3_ML-Workflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML-Workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Für dieses Notebook verwenden wir zwei Datensätze von [Sendy Logistic](https://zindi.africa/competitions/sendy-logistics-challenge/data) (ein afrikanischer Bike-Taxi-Service). Die Problemstellung lautet:\n",
    "\"The dataset provided by Sendy includes order details and rider metrics based on orders made on the Sendy platform. The challenge is to predict the estimated time of arrival for orders from pick-up to drop-off.\"\n",
    "\n",
    "Dazu gibt es zwei Datensätze im CSV-Format:\n",
    "1. Daten zu den einzelnen Fahrten mit verschiedenen Attributen zu der tatsächlichen Dauer der Fahrt.\n",
    "2. Details zu den Fahrern welche auf den Fahrten im Einsatz sind."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hinweis: Dieses Notebook folgt in weiten Teilen diesem (sehr lesenswerten) [Blog-Post](https://heartbeat.fritz.ai/a-practical-guide-to-feature-engineering-in-python-8326e40747c8). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Laden der Datensätze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None # avoids some (false-positive) warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21201, 29)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order No</th>\n",
       "      <th>User Id</th>\n",
       "      <th>Vehicle Type</th>\n",
       "      <th>Platform Type</th>\n",
       "      <th>Personal or Business</th>\n",
       "      <th>Placement - Day of Month</th>\n",
       "      <th>Placement - Weekday (Mo = 1)</th>\n",
       "      <th>Placement - Time</th>\n",
       "      <th>Confirmation - Day of Month</th>\n",
       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
       "      <th>...</th>\n",
       "      <th>Arrival at Destination - Time</th>\n",
       "      <th>Distance (KM)</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Precipitation in millimeters</th>\n",
       "      <th>Pickup Lat</th>\n",
       "      <th>Pickup Long</th>\n",
       "      <th>Destination Lat</th>\n",
       "      <th>Destination Long</th>\n",
       "      <th>Rider Id</th>\n",
       "      <th>Time from Pickup to Arrival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Order_No_4211</td>\n",
       "      <td>User_Id_633</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>9:35:46 AM</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>10:39:55 AM</td>\n",
       "      <td>4</td>\n",
       "      <td>20.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.317755</td>\n",
       "      <td>36.830370</td>\n",
       "      <td>-1.300406</td>\n",
       "      <td>36.829741</td>\n",
       "      <td>Rider_Id_432</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Order_No_25375</td>\n",
       "      <td>User_Id_2285</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Personal</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>11:16:16 AM</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>12:17:22 PM</td>\n",
       "      <td>16</td>\n",
       "      <td>26.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.351453</td>\n",
       "      <td>36.899315</td>\n",
       "      <td>-1.295004</td>\n",
       "      <td>36.814358</td>\n",
       "      <td>Rider_Id_856</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Order_No_1899</td>\n",
       "      <td>User_Id_265</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>12:39:25 PM</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1:00:38 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.308284</td>\n",
       "      <td>36.843419</td>\n",
       "      <td>-1.300921</td>\n",
       "      <td>36.828195</td>\n",
       "      <td>Rider_Id_155</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Order_No_9336</td>\n",
       "      <td>User_Id_1402</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>9:25:34 AM</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>10:05:27 AM</td>\n",
       "      <td>9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.281301</td>\n",
       "      <td>36.832396</td>\n",
       "      <td>-1.257147</td>\n",
       "      <td>36.795063</td>\n",
       "      <td>Rider_Id_855</td>\n",
       "      <td>1341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Order_No_27883</td>\n",
       "      <td>User_Id_1737</td>\n",
       "      <td>Bike</td>\n",
       "      <td>1</td>\n",
       "      <td>Personal</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>9:55:18 AM</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>10:25:37 AM</td>\n",
       "      <td>9</td>\n",
       "      <td>15.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.266597</td>\n",
       "      <td>36.792118</td>\n",
       "      <td>-1.295041</td>\n",
       "      <td>36.809817</td>\n",
       "      <td>Rider_Id_770</td>\n",
       "      <td>1214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
       "0   Order_No_4211   User_Id_633         Bike              3   \n",
       "1  Order_No_25375  User_Id_2285         Bike              3   \n",
       "2   Order_No_1899   User_Id_265         Bike              3   \n",
       "3   Order_No_9336  User_Id_1402         Bike              3   \n",
