* Gaikwad, Aniket Sambhaji (anikgaik@umail.iu.edu)
* Dileep Vishwanathan (Diviswan@umail.iu.edu)
Problem Statement : dataset contains information about used vehicles sold at auctions. The task is to predict if the vehicle to be sold at an auction will be a good buy or not. A vehicle that is not in the best of conditions is considered a kick.
This is a Kaggle competition. So like any of the kaggle competition, this competition has its own train dataset and sample test dataset.
>Dataset Description:
The dataset has 34 features with just over 72,900 data points.
The binary target label IsBadBuy can take values 1 and 0 with 1 indicating the vehicle to be a kick.
Please find more details in Project report.
This project was build using Indiana university's enterprise account github.iu.edu.
Please find screenshot.
