Honda Used Car Selling Prices dataset is now fully cleaned to be used for Exploratory Data Analysis. The analysis can be performed in order to check the market trends. This dataset is small but it contains valuable insights in order to understand the car prices and models. Different Machine Learning algorithms can be applied to predict the car prices, like Linear Regression

Car Price Prediction (Linear Regression - RFE)

Problem Statement:

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts.

They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know:

• Which variables are significant in predicting the price of a car
• How well those variables describe the price of a car

Based on various market surveys, the consulting firm has gathered a large dataset of different types of cars across the Americal market.

Business Goal:

You are required to model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent variables. They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. Further, the model will be a good way for management to understand the pricing dynamics of a new market.

Programming Language: python IDE: jupyternotebook

Dataset Overview

The "Vehicle Dataset 2024" provides a comprehensive look at new vehicles available in the market, including SUVs, cars, trucks, and vans. This dataset contains detailed information on various attributes such as make, model, year, price, mileage, and more. With 1002 entries and 18 columns, this dataset is ideal for data science enthusiasts and professionals looking to practice data cleaning, exploratory data analysis (EDA), and predictive modeling.

Data Science Applications

Given the richness of the data, this dataset can be used for a variety of data science applications, including but not limited to: - Price Prediction: Build models to predict vehicle prices based on features such as make, model, year, and mileage. - Market Analysis: Perform market segmentation and identify trends in vehicle types, brands, and pricing. - Descriptive Statistics: Conduct comprehensive descriptive statistical analyses to summarize and describe the main features of the dataset. - Visualization: Create visualizations to illustrate the distribution of prices, mileage, and other features across different vehicle types. - Data Cleaning: Practice data cleaning techniques, handling missing values, and transforming data for further analysis. - Feature Engineering: Develop new features to improve model performance, such as price per year or mileage per year.

Column Descriptors

1. name: The full name of the vehicle, including make, model, and trim.
2. description: A brief description of the vehicle, often including key features and selling points.
3. make: The manufacturer of the vehicle (e.g., Ford, Toyota, BMW).
4. model: The model name of the vehicle.
5. type: The type of the vehicle, which is "New" for all entries in this dataset.
6. year: The year the vehicle was manufactured.
7. price: The price of the vehicle in USD.
8. engine: Details about the engine, including type and specifications.
9. cylinders: The number of cylinders in the vehicle's engine.
10. fuel: The type of fuel used by the vehicle (e.g., Gasoline, Diesel, Electric).
11. mileage: The mileage of the vehicle, typically in miles.
12. transmission: The type of transmission (e.g., Automatic, Manual).
13. trim: The trim level of the vehicle, indicating different feature sets or packages.
14. body: The body style of the vehicle (e.g., SUV, Sedan, Pickup Truck).
15. doors: The number of doors on the vehicle.
16. exterior_color: The exterior color of the vehicle.
17. interior_color: The interior color of the vehicle.
18. drivetrain: The drivetrain of the vehicle (e.g., All-wheel Drive, Front-wheel Drive).

Ethically Mined Data

This dataset was ethically mined from cars.com using an API provided by Apify. All data collection practices adhered to the terms of service and privacy policies of the source website, ensuring the ethical use of data.

Acknowledgements

• Apify: For providing the API used to scrape the data from cars.com.
• Cars.com: For being the source of the vehicle data.
• DALL-E 3: For generating the thumbnail image for this dataset.

Context

This dataset was created by webscraping over 200,000 car offers from one of the largest car advertisement sites in Poland. The code used to collect and clean the data is available at github: github.com/pt3k/otomoto-webscrape

Content

The dataset contains 208,304 observations of 25 variables.

Variables describtion: - ID - unique ID of offer - Price - value of the price - Currency - currency of the price (mostly polish złoty, but also some euro) - Condition - new or used - Vehicle_brand - brand of vehicle in offer - Vehicle_model - model of vehicle in offer - Vehicle_generation - generation of vehicle in offer - Vehicle_version - version of vehicle in offer - Production_year - year of car production - Mileage_km - total distance that the car has driven in kilometers - Power_HP - car engine power in horsepower - Displacement_cm3 - car engine size in cubic centimeters - Fuel_type - car fuel type - CO2_emissions - car CO2 emissions in g/km - Drive - type of car drive - Transmission - type of car transmission - Type - car body style - Doors_number - number of car doors - Colour - car body color - Origin_country - country of origin of the car - First_owner - whether the owner is the first owner - First_registration_date - date of first registration - Offer_publication_date - date of publication of the offer - Offer_location - address provided by the issuer - Features - listed car features (ABS, airbag, parking sensors e.t.c)

Inspiration

I collected this dataset for performing exploratory data analysis and data visualization for my university assignment. You can use the data to: - Perform EDA; - data visualization; - price prediction;