       "4  Order_No_27883  User_Id_1737         Bike              1   \n",
       "\n",
       "  Personal or Business  Placement - Day of Month  \\\n",
       "0             Business                         9   \n",
       "1             Personal                        12   \n",
       "2             Business                        30   \n",
       "3             Business                        15   \n",
       "4             Personal                        13   \n",
       "\n",
       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
       "0                             5       9:35:46 AM                            9   \n",
       "1                             5      11:16:16 AM                           12   \n",
       "2                             2      12:39:25 PM                           30   \n",
       "3                             5       9:25:34 AM                           15   \n",
       "4                             1       9:55:18 AM                           13   \n",
       "\n",
       "   Confirmation - Weekday (Mo = 1)  ... Arrival at Destination - Time  \\\n",
       "0                                5  ...                   10:39:55 AM   \n",
       "1                                5  ...                   12:17:22 PM   \n",
       "2                                2  ...                    1:00:38 PM   \n",
       "3                                5  ...                   10:05:27 AM   \n",
       "4                                1  ...                   10:25:37 AM   \n",
       "\n",
       "   Distance (KM)  Temperature Precipitation in millimeters  Pickup Lat  \\\n",
       "0              4         20.4                          NaN   -1.317755   \n",
       "1             16         26.4                          NaN   -1.351453   \n",
       "2              3          NaN                          NaN   -1.308284   \n",
       "3              9         19.2                          NaN   -1.281301   \n",
       "4              9         15.4                          NaN   -1.266597   \n",
       "\n",
       "   Pickup Long Destination Lat  Destination Long      Rider Id  \\\n",
       "0    36.830370       -1.300406         36.829741  Rider_Id_432   \n",
       "1    36.899315       -1.295004         36.814358  Rider_Id_856   \n",
       "2    36.843419       -1.300921         36.828195  Rider_Id_155   \n",
       "3    36.832396       -1.257147         36.795063  Rider_Id_855   \n",
       "4    36.792118       -1.295041         36.809817  Rider_Id_770   \n",
       "\n",
       "  Time from Pickup to Arrival  \n",
       "0                         745  \n",
       "1                        1993  \n",
       "2                         455  \n",
       "3                        1341  \n",
       "4                        1214  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/artyerokhin/zindy_logistics_challenge/master/Train.csv'\n",
    "df_trips = pd.read_csv(url)\n",
    "print(df_trips.shape)\n",
    "df_trips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um Datensätze mit vielen Features übersichtlich darzustellen, hilft das Transponieren des DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Order No</th>\n",
       "      <td>Order_No_4211</td>\n",
       "      <td>Order_No_25375</td>\n",
       "      <td>Order_No_1899</td>\n",
       "      <td>Order_No_9336</td>\n",
       "      <td>Order_No_27883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User Id</th>\n",
       "      <td>User_Id_633</td>\n",
       "      <td>User_Id_2285</td>\n",
       "      <td>User_Id_265</td>\n",
       "      <td>User_Id_1402</td>\n",
       "      <td>User_Id_1737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vehicle Type</th>\n",
       "      <td>Bike</td>\n",
       "      <td>Bike</td>\n",
       "      <td>Bike</td>\n",
       "      <td>Bike</td>\n",
       "      <td>Bike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platform Type</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personal or Business</th>\n",
       "      <td>Business</td>\n",
       "      <td>Personal</td>\n",
       "      <td>Business</td>\n",
       "      <td>Business</td>\n",
       "      <td>Personal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placement - Day of Month</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placement - Weekday (Mo = 1)</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placement - Time</th>\n",
       "      <td>9:35:46 AM</td>\n",
       "      <td>11:16:16 AM</td>\n",
       "      <td>12:39:25 PM</td>\n",
       "      <td>9:25:34 AM</td>\n",
       "      <td>9:55:18 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Confirmation - Day of Month</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Confirmation - Time</th>\n",
       "      <td>9:40:10 AM</td>\n",
       "      <td>11:23:21 AM</td>\n",
       "      <td>12:42:44 PM</td>\n",
       "      <td>9:26:05 AM</td>\n",
       "      <td>9:56:18 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival at Pickup - Day of Month</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival at Pickup - Time</th>\n",
       "      <td>10:04:47 AM</td>\n",
       "      <td>11:40:22 AM</td>\n",
       "      <td>12:49:34 PM</td>\n",
       "      <td>9:37:56 AM</td>\n",
