{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**nicht als Notebook verteilen**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Feature Engineering\n",
    "\n",
    "## Feature Engineering - Einführung\n",
    "\n",
    "### Abgrenzungen\n",
    "\n",
    "### CRISP - und die Gliederung des Kurses\n",
    "\n",
    "### Strukturierte Daten\n",
    "\n",
    "#### Aufbau und Organisation eines Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     age            job  marital  duration  campaign    y\n",
      "0   30.0  self-employed   single     245.0         3  yes\n",
      "1   32.0     technician  married     370.0         1   no\n",
      "2   27.0    blue collar   single     623.0         1  yes\n",
      "3    NaN    blue collar   single       9.0         6   no\n",
      "4   27.0         admin.   single     126.0         2  yes\n",
      "5   34.0         admin.   single     548.0         2  yes\n",
      "6   46.0           None  married      86.0         2   no\n",
      "7    NaN        retired  married     707.0         3  yes\n",
      "8   46.0         admin.  married      96.0         6   no\n",
      "9   48.0    blue collar  married     241.0         2   no\n",
      "10  29.0     technician  married     154.0         3   no\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('../3_data/bank_data.csv', sep=';')\n",
    "#print(data.iloc[0:11, 0:6])\n",
    "\n",
    "## arbitrarily input\n",
    "data.iloc[3, 0] = None\n",
    "data.iloc[6, 1] = None\n",
    "#print(data.iloc[0:11, 0:6])\n",
    "print(data.iloc[0:11, [0,1,2,10,11,20]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Begriffe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Beispieldaten"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import pandas as pd\n",
    "demo_data_class = pd.read_csv('../3_data/demo_data_class.csv')\n",
    "demo_data_regr = pd.read_csv('../3_data/demo_data_regr.csv') "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import seaborn as sns\n",
    "iris_data = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Anforderungen an die Daten für Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eine typische ML Sequenz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Die Python Libraries und CRISP-DM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Begleitende Literatur"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.13.0"
  },
  "toc": {
   "base_numbering": "1.1",
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "1.1 Feature Engineering - Einführung",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "251px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "toc-autonumbering": true,
  "toc-showcode": false,
  "toc-showmarkdowntxt": false,
  "toc-showtags": false,
  "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()) "
    }
   },
   "position": {
    "height": "326.85px",
    "left": "910px",
    "right": "20px",
    "top": "120px",
    "width": "350px"
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}