Don't Get Kicked!
Predict if a car purchased at auction is a lemon
One of the biggest challenges of an auto dealership purchasing a used car at an auto auction is the risk of that the vehicle might have serious issues that prevent it from being sold to customers. The auto community calls these unfortunate purchases "kicks".
Kicked cars often result when there are tampered odometers, mechanical issues the dealer is not able to address, issues with getting the vehicle title from the seller, or some other unforeseen problem. Kick cars can be very costly to dealers after transportation cost, throw-away repair work, and market losses in reselling the vehicle.
Modelers who can figure out which cars have a higher risk of being kick can provide real value to dealerships trying to provide the best inventory selection possible to their customers.
The challenge of this competition is to predict if the car purchased at the Auction is a Kick (bad buy).
The following topics are covered in this tutorial:
- A typical problem statement for machine learning
- Downloading and exploring a dataset for machine learning
- Implementing Logistic Regression
- Implementing DecisionTreeClassifier
- Implementing RandomForestClassifier
- Implementing GradientBoostingClassifier
In this work our focus is to predict if the car puchased at the Auction is a bud buy or not.
#Install neccessary packages
!pip install pandas numpy matplotlib seaborn --quiet
!pip install opendatasets scikit-learn jovian plotly --quiet --upgrade