       "      <td>10:03:53 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pickup - Day of Month</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pickup - Time</th>\n",
       "      <td>10:27:30 AM</td>\n",
       "      <td>11:44:09 AM</td>\n",
       "      <td>12:53:03 PM</td>\n",
       "      <td>9:43:06 AM</td>\n",
       "      <td>10:05:23 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival at Destination - Day of Month</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival at Destination - Time</th>\n",
       "      <td>10:39:55 AM</td>\n",
       "      <td>12:17:22 PM</td>\n",
       "      <td>1:00:38 PM</td>\n",
       "      <td>10:05:27 AM</td>\n",
       "      <td>10:25:37 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance (KM)</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temperature</th>\n",
       "      <td>20.4</td>\n",
       "      <td>26.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.2</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precipitation in millimeters</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pickup Lat</th>\n",
       "      <td>-1.31775</td>\n",
       "      <td>-1.35145</td>\n",
       "      <td>-1.30828</td>\n",
       "      <td>-1.2813</td>\n",
       "      <td>-1.2666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pickup Long</th>\n",
       "      <td>36.8304</td>\n",
       "      <td>36.8993</td>\n",
       "      <td>36.8434</td>\n",
       "      <td>36.8324</td>\n",
       "      <td>36.7921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Destination Lat</th>\n",
       "      <td>-1.30041</td>\n",
       "      <td>-1.295</td>\n",
       "      <td>-1.30092</td>\n",
       "      <td>-1.25715</td>\n",
       "      <td>-1.29504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Destination Long</th>\n",
       "      <td>36.8297</td>\n",
       "      <td>36.8144</td>\n",
       "      <td>36.8282</td>\n",
       "      <td>36.7951</td>\n",
       "      <td>36.8098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rider Id</th>\n",
       "      <td>Rider_Id_432</td>\n",
       "      <td>Rider_Id_856</td>\n",
       "      <td>Rider_Id_155</td>\n",
       "      <td>Rider_Id_855</td>\n",
       "      <td>Rider_Id_770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time from Pickup to Arrival</th>\n",
       "      <td>745</td>\n",
       "      <td>1993</td>\n",
       "      <td>455</td>\n",
       "      <td>1341</td>\n",
       "      <td>1214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       0               1  \\\n",
       "Order No                                   Order_No_4211  Order_No_25375   \n",
       "User Id                                      User_Id_633    User_Id_2285   \n",
       "Vehicle Type                                        Bike            Bike   \n",
       "Platform Type                                          3               3   \n",
       "Personal or Business                            Business        Personal   \n",
       "Placement - Day of Month                               9              12   \n",
       "Placement - Weekday (Mo = 1)                           5               5   \n",
       "Placement - Time                              9:35:46 AM     11:16:16 AM   \n",
       "Confirmation - Day of Month                            9              12   \n",
       "Confirmation - Weekday (Mo = 1)                        5               5   \n",
       "Confirmation - Time                           9:40:10 AM     11:23:21 AM   \n",
       "Arrival at Pickup - Day of Month                       9              12   \n",
       "Arrival at Pickup - Weekday (Mo = 1)                   5               5   \n",
       "Arrival at Pickup - Time                     10:04:47 AM     11:40:22 AM   \n",
       "Pickup - Day of Month                                  9              12   \n",
       "Pickup - Weekday (Mo = 1)                              5               5   \n",
       "Pickup - Time                                10:27:30 AM     11:44:09 AM   \n",
       "Arrival at Destination - Day of Month                  9              12   \n",
       "Arrival at Destination - Weekday (Mo = 1)              5               5   \n",
       "Arrival at Destination - Time                10:39:55 AM     12:17:22 PM   \n",
       "Distance (KM)                                          4              16   \n",
       "Temperature                                         20.4            26.4   \n",
       "Precipitation in millimeters                         NaN             NaN   \n",
       "Pickup Lat                                      -1.31775        -1.35145   \n",
       "Pickup Long                                      36.8304         36.8993   \n",
       "Destination Lat                                 -1.30041          -1.295   \n",
       "Destination Long                                 36.8297         36.8144   \n",
       "Rider Id                                    Rider_Id_432    Rider_Id_856   \n",
       "Time from Pickup to Arrival                          745            1993   \n",
       "\n",
       "                                                       2              3  \\\n",
       "Order No                                   Order_No_1899  Order_No_9336   \n",
