hardiksraja / Predicting-the-costs-of-used-cars

Python Self Learning Project : Predicting the costs of used cars

Python Self Learning Project : Predicting the costs of used cars

Done By: Hardik Raja (https://github.com/hardiksraja)

Task:

The data (and its description) can also be downloaded from here: https://www.machinehack.com/course/predicting-the-costs-of-used-cars-hackathon-by-imarticus/
Develop a machine learning model using only the training set. This is an end to end activity. The entire ML pipeline must be shown with an explanation.

Dataset Description :

Size of training set: 6,019 records

Size of test set: 1,234 records

FEATURES:

Name: The brand and model of the car.

Location: The location in which the car is being sold or is available for purchase.

Year: The year or edition of the model.

Kilometers_Driven: The total kilometres driven in the car by the previous owner(s) in KM.

Fuel_Type: The type of fuel used by the car.

Transmission: The type of transmission used by the car.

Owner_Type: Whether the ownership is Firsthand, Second hand or other.

Mileage: The standard mileage offered by the car company in kmpl or km/kg

Engine: The displacement volume of the engine in cc.

Power: The maximum power of the engine in bhp.

Seats: The number of seats in the car.

New_Price: The price of a new car of the same model.

Price: The price of the used car in INR Lakhs.

Notes:

1. There are two datasets provided Train and Test; I will be using Train dataset for training and validating model using train-test split as well as cross-validation. The test dataset will be used as an input (set of independent variables) to predict the output (dependent variable - Price of used car)