mutipleCustomers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello world\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello world\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from service_time_fake import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "service_time1 = service_time(2, 3, True, True, \"0680\", None, None)\n",
    "service_time2 = service_time(20, 3, True, True, \"0680\", None, None)\n",
    "service_time3 = service_time(40, 3, True, True, \"0680\", None, None)\n",
    "service_time4 = service_time(10, 3, True, True, \"0680\", None, None)\n",
    "service_time5 = service_time(15, 3, True, True, \"0680\", None, None)\n",
    "service_time6 = service_time(90, 3, True, True, \"0680\", None, None)\n",
    "service_time7 = service_time(500, 3, True, True, \"0680\", None, None)\n",
    "service_time9 = service_time(50, 3, True, True, \"0680\", None, None)\n",
    "service_time10 = service_time(43, 3, True, True, \"0680\", None, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "service_time1.actual_st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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",
       "        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>gross_weight</th>\n",
       "      <th>floor</th>\n",
       "      <th>can_use_trolley</th>\n",
       "      <th>has_elevator</th>\n",
       "      <th>postnr</th>\n",
       "      <th>predicted_st</th>\n",
       "      <th>actual_st</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10003</th>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0694</td>\n",
       "      <td>None</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10004</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0693</td>\n",
       "      <td>None</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10005</th>\n",
       "      <td>54</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0697</td>\n",
       "      <td>None</td>\n",
       "      <td>28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10006</th>\n",
       "      <td>42</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0697</td>\n",
       "      <td>None</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10007</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0693</td>\n",
       "      <td>None</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gross_weight  floor  can_use_trolley  has_elevator postnr predicted_st  \\\n",
       "10003            66      2             True         False   0694         None   \n",
       "10004            21      2             True          True   0693         None   \n",
       "10005            54     10            False         False   0697         None   \n",
       "10006            42      6             True         False   0697         None   \n",
       "10007            20      0            False          True   0693         None   \n",
       "\n",
       "       actual_st  \n",
       "10003       15.6  \n",
       "10004        8.0  \n",
       "10005       28.5  \n",
       "10006       18.2  \n",
       "10007        5.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = [service_time1, service_time2, service_time3, service_time4, service_time5, service_time6, service_time7, service_time9, service_time10]\n",
    "\n",
    "for x in range (1, 10000, 1):\n",
    "    service_time_sample = service_time(None, None, None, None, None, None, None)\n",
    "    df.append(service_time_sample)\n",
    "\n",
    "df = pd.DataFrame(vars(f) for f in df)\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df2 = df[df['gross_weight'] < 100]\n",
    "plt.scatter(df2['gross_weight'], df2['actual_st'], color='green')\n",
    "plt.title('Gross weight Vs Service Time', fontsize=14)\n",
    "plt.xlabel('Gross Weight', fontsize=14)\n",
    "plt.ylabel('Service Time', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "X = df.drop(['predicted_st', 'actual_st'],axis=1) \n",
    "Y = df['actual_st']\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.20, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.9047929  19.09970611 16.26257762 ...  1.90952925 20.63339857\n",
      " 17.43031013]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg_model = LinearRegression().fit(X_train, Y_train)\n",
    "reg_predict = reg_model.predict(X_test) #Predictions on Testing data\n",
    "print(reg_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gross_weight</th>\n",
       "      <th>floor</th>\n",
       "      <th>can_use_trolley</th>\n",
       "      <th>has_elevator</th>\n",
       "      <th>postnr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9062</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>68</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3726</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4499</th>\n",
       "      <td>79</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8679</th>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8484</th>\n",
       "      <td>100</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1578</th>\n",
       "      <td>85</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5886</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7146</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2002 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gross_weight  floor  can_use_trolley  has_elevator postnr\n",
       "9062            62      0             True         False   0697\n",
       "836             68      8             True          True   0698\n",
       "3726             3      6            False         False   0695\n",
       "4499            79      4             True         False   0690\n",
       "8679            49      2            False         False   0698\n",
       "...            ...    ...              ...           ...    ...\n",
       "8484           100      8             True         False   0693\n",
       "1578            85      3            False         False   0695\n",
       "5886             4      1             True          True   0697\n",
       "2892            38      5            False         False   0692\n",
       "7146             4     10             True          True   0692\n",
       "\n",
       "[2002 rows x 5 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>gross_weight</th>\n",
       "      <th>floor</th>\n",
       "      <th>can_use_trolley</th>\n",
       "      <th>has_elevator</th>\n",
