🚀Rocketloop - Machine Learning Classification Demos

The dataset in data_files is from here: https://archive.ics.uci.edu/ml/datasets/Adult.

Running the script on your machine

To run the script we recommend creating a virtual environment to install the required packages. Do so by typing virtualenv venv in your terminal in the directory where you want to install the environment. The next step will be to start the virtual environment with source venv/bin/activate. Once you are in the virtual environment enter pip install -r requirements.txt to install the required packages. After the install you are good to run python3 classifiers.py. For any changes of the data set used for training or testing of the models you have to manually change the code. To do so use an editor of your choice and navigate to the main function and edit the values for 'files'. The first file is the train data and the second file is the test data. You will also need to change the names to match with your dataset. After changing the names you will have to declare what the categorical features are and you will need to change your target value when creating the data object. When you want to leave the virtual environment simply type deactivate.

If you speak german we highly recommend reading our blog. You will find helpful information and insights about what the script does and how it works. Simply hit the following link: https://rocketloop.de/machine-learning-klassifizierung-in-python-teil-1/

Using datasplitter.py for creating sub data sets

We included reduced data sets in the data_files folder to compare training times. The files were created using the datasplitter.py, a small script which splits a dataset into a 1/4 train set and 3/4 test set. It may be a bit buggy so verify the outcome manually if you want to use it. Usage: Change the value for file to the file you want to split. Change the value for filee to the file you want to store the train data in. Change the value for fileee to the file you want to store the test data in.

Additional info

• We already implemented the svm classifier but it got deactivated for performance reson. If you want to use the svm you simply have to remove the commented sections in the main method regarding svm. You should be aware that you either have to use a powerful machine or use a smaller dataset otherwise it takes a very long time for the process to complete.
• classifier.ipynb is not up to date. Feel free to use it but expect it not to work properly.