user study: cet: counterfactual example with transition from one example to the counterfactual one ceh: counterfactual example with the contrastive feature highlighted

Rule-Based Explanation

Rule - rt Decision Tree - dt

Decision Flow - df

Supplementary Information

Input Output Performance Dataset - prior (output prediction with prior distribution of each class in the training set)

Note: occasionally there is a wild card, which means the participant draw the card by themselves. It is indicated as 'wc'.

For visual examples of each explanatory form card, please refer to the Explanatory_form_labels.pdf document.

Link to the details on users' requirements on different explanatory forms

Code and report for EUCA data quantatitve analysis

EUCA data analysis code EUCA quantatative data analysis report

EUCA data citation @article{jin2021euca, title={EUCA: the End-User-Centered Explainable AI Framework}, author={Weina Jin and Jianyu Fan and Diane Gromala and Philippe Pasquier and Ghassan Hamarneh}, year={2021}, eprint={2102.02437}, archivePrefix={arXiv}, primaryClass={cs.HC} }

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Nvidia: The Future of Gaming, AI, and Self-Driving Cars

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The used car market is projected to reach over *** trillion U.S. dollars in 2027, up from *** trillion in 2020. This represents a compound annual growth rate of around *** percent across seven years. This growth is in part attributed to a shift in car ownership patterns across the globe, as well as the rise of online sales channels, which make used cars more accessible to customers.

The German used car market is predicted to grow at a compound annual growth rate of 2.05 percent between 2021 and 2027. By 2027, the market is expected to have grown to 117.24 billion US dollars, an increase of 13.63 billion US dollars from its size in 2021.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Carvana: A Strong Buy for Investors Who Want to Invest in the Future of Used Car Buying

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

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