       "User Id                                      User_Id_265   User_Id_1402   \n",
       "Vehicle Type                                        Bike           Bike   \n",
       "Platform Type                                          3              3   \n",
       "Personal or Business                            Business       Business   \n",
       "Placement - Day of Month                              30             15   \n",
       "Placement - Weekday (Mo = 1)                           2              5   \n",
       "Placement - Time                             12:39:25 PM     9:25:34 AM   \n",
       "Confirmation - Day of Month                           30             15   \n",
       "Confirmation - Weekday (Mo = 1)                        2              5   \n",
       "Confirmation - Time                          12:42:44 PM     9:26:05 AM   \n",
       "Arrival at Pickup - Day of Month                      30             15   \n",
       "Arrival at Pickup - Weekday (Mo = 1)                   2              5   \n",
       "Arrival at Pickup - Time                     12:49:34 PM     9:37:56 AM   \n",
       "Pickup - Day of Month                                 30             15   \n",
       "Pickup - Weekday (Mo = 1)                              2              5   \n",
       "Pickup - Time                                12:53:03 PM     9:43:06 AM   \n",
       "Arrival at Destination - Day of Month                 30             15   \n",
       "Arrival at Destination - Weekday (Mo = 1)              2              5   \n",
       "Arrival at Destination - Time                 1:00:38 PM    10:05:27 AM   \n",
       "Distance (KM)                                          3              9   \n",
       "Temperature                                          NaN           19.2   \n",
       "Precipitation in millimeters                         NaN            NaN   \n",
       "Pickup Lat                                      -1.30828        -1.2813   \n",
       "Pickup Long                                      36.8434        36.8324   \n",
       "Destination Lat                                 -1.30092       -1.25715   \n",
       "Destination Long                                 36.8282        36.7951   \n",
       "Rider Id                                    Rider_Id_155   Rider_Id_855   \n",
       "Time from Pickup to Arrival                          455           1341   \n",
       "\n",
       "                                                        4  \n",
       "Order No                                   Order_No_27883  \n",
       "User Id                                      User_Id_1737  \n",
       "Vehicle Type                                         Bike  \n",
       "Platform Type                                           1  \n",
       "Personal or Business                             Personal  \n",
       "Placement - Day of Month                               13  \n",
       "Placement - Weekday (Mo = 1)                            1  \n",
       "Placement - Time                               9:55:18 AM  \n",
       "Confirmation - Day of Month                            13  \n",
       "Confirmation - Weekday (Mo = 1)                         1  \n",
       "Confirmation - Time                            9:56:18 AM  \n",
       "Arrival at Pickup - Day of Month                       13  \n",
       "Arrival at Pickup - Weekday (Mo = 1)                    1  \n",
       "Arrival at Pickup - Time                      10:03:53 AM  \n",
       "Pickup - Day of Month                                  13  \n",
       "Pickup - Weekday (Mo = 1)                               1  \n",
       "Pickup - Time                                 10:05:23 AM  \n",
       "Arrival at Destination - Day of Month                  13  \n",
       "Arrival at Destination - Weekday (Mo = 1)               1  \n",
       "Arrival at Destination - Time                 10:25:37 AM  \n",
       "Distance (KM)                                           9  \n",
       "Temperature                                          15.4  \n",
       "Precipitation in millimeters                          NaN  \n",
       "Pickup Lat                                        -1.2666  \n",
       "Pickup Long                                       36.7921  \n",
       "Destination Lat                                  -1.29504  \n",
       "Destination Long                                  36.8098  \n",
       "Rider Id                                     Rider_Id_770  \n",
       "Time from Pickup to Arrival                          1214  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trips.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auf Basis der folgenden Attributen bauen wir Features um das Label `Time from Pickup to Arrival` vorherzusagen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Personal or Business</th>\n",
       "      <th>Placement - Weekday (Mo = 1)</th>\n",
       "      <th>Distance (KM)</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Time from Pickup to Arrival</th>\n",
       "      <th>Rider Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Business</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>20.4</td>\n",
