{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Feature Engineering\n",
    "# Klassifikation\n",
    "# Regression\n",
    "## Einleitung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('./')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-08T10:06:24.890328Z",
     "start_time": "2020-04-08T10:06:23.220148Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## prepare\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "%matplotlib inline\n",
    "\n",
    "datapath = '../3_data'\n",
    "from os import chdir; chdir(datapath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Abgrenzung gegenüber Klassifikation\n",
    "(kein Code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Das Demo Dataset: demo_data_regr.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     X    y\n",
      "0  6.0  2.5\n",
      "1  5.1  1.9\n",
      "2  5.9  2.1\n",
      "3  5.6  1.8\n",
      "4  5.8  2.2\n"
     ]
    }
   ],
   "source": [
    "demo_data = pd.read_csv('demo_data_regr.csv')\n",
    "print(demo_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-08T10:06:25.476230Z",
     "start_time": "2020-04-08T10:06:24.976970Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "ax = sns.scatterplot(x='X', y='y', data=demo_data)\n",
    "ax.set(xlabel='X', ylabel='y');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Das Fallstudien Dataset: melb_data_prep.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18393 entries, 0 to 18392\n",
      "Data columns (total 24 columns):\n",
      " #   Column                                 Non-Null Count  Dtype  \n",
      "---  ------                                 --------------  -----  \n",
      " 0   Rooms                                  18393 non-null  int64  \n",
      " 1   Type                                   18393 non-null  int64  \n",
      " 2   Price                                  18393 non-null  float64\n",
      " 3   Distance                               18393 non-null  float64\n",
      " 4   Bathroom                               18393 non-null  float64\n",
      " 5   Car                                    18393 non-null  float64\n",
      " 6   logLandsize                            18393 non-null  float64\n",
      " 7   logBuildingArea                        18393 non-null  float64\n",
      " 8   YearBuilt                              18393 non-null  float64\n",
      " 9   CouncilArea                            18393 non-null  int64  \n",
      " 10  Lattitude                              18393 non-null  float64\n",
      " 11  Longtitude                             18393 non-null  float64\n",
      " 12  Propertycount                          18393 non-null  float64\n",
      " 13  Method_S                               18393 non-null  int64  \n",
      " 14  Method_SP                              18393 non-null  int64  \n",
      " 15  Method_VB                              18393 non-null  int64  \n",
      " 16  Regionname_Northern_Metropolitan       18393 non-null  int64  \n",
      " 17  Regionname_South_Eastern_Metropolitan  18393 non-null  int64  \n",
      " 18  Regionname_Southern_Metropolitan       18393 non-null  int64  \n",
      " 19  Regionname_Victoria                    18393 non-null  int64  \n",
      " 20  Regionname_Western_Metropolitan        18393 non-null  int64  \n",
      " 21  month                                  18393 non-null  int64  \n",
      " 22  year                                   18393 non-null  int64  \n",
      " 23  day_of_week                            18393 non-null  int64  \n",
      "dtypes: float64(10), int64(14)\n",
      "memory usage: 3.4 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('melb_data_prep.csv')\n",
    "print(data.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vorbereiten der Daten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## read and prep data\n",
    "from bfh_cas_pml import prep_data, prep_demo_data\n",
    "X_train, X_test, y_train, y_test = prep_data(\n",
    "    'melb_data_prep.csv', 'Price', seed = 1234)\n",
    "\n",
    "X_demo, y_demo = prep_demo_data('demo_data_regr.csv', 'y')"
   ]
  }
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