{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"1aa6599b-36ce-0266-ed82-c11100891cd6"},"source":"# Intel & MobileODT Cervical Cancer Screening\n\nWelcome to another image processing competition! This time it's a competition from Intel, where we are trying to predict the cervix type from cervical images, before they might develop into cancer.\n\nThe goal is to predict one of three classes, so this seems like a straightforward image classification challenge, with the mlogloss evaluation metric."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"2a719a88-d31a-5f89-2204-fcc882056c9d"},"outputs":[],"source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport os\nimport glob\n%matplotlib inline\np = sns.color_palette()\n\nos.listdir('../input')\nfor folder in ['additional', '']"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"c757bc12-1f0e-e370-a562-f43f5a7f44c4"},"outputs":[],"source":""}],"metadata":{"_change_revision":0,"_is_fork":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.0"}},"nbformat":4,"nbformat_minor":0}