       "      <td>745</td>\n",
       "      <td>Rider_Id_432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Personal</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>26.4</td>\n",
       "      <td>1993</td>\n",
       "      <td>Rider_Id_856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Business</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>455</td>\n",
       "      <td>Rider_Id_155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Business</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>1341</td>\n",
       "      <td>Rider_Id_855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Personal</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>15.4</td>\n",
       "      <td>1214</td>\n",
       "      <td>Rider_Id_770</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Personal or Business  Placement - Weekday (Mo = 1)  Distance (KM)  \\\n",
       "0             Business                             5              4   \n",
       "1             Personal                             5             16   \n",
       "2             Business                             2              3   \n",
       "3             Business                             5              9   \n",
       "4             Personal                             1              9   \n",
       "\n",
       "   Temperature  Time from Pickup to Arrival      Rider Id  \n",
       "0         20.4                          745  Rider_Id_432  \n",
       "1         26.4                         1993  Rider_Id_856  \n",
       "2          NaN                          455  Rider_Id_155  \n",
       "3         19.2                         1341  Rider_Id_855  \n",
       "4         15.4                         1214  Rider_Id_770  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_columns = [\"Personal or Business\", \"Placement - Weekday (Mo = 1)\",\n",
    "                    \"Distance (KM)\",\"Temperature\", \"Time from Pickup to Arrival\",\n",
    "                    \"Rider Id\"]\n",
    "\n",
    "df_trips_selection = df_trips[selected_columns]\n",
    "df_trips_selection.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Der zweite Datensatz enthält Details über die Fahrer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rider Id</th>\n",
       "      <th>No_Of_Orders</th>\n",
       "      <th>Age</th>\n",
       "      <th>Average_Rating</th>\n",
       "      <th>No_of_Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rider_Id_396</td>\n",
       "      <td>2946</td>\n",
       "      <td>2298</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rider_Id_479</td>\n",
       "      <td>360</td>\n",
       "      <td>951</td>\n",
       "      <td>13.5</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rider_Id_648</td>\n",
       "      <td>1746</td>\n",
       "      <td>821</td>\n",
       "      <td>14.3</td>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rider_Id_753</td>\n",
       "      <td>314</td>\n",
       "      <td>980</td>\n",
       "      <td>12.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rider_Id_335</td>\n",
       "      <td>536</td>\n",
       "      <td>1113</td>\n",
       "      <td>13.7</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rider Id  No_Of_Orders   Age  Average_Rating  No_of_Ratings\n",
       "0  Rider_Id_396          2946  2298            14.0           1159\n",
       "1  Rider_Id_479           360   951            13.5            176\n",
       "2  Rider_Id_648          1746   821            14.3            466\n",
       "3  Rider_Id_753           314   980            12.5             75\n",
       "4  Rider_Id_335           536  1113            13.7            156"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://raw.githubusercontent.com/artyerokhin/zindy_logistics_challenge/master/Riders.csv\"\n",
    "df_riders = pd.read_csv(url)\n",
    "df_riders.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In diesem Datensatz interessiert uns nur das Attribut `Average_Rating`, welches die durchschnittliche Bewertung des Fahrers auf seinen bisherigen Trips angibt. Das Attribut `Rider Id` behalten wir ebenfalls, da wir es für die Verknüpfung mit dem anderen Datensatz benötigen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Im nächsten Schritt werden die zwei Datensätze mit einem `merge` über das gemeinsame Attribute `Rider Id` zusammengeführt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Personal or Business</th>\n",
       "      <th>Placement - Weekday (Mo = 1)</th>\n",
       "      <th>Distance (KM)</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Time from Pickup to Arrival</th>\n",
       "      <th>Rider Id</th>\n",
       "      <th>No_Of_Orders</th>\n",
       "      <th>Age</th>\n",
       "      <th>Average_Rating</th>\n",
       "      <th>No_of_Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Business</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>20.4</td>\n",
       "      <td>745</td>\n",
       "      <td>Rider_Id_432</td>\n",
       "      <td>1637</td>\n",
       "      <td>1309</td>\n",
       "      <td>13.8</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Personal</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>24.5</td>\n",
       "      <td>2886</td>\n",
