Name: Car Price Prediction Challenge
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Car Price Prediction Challenge

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!

Tables

Car Price Prediction

@kaggle.deepcontractor_car_price_prediction_challenge.car_price_prediction

435.71 kB
19,237 rows
18 columns

CREATE TABLE car_price_prediction (
  "id" BIGINT,
  "price" BIGINT,
  "levy" VARCHAR,
  "manufacturer" VARCHAR,
  "model" VARCHAR,
  "prod_year" BIGINT  -- Prod. Year,
  "category" VARCHAR,
  "leather_interior" VARCHAR,
  "fuel_type" VARCHAR,
  "engine_volume" VARCHAR,
  "mileage" VARCHAR,
  "cylinders" DOUBLE,
  "gear_box_type" VARCHAR,
  "drive_wheels" VARCHAR,
  "doors" VARCHAR,
  "wheel" VARCHAR,
  "color" VARCHAR,
  "airbags" BIGINT
);