The following is a file Loader script that tweaks the existing sklearn.datasets load_files function to recursively load nested directory structure. The Following script expects you to have installed sklearn. If not already installed do a : pip install sklearn or sudo pip install sklearn Load text files with categories as subfolder names. Individual samples are assumed to be files stored a heirarchical folder structure The folder names are used as supervised signal label names. The individual file names are not important. This function does not try to extract features into a numpy array or scipy sparse matrix. In addition, if load_content is false it does not try to load the files in memory. To use text files in a scikit-learn classification or clustering algorithm, you will need to use the sklearn.feature_extraction.text module to build a feature extraction transformer that suits your problem. If you set load_content=True, you should also specify the encoding of the text using the 'encoding' parameter. For many modern text files, 'utf-8' will be the correct encoding. If you leave encoding equal to None, then the content will be made of bytes instead of Unicode, and you will not be able to use most functions in sklearn.feature_extraction.text. Similar feature extractors should be built for other kind of unstructured data input such as images, audio, video, ...