       "      <td>Rider_Id_432</td>\n",
       "      <td>1637</td>\n",
       "      <td>1309</td>\n",
       "      <td>13.8</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Business</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>24.7</td>\n",
       "      <td>2615</td>\n",
       "      <td>Rider_Id_432</td>\n",
       "      <td>1637</td>\n",
       "      <td>1309</td>\n",
       "      <td>13.8</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Personal</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>15.2</td>\n",
       "      <td>2986</td>\n",
       "      <td>Rider_Id_432</td>\n",
       "      <td>1637</td>\n",
       "      <td>1309</td>\n",
       "      <td>13.8</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Personal</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>19.2</td>\n",
       "      <td>1602</td>\n",
       "      <td>Rider_Id_432</td>\n",
       "      <td>1637</td>\n",
       "      <td>1309</td>\n",
       "      <td>13.8</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Personal or Business  Placement - Weekday (Mo = 1)  Distance (KM)  \\\n",
       "0             Business                             5              4   \n",
       "1             Personal                             5             20   \n",
       "2             Business                             5              6   \n",
       "3             Personal                             2             18   \n",
       "4             Personal                             2              7   \n",
       "\n",
       "   Temperature  Time from Pickup to Arrival      Rider Id  No_Of_Orders   Age  \\\n",
       "0         20.4                          745  Rider_Id_432          1637  1309   \n",
       "1         24.5                         2886  Rider_Id_432          1637  1309   \n",
       "2         24.7                         2615  Rider_Id_432          1637  1309   \n",
       "3         15.2                         2986  Rider_Id_432          1637  1309   \n",
       "4         19.2                         1602  Rider_Id_432          1637  1309   \n",
       "\n",
       "   Average_Rating  No_of_Ratings  \n",
       "0            13.8            549  \n",
       "1            13.8            549  \n",
       "2            13.8            549  \n",
       "3            13.8            549  \n",
       "4            13.8            549  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(df_trips_selection, df_riders, on='Rider Id')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Date Preparation und Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Attribute `Personal or Business` und `Placement - Weekday (Mo = 1)` sind beide kategorisch, d.h. sie können nicht direkt als Feature genutzt werden, sondern müssen mit Hifle von \"one-hot-encoding\" transformiert werden:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Business</th>\n",
       "      <th>Personal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "   Business  Personal\n",
       "0         1         0\n",
       "1         0         1\n",
       "2         1         0\n",
       "3         0         1\n",
       "4         0         1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_feature_1 = pd.get_dummies(data[\"Personal or Business\"])\n",
    "one_hot_feature_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1  2  3  4  5  6  7\n",
       "0  0  0  0  0  1  0  0\n",
       "1  0  0  0  0  1  0  0\n",
       "2  0  0  0  0  1  0  0\n",
       "3  0  1  0  0  0  0  0\n",
       "4  0  1  0  0  0  0  0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_feature_2 = pd.get_dummies(data[\"Placement - Weekday (Mo = 1)\"])\n",
    "one_hot_feature_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jede Spalte entspricht hier einem Wochentag.\n",
    "Als nächstes werden diese Features wieder dem DataFrame hinzugefügt:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Personal or Business</th>\n",
       "      <th>Placement - Weekday (Mo = 1)</th>\n",
       "      <th>Distance (KM)</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Time from Pickup to Arrival</th>\n",
       "      <th>Rider Id</th>\n",
       "      <th>No_Of_Orders</th>\n",
       "      <th>Age</th>\n",
       "      <th>Average_Rating</th>\n",
       "      <th>No_of_Ratings</th>\n",
       "      <th>Business</th>\n",
       "      <th>Personal</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Business</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>20.4</td>\n",
       "      <td>745</td>\n",
       "      <td>Rider_Id_432</td>\n",
       "      <td>1637</td>\n",
       "      <td>1309</td>\n",
       "      <td>13.8</td>\n",
       "      <td>549</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Personal</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>24.5</td>\n",
       "      <td>2886</td>\n",
       "      <td>Rider_Id_432</td>\n",
       "      <td>1637</td>\n",
       "      <td>1309</td>\n",
       "      <td>13.8</td>\n",
       "      <td>549</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Business</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>24.7</td>\n",
       "      <td>2615</td>\n",
       "      <td>Rider_Id_432</td>\n",
       "      <td>1637</td>\n",
