{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\ntrain = next(pd.read_csv(\"/kaggle/input/riiid-test-answer-prediction/train.csv\", chunksize=10**5))\ntrain.head()\n","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\n%matplotlib inline \n\ntrain.hist(figsize=(10,10))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"correct = train[\"answered_correctly\"] == 1\ntrain[correct][\"prior_question_elapsed_time\"].hist()\ntrain[~correct][\"prior_question_elapsed_time\"].hist()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train[correct][\"timestamp\"].hist()\ntrain[~correct][\"timestamp\"].hist()\n# these should probably be normalized...","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.plotting.autocorrelation_plot(train[\"answered_correctly\"].iloc[:1000])\n# no autocorrelation","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# to be completely honest, i have no idea what other EDA to do here\n# i hadn't realized at all that you could use transformers on time series https://arxiv.org/abs/2010.12042","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}