{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Information\n", "__Section__: Missing data \n", "__Goal__: Understand how missing data affect the prediction and how to fix it. \n", "__Time needed__: 50 min \n", "__Prerequisites__: AIS data, basics about machine learning\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Missing data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this section, we will make some experiments on the data used and discover how to deal with missing data. For that, we will perform two different tasks of prediction, and see how the removal of missing data affects the performance of the model. We will end on a generalization about how to deal with missing data in machine learning." ] } ], "metadata": { "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": "3.7.1" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }