GetAround Project on Exploratory Data Analysis, Supervised Machine Learning, Dashboard and API Build

Link to dashboard

Links to API/docs and API/predict

Table of contents

• Project
• Pipeline
• Deliverables
• Model performances
• Technologies
• Getting Started
• License
• Author

Project

Getaround is an online rental car rental service in an Airbnb way. When renting a car, users have to complete a checkin flow at the beginning of the rental and a checkout flow at the end of the rental in order to:

• Assess the state of the car and notify other parties of pre-existing damages or damages that occurred during the rental.
• Compare fuel levels.
• Measure how many kilometers were driven.
• Ensure checkin and checkout policies

First goal is to resolve those issues and give insights on implementing a minimum delay between two rentals. A car won’t be displayed in the search results if the requested checkin or checkout times are too close from an already booked rental. It can solve the late checkout issue but may hurt Getaround/owners' revenues.

Second goal is to provide an online API on Heroku server containing /predict endpoint that estimates the rental price per day.

Pipeline

Deliverables 📬

1- Exploratory data analysis on Getaround data and test of various machine learning models
2- A dashboard in production. Dashboard
3- A documented online API/docs containing /predict endpoint that respect the technical descriptions.

Model performances

Machine learning model performances are summarized below:

Please note that the results show the box plots of metrics obtained with cross-validation and K=4-fold attributions.
In this project, the best performances were obtained by XGboost regressor model (experiment number 13). Therefore, it was taken into consideration to deploy together with the web API. The error metric is listed below:

• RMSE = 12.8 dollars on predicting car rental prices per day (mean of 4-fold scores)

More details can be found under 'tracking' folder.

Technologies

Project is created with:

• Python 3.8
• Jupyter Notebook 6.4.12
• Python libraries (see /requirements.txt)
• VSCode 1.71.2
• Docker for containers

or this github project can be launched on colab-google without any local installations. It is free and requires Google account sign-in.

Getting Started

To run this project,

1. Clone the repo:

   git clone https://github.com/levist7/GetAround_Project.git

2. Install packages

3. Install python libraries

   pip3 install -r requirements.txt

License

Distributed under the MIT License. See LICENSE.txt for more information.

Author

• levist7

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Made with ❤️ in Paris