       "      <td>1309</td>\n",
       "      <td>13.8</td>\n",
       "      <td>549</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Personal</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>15.2</td>\n",
       "      <td>2986</td>\n",
       "      <td>Rider_Id_432</td>\n",
       "      <td>1637</td>\n",
       "      <td>1309</td>\n",
       "      <td>13.8</td>\n",
       "      <td>549</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Personal</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>19.2</td>\n",
       "      <td>1602</td>\n",
       "      <td>Rider_Id_432</td>\n",
       "      <td>1637</td>\n",
       "      <td>1309</td>\n",
       "      <td>13.8</td>\n",
       "      <td>549</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Personal or Business  Placement - Weekday (Mo = 1)  Distance (KM)  \\\n",
       "0             Business                             5              4   \n",
       "1             Personal                             5             20   \n",
       "2             Business                             5              6   \n",
       "3             Personal                             2             18   \n",
       "4             Personal                             2              7   \n",
       "\n",
       "   Temperature  Time from Pickup to Arrival      Rider Id  No_Of_Orders   Age  \\\n",
       "0         20.4                          745  Rider_Id_432          1637  1309   \n",
       "1         24.5                         2886  Rider_Id_432          1637  1309   \n",
       "2         24.7                         2615  Rider_Id_432          1637  1309   \n",
       "3         15.2                         2986  Rider_Id_432          1637  1309   \n",
       "4         19.2                         1602  Rider_Id_432          1637  1309   \n",
       "\n",
       "   Average_Rating  No_of_Ratings  Business  Personal  1  2  3  4  5  6  7  \n",
       "0            13.8            549         1         0  0  0  0  0  1  0  0  \n",
       "1            13.8            549         0         1  0  0  0  0  1  0  0  \n",
       "2            13.8            549         1         0  0  0  0  0  1  0  0  \n",
       "3            13.8            549         0         1  0  1  0  0  0  0  0  \n",
       "4            13.8            549         0         1  0  1  0  0  0  0  0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_all = pd.concat([data, one_hot_feature_1, one_hot_feature_2], axis=1)\n",
    "data_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Time from Pickup to Arrival` ist das Label welches vorhergesagt werden soll. Da es numerisch ist, handelt es sich um ein Regressionsproblem (keine Klassifikation).\n",
    "\n",
    "Als nächstes unterteilen wir die Daten in Train- und Testdaten:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance (KM)</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>No_Of_Orders</th>\n",
       "      <th>Age</th>\n",
       "      <th>Average_Rating</th>\n",
       "      <th>No_of_Ratings</th>\n",
       "      <th>Business</th>\n",
       "      <th>Personal</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9470</th>\n",
       "      <td>21</td>\n",
       "      <td>22.1</td>\n",
       "      <td>730</td>\n",
       "      <td>1113</td>\n",
       "      <td>14.4</td>\n",
       "      <td>156</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>20042</th>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3670</td>\n",
       "      <td>2015</td>\n",
       "      <td>13.5</td>\n",
       "      <td>1193</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>928</td>\n",
       "      <td>494</td>\n",
       "      <td>14.3</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18964</th>\n",
       "      <td>6</td>\n",
       "      <td>23.1</td>\n",
       "      <td>582</td>\n",
       "      <td>273</td>\n",
       "      <td>13.2</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6894</th>\n",
       "      <td>2</td>\n",
       "      <td>16.8</td>\n",
       "      <td>243</td>\n",
       "      <td>911</td>\n",
       "      <td>14.1</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Distance (KM)  Temperature  No_Of_Orders   Age  Average_Rating  \\\n",
       "9470              21         22.1           730  1113            14.4   \n",
       "20042             12          NaN          3670  2015            13.5   \n",
       "1889               3          NaN           928   494            14.3   \n",
       "18964              6         23.1           582   273            13.2   \n",
       "6894               2         16.8           243   911            14.1   \n",
       "\n",
       "       No_of_Ratings  Business  Personal  1  2  3  4  5  6  7  \n",
       "9470             156         1         0  0  0  0  0  0  1  0  \n",
       "20042           1193         1         0  0  0  0  0  1  0  0  \n",
       "1889              43         1         0  0  1  0  0  0  0  0  \n",
       "18964             42         1         0  0  0  0  0  1  0  0  \n",
       "6894              84         1         0  0  0  0  1  0  0  0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_X = data_all.drop(columns = [\"Personal or Business\", \"Placement - Weekday (Mo = 1)\",\n",
