Hello everyone, 
Thanks for participating in our LightAutoML course!

In this competition the task is to predict the cars prices based on information about them.

There are several baselines for this competition:

<ul>
	<li>All zeros baseline</li>
	<li>Mean train price baseline</li>
	<li>Median train price baseline</li>
	<li><a href="https://www.kaggle.com/alexryzhkov/lightautoml-starter-for-hw1">LightAutoML simple baseline</a></li>
	<li><a href="https://www.kaggle.com/alexryzhkov/lightautoml-hw1-better-baseline">LightAutoML real baseline</a></li>
	<li><a href="https://www.kaggle.com/alexryzhkov/lightautoml-hw-1-with-custom-fe">LightAutoML stronger baseline</a></li>
</ul>

Let&#39;s beat all of them!

P.S. If you like LightAutoML, do not forget to support us with the star on <a href="https://github.com/sberbank-ai-lab/LightAutoML">Github</a>.

Predict car prices using LightAutoML framework

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Registration application for Course Season 2021

LightAutoML course homework 1

Predictions

Competition data.

Variable description:

<ul>
	<li><code>row_id</code> - ID for object</li>
	<li><code>vehicle_manufacturer</code> - the car producer company</li>
	<li><code>vehicle_model</code> - specific car model</li>
	<li><code>vehicle_category</code> - the type of vehicle</li>
	<li><code>current_mileage</code> - how much kilometers the car has already driven</li>
	<li><code>vehicle_year</code> - car production year</li>
	<li><code>vehicle_gearbox_type</code> - gear box type</li>
	<li><code>doors_cnt</code> - how many doors the car has</li>
	<li><code>wheels</code> - wheel type (left or right)</li>
	<li><code>vehicle_color</code> - car body color</li>
	<li><code>vehicle_interior_color</code> - car interior color</li>
	<li><code>car_vin</code> - VIN number</li>
	<li><code>car_leather_interior</code> - is the car interior made from leather</li>
	<li><code>deal_type</code> - is the car available for sale or for rent</li>
	<li><code>final_price</code> - car price (Target variable)</li>
</ul>

train_data.csv

test_data.csv

sample_submission.csv

train_data.csv	4 MB
test_data.csv	1 MB
sample_submission.csv	1 MB

Materials (6 MB)