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"Knowledge is power," they said. If you know Python (or any other programming language), are familiar with basic statistics, probability theory, and machine learning – then hurry up and apply. We cannot wait to teach you how to turn plain old data into invaluable knowledge.
Here is your test task:
For this test task you need to build a model for predicting real estate prices. You may use any libraries and models (except auto ml and deep learning approaches). Description of fields are in data_description.txt file.
Your aim is to demonstrate skills in data processing and writing of a correct, understandable code. You don’t have to be as precise as possible in your predictions.
Mandatory steps:
- Create a Jupyter notebook file to carry out all the further actions.
- Open train.csv file as pandas Dataframe.
- Choose a metric to evaluate the model and justify your choice
- Transform data to the input format of the model
- Train the model, evaluate its work
- Using the model, make predictions from data with test.csv. Save the result to a prediction.csv file with two columns: Id and SalePrice (check out sample_prediction.csv as an example)
Optional steps:
- Conduct basic EDA data. Describe the main features that you should pay attention to
- Conduct feature engineering, describe the characteristics obtained and the arguments for their use
- Conduct feature selection
- Carry out hyperparameter tuning
The result of the test task is a jupyter notebook file.
Attach your completed test task as a file or provide a link to it via the application form below. Look sharp, we won’t process your application if the completed test task is not attached.
Application deadline: May 10
