Automotive Resale Value Predictor Data

Columns

• car id (Integer): A unique identifier for each car listing.
• year (Integer): The manufacturing year of the car, ranging from 1990 to 2021.
• brand (String): The make of the car, including popular brands like Hyundai and Maruti Suzuki, with 31 unique brands recorded.
• full_model_name (String): The complete model name, including specific specifications, with 750 unique entries.
• model_name (String): The general model name of the car, such as Creta or Innova, with 169 unique models.
• price (Float): The resale value of the car, with values ranging from 62,500 to 14,700,000. The mean price is approximately 1,490,000.
• distance_travelled(kms) (Float): The mileage of the car in kilometres, prior to being listed for sale. Values range from 350 to 790,000 kms, with a mean of 53,800 kms.
• fuel_type (String): The type of fuel the car uses, such as Diesel or Petrol.
• city (String): The urban location where the car is currently available, including cities like Chennai and Bangalore.
• brand_rank (Integer): A numerical ranking associated with the car's brand, from 1 to 81.
• car_age (Integer): The age of the car in years, calculated from its manufacturing year, ranging from 0 to 31 years.
• distance below 30k km (Binary): An indicator (0 or 1) signifying if the car's distance travelled is below 30,000 kilometres.
• new and less used (Binary): An indicator (0 or 1) suggesting if the car is relatively new and has seen minimal use.
• inv_car_price (Float): The inverse of the car's price.
• inv_car_dist (Float): The inverse of the car's distance travelled.
• inv_car_age (Float): The inverse of the car's age.
• inv_brand (Float): An inverse value related to the car's brand, ranging from 0.01 to 1.
• std_invprice (Float): A standardised inverse car price, ranging from 0 to 1.
• std_invdistance_travelled (Float): A standardised inverse distance travelled, ranging from 0 to 1.
• std_invrank (Float): A standardised inverse brand rank, ranging from 0 to 1.
• best_buy1 (Float): A calculated metric, potentially indicating a "best buy" opportunity.
• best_buy2 (Float): Another calculated metric for "best buy" opportunities.

Distribution

Usage

• Evaluating second-hand car purchases: Helping potential owners determine the value and suitability of a used car.
• Data Analytics: Performing detailed analysis on used car market trends.
• Data Visualisation: Creating visual representations of market data, such as price distributions or brand popularity.
• Exploratory Data Analysis: Investigating relationships between car attributes like age, mileage, price, and brand.

Coverage

License

Who Can Use It

• Second-hand car buyers: To make informed decisions on vehicle purchases.
• Data analysts and scientists: For market research, predictive modelling, and trend analysis within the automotive sector.
• Beginner and intermediate data practitioners: As a practical dataset for learning data analytics, visualisation, and exploratory data analysis.
• Automotive industry professionals: To gain insights into pricing strategies and market demand for used vehicles.

Dataset Name Suggestions

• Used Car Market Dynamics (Across Cities)
• Second-Hand Vehicle Price and Mileage Data
• Urban Used Car Assessment Dataset
• Automotive Resale Value Predictor Data
• City-Specific Used Car Sales Insights

Attributes