Baselight

Cornell Car Rental Dataset

Dataset of 5,851 Rental Cars: Make, Model, Year, Fare, Location and Fuel Type

@kaggle.kushleshkumar_cornell_car_rental_dataset

About this Dataset

Cornell Car Rental Dataset

Context

As a Product Manager for a startup I needed to make certain data driven decisions which involved the car rental industry. So I scraped the web and built this dataset myself. I thought to make is public as a way to give back to the community.

Here is a Medium Post by me describing some insights from this dataset and releasing the dataset to public:
https://link.medium.com/Yduf7ceYC8

Content

The content is acquired during the time of July 2020 for major US cities.

Acknowledgements

The scraping scripts I used were built off of certain StackOverflow responses.

Inspiration

Some of the answers this dataset can help unwind is:

  • Which car makes and models are popular and in which cities
  • What is the typical fare of car rental in various major cities
  • Is there a Market gap or are some markets oversaturated
  • Users can also explore if the ratings on the sites have any co-relation or do they appear suspicious as most are close to 5 ratings.

Tables

Carrentaldata

@kaggle.kushleshkumar_cornell_car_rental_dataset.carrentaldata
  • 211.86 KB
  • 5851 rows
  • 15 columns
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CREATE TABLE carrentaldata (
  "fueltype" VARCHAR,
  "rating" DOUBLE,
  "rentertripstaken" BIGINT,
  "reviewcount" BIGINT,
  "location_city" VARCHAR,
  "location_country" VARCHAR,
  "location_latitude" DOUBLE,
  "location_longitude" DOUBLE,
  "location_state" VARCHAR,
  "owner_id" BIGINT,
  "rate_daily" BIGINT,
  "vehicle_make" VARCHAR,
  "vehicle_model" VARCHAR,
  "vehicle_type" VARCHAR,
  "vehicle_year" BIGINT
);

Carrentaldatav1

@kaggle.kushleshkumar_cornell_car_rental_dataset.carrentaldatav1
  • 215.7 KB
  • 5851 rows
  • 16 columns
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CREATE TABLE carrentaldatav1 (
  "fueltype" VARCHAR,
  "rating" DOUBLE,
  "rentertripstaken" DOUBLE,
  "reviewcount" DOUBLE,
  "location_city" VARCHAR,
  "location_country" VARCHAR,
  "location_latitude" DOUBLE,
  "location_longitude" DOUBLE,
  "location_state" VARCHAR,
  "owner_id" DOUBLE,
  "rate_daily" DOUBLE,
  "vehicle_make" VARCHAR,
  "vehicle_model" VARCHAR,
  "vehicle_type" VARCHAR,
  "vehicle_year" DOUBLE,
  "airportcity" VARCHAR
);