       "      <th>postnr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gross_weight  floor  can_use_trolley  has_elevator postnr\n",
       "0            30      3             True          True   0690\n",
       "1            30      3             True          True   0691"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "service_time_test1 = service_time(30, 3, True, True, \"0690\", 12, None)\n",
    "service_time_test2 = service_time(30, 3, True, True, \"0691\", 12, None)\n",
    "\n",
    "df_test = [service_time_test1, service_time_test2]\n",
    "df_test = pd.DataFrame(vars(f) for f in df_test)\n",
    "df_test = df_test.drop(['predicted_st', 'actual_st'],axis=1)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.6050617 , 11.07590661])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test1 = reg_model.predict(df_test)\n",
    "Test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gross_weight</th>\n",
       "      <th>floor</th>\n",
       "      <th>can_use_trolley</th>\n",
       "      <th>has_elevator</th>\n",
       "      <th>postnr</th>\n",
       "      <th>predicted_st</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0690</td>\n",
       "      <td>11.605062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0691</td>\n",
       "      <td>11.075907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gross_weight  floor  can_use_trolley  has_elevator postnr  predicted_st\n",
       "0            30      3             True          True   0690     11.605062\n",
       "1            30      3             True          True   0691     11.075907"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['predicted_st'] = Test1\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8410152257994479"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_model.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8475899475504438"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_model.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gross_weight</th>\n",
       "      <th>floor</th>\n",
       "      <th>can_use_trolley</th>\n",
       "      <th>has_elevator</th>\n",
       "      <th>postnr</th>\n",
       "      <th>predicted_st</th>\n",
       "      <th>actual_st</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6175</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0696</td>\n",
       "      <td>None</td>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4891</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0696</td>\n",
       "      <td>None</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7184</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0695</td>\n",
       "      <td>None</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2467</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0692</td>\n",
       "      <td>None</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0696</td>\n",
       "      <td>None</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7835</th>\n",
       "      <td>293</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0692</td>\n",
       "      <td>None</td>\n",
       "      <td>52.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8602</th>\n",
       "      <td>294</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0697</td>\n",
       "      <td>None</td>\n",
       "      <td>38.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8788</th>\n",
       "      <td>297</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0694</td>\n",
       "      <td>None</td>\n",
       "      <td>48.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4991</th>\n",
       "      <td>299</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0695</td>\n",
       "      <td>None</td>\n",
       "      <td>41.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>500</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0680</td>\n",
       "      <td>None</td>\n",
       "      <td>56.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10008 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gross_weight  floor  can_use_trolley  has_elevator postnr predicted_st  \\\n",
       "6175             0      9            False          True   0696         None   \n",
       "4891             0      1            False          True   0696         None   \n",
       "7184             0      6            False         False   0695         None   \n",
       "2467             0      9             True          True   0692         None   \n",
       "550              0      6             True         False   0696         None   \n",
       "...            ...    ...              ...           ...    ...          ...   \n",
       "7835           293      2            False         False   0692         None   \n",
       "8602           294      5             True          True   0697         None   \n",
       "8788           297      4             True         False   0694         None   \n",
       "4991           299      8             True          True   0695         None   \n",
       "6              500      3             True          True   0680         None   \n",
       "\n",
       "      actual_st  \n",
       "6175      16.25  \n",
       "4891       5.00  \n",
       "7184      15.00  \n",
       "2467      13.00  \n",
       "550       13.00  \n",
       "...         ...  \n",
       "7835      52.50  \n",
       "8602      38.00  \n",
       "8788      48.10  \n",
       "4991      41.00  \n",
       "6         56.00  \n",
       "\n",
       "[10008 rows x 7 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=['gross_weight'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a new dataframe with random values again, but for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gross_weight</th>\n",
       "      <th>floor</th>\n",
       "      <th>can_use_trolley</th>\n",
       "      <th>has_elevator</th>\n",
       "      <th>postnr</th>\n",
       "      <th>predicted_st</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>13.525483</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0680</td>\n",
       "      <td>15.692638</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
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       "      <td>True</td>\n",
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       "      <td>18.100587</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0680</td>\n",
