A hackathon challenge focussing on predicting the price of the used cars up for sale.

A recent challenge by Imarticus Learning had participant predicting the price of used cars based on certain select features provided.

Description of the data:

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.

Size of training set: 6,019 records Size of test set: 1,234 records

We approached the problem addressing issues of inconsistency in units, missing values, feature importance, high correlation. Each of them are addressed in detail in the jupyter notebook listed in the repository.