    "                            \"Time from Pickup to Arrival\", \"Rider Id\"])\n",
    "df_y = data_all[\"Time from Pickup to Arrival\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_X, df_y, test_size=0.2, random_state=0)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Im nächsten Schritt schauen wir uns die fehlende Datenfelder an. Dazu schauen wir wie oft einzelne Felder fehlen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Distance (KM)        0\n",
       "Temperature       3500\n",
       "No_Of_Orders         0\n",
       "Age                  0\n",
       "Average_Rating       0\n",
       "No_of_Ratings        0\n",
       "Business             0\n",
       "Personal             0\n",
       "1                    0\n",
       "2                    0\n",
       "3                    0\n",
       "4                    0\n",
       "5                    0\n",
       "6                    0\n",
       "7                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Das Attribut `Temperature` fehlt in vielen Datenpunkten.\n",
    "Daher ersetzen wir die fehlenden Felder mit dem Durchschnittswert der vorhandenen Felder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.264873699851414\n"
     ]
    }
   ],
   "source": [
    "temperature_mean = X_train['Temperature'].mean()\n",
    "print(temperature_mean)\n",
    "X_train['Temperature'] = X_train['Temperature'].fillna(temperature_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Distance (KM)     0\n",
       "Temperature       0\n",
       "No_Of_Orders      0\n",
       "Age               0\n",
       "Average_Rating    0\n",
       "No_of_Ratings     0\n",
       "Business          0\n",
       "Personal          0\n",
       "1                 0\n",
       "2                 0\n",
       "3                 0\n",
       "4                 0\n",
       "5                 0\n",
       "6                 0\n",
       "7                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alle Attribute müssen vor dem Training des ML-Modells noch normalisiert werden. Dafür nutzen wir eine Methode aus sklearn. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.04002188, -0.36232115, -0.61369115, ..., -0.47880344,\n",
       "         4.03808401, -0.09188762],\n",
       "       [ 0.44304087,  0.        ,  1.25536701, ...,  2.08853971,\n",
       "        -0.2476422 , -0.09188762],\n",
       "       [-1.15394014,  0.        , -0.48781581, ..., -0.47880344,\n",
       "        -0.2476422 , -0.09188762],\n",
       "       ...,\n",
       "       [ 0.79792554, -0.79777574, -0.30981027, ..., -0.47880344,\n",
       "        -0.2476422 , -0.09188762],\n",
       "       [-0.26672847, -0.26900945,  1.10342657, ..., -0.47880344,\n",
       "        -0.2476422 , -0.09188762],\n",
       "       [-0.44417081, -0.14459385,  0.25408585, ..., -0.47880344,\n",
       "        -0.2476422 , -0.09188762]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_scaled = scaler.transform(X_train)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nach diesen Schritten könne wir jetzt eine lineare Regression auf den Daten trainieren:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "reg = LinearRegression()\n",
    "reg.fit(X_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bevor wir das Modell auf den Testdaten testen, gibt es einiges zu beachten:\n",
    "\n",
    "- Zuerst entfernen wir aus den Testdaten alle Datenpunkte welche ein N/A enthalten (Hinweis: Beim Testen werden die fehlenden Werte nicht aufgefüllt, wie beim Training)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3375, 15)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = X_test.dropna()\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Da wir die Datenpunkte nur aus `X_test` entfernt haben, müssen die übrig gebliebenen Punkte auch aus `y_test` ausgewählt werden:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3375,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = y_test[X_test.index]\n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Als nächstes nehmen wir den oben erstellten Skalierer um auch die Testdaten zu skalieren:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Letztendlich können wir auf den Testdaten vorhersagen und den MSE berechnen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error (MSE) auf Testdaten: 613034.31\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "predictions = reg.predict(X_test_scaled)\n",
    "mse =  mean_squared_error(y_test, predictions)\n",
    "print(\"Mean squared error (MSE) auf Testdaten: %.2f\" % mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "782.9650779739837"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "math.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wie wir sehen, liegen wir im Schnitt 786 Sekunden (= 13 Minuten) daneben. Schauen wir uns kurz die Verteilung des Labels an, um dies einzuordnen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test.hist(bins= 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Im Vergleich zu diesem Wertebereich ist die mittlere Vorhersagegenauigkeit einigermaßen okay. Würden wir noch mehr Features aus den gegebenen Daten bauen, könnten wir noch um einiges besser werden."
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}