       "      <td>14.488663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0680</td>\n",
       "      <td>15.090650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0697</td>\n",
       "      <td>15.408829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0691</td>\n",
       "      <td>17.174655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>65</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0690</td>\n",
       "      <td>23.511885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>68</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0694</td>\n",
       "      <td>26.089648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>96</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0698</td>\n",
       "      <td>17.330265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     gross_weight  floor  can_use_trolley  has_elevator postnr  predicted_st\n",
       "0               2      3             True          True   0680     13.525483\n",
       "1              20      3             True          True   0680     15.692638\n",
       "2              40      3             True          True   0680     18.100587\n",
       "3              10      3             True          True   0680     14.488663\n",
       "4              15      3             True          True   0680     15.090650\n",
       "..            ...    ...              ...           ...    ...           ...\n",
       "103            40      5             True         False   0697     15.408829\n",
       "104            81      1            False          True   0691     17.174655\n",
       "105            65      4            False         False   0690     23.511885\n",
       "106            68      9             True         False   0694     26.089648\n",
       "107            96      2             True         False   0698     17.330265\n",
       "\n",
       "[108 rows x 6 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_testing = [service_time1, service_time2, service_time3, service_time4, service_time5, service_time6, service_time7, service_time9, service_time10]\n",
    "\n",
    "for x in range (1, 100, 1):\n",
    "    service_time_sample = service_time(None, None, None, None, None, None, None)\n",
    "    df_testing.append(service_time_sample)\n",
    "\n",
    "df_testing = pd.DataFrame(vars(f) for f in df_testing)\n",
    "df_testing_actual = df_testing['actual_st']\n",
    "df_testing = df_testing.drop(['predicted_st', 'actual_st'],axis=1)\n",
    "df_testing['predicted_st'] = reg_model.predict(df_testing)\n",
    "\n",
    "df_testing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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",
       "        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>gross_weight</th>\n",
       "      <th>floor</th>\n",
       "      <th>can_use_trolley</th>\n",
       "      <th>has_elevator</th>\n",
       "      <th>postnr</th>\n",
       "      <th>predicted_st</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0680</td>\n",
       "      <td>13.525483</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0680</td>\n",
       "      <td>15.692638</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0680</td>\n",
       "      <td>18.100587</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0680</td>\n",
       "      <td>14.488663</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0680</td>\n",
       "      <td>15.090650</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0697</td>\n",
       "      <td>15.408829</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0691</td>\n",
       "      <td>17.174655</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>65</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0690</td>\n",
       "      <td>23.511885</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>68</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0694</td>\n",
       "      <td>26.089648</td>\n",
       "      <td>24.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>96</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0698</td>\n",
       "      <td>17.330265</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     gross_weight  floor  can_use_trolley  has_elevator postnr  predicted_st  \\\n",
       "0               2      3             True          True   0680     13.525483   \n",
       "1              20      3             True          True   0680     15.692638   \n",
       "2              40      3             True          True   0680     18.100587   \n",
       "3              10      3             True          True   0680     14.488663   \n",
       "4              15      3             True          True   0680     15.090650   \n",
       "..            ...    ...              ...           ...    ...           ...   \n",
       "103            40      5             True         False   0697     15.408829   \n",
       "104            81      1            False          True   0691     17.174655   \n",
       "105            65      4            False         False   0690     23.511885   \n",
       "106            68      9             True         False   0694     26.089648   \n",
       "107            96      2             True         False   0698     17.330265   \n",
       "\n",
       "     actual  \n",
       "0       7.0  \n",
       "1       8.0  \n",
       "2      10.0  \n",
       "3       7.0  \n",
       "4       8.0  \n",
       "..      ...  \n",
       "103    15.6  \n",
       "104    13.0  \n",
       "105    31.0  \n",
       "106    24.7  \n",
       "107    19.5  \n",
       "\n",
       "[108 rows x 7 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_testing['actual'] = df_testing_actual\n",
    "df_testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "plt.scatter(df_testing['predicted_st'], df_testing['actual'], color='green')\n",
    "\n",
    "m, b = np.polyfit(df_testing['predicted_st'], df_testing['actual'], 1)\n",
    "plt.plot(df_testing['predicted_st'], m*df_testing['predicted_st'] + b)\n",
    "\n",
    "plt.title('Predicted Vs Actual', fontsize=14)\n",
    "plt.xlabel('Predicted', fontsize=14)\n",
    "plt.ylabel('Actual', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "2be5faf79681da6f2a61fdfdd5405d65d042280f7fba6178067603e3a2925119"
  },
  "kernelspec": {
   "display_name": "Python 3.10.0 64-bit",
   "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": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}