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.idea
data
features
modelling
preprocessing
submission
utility
validation
GradienBoosting.py
PREREQUISITES
README
TODO
averaging.py
catboost_regression.py
catboost_regression_gridsearch.py
keras_autoencoder.py
keras_regression.py
linear_regression.py
read_data.py
stacked_estimator_kernel.py
xgboost_regression.py
xgboost_regression_gridsearch.py

README

1) About

This project is a solution to the Kaggle Mercedes contest (https://www.kaggle.com/c/mercedes-benz-greener-manufacturing).

Its task is to reduce the time it takes to test a new car. The data is a .csv file containing, on each line,
the test results for that given car. It is a multi dimensional data. The y column is the most important one,
it contains the total time to run all tests for that given car.

Our task: build (from the training data) a classifier, that for a new entry will be able to predict the value of y column.

Our solution: we have writter several classifiers to do this task, so far with average results.


2) How to run the program

- first you must install all packages in the PREREQUISITES files
- data is already read, simply run "python catboost_classification.py"