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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+
+
+with open('model_lin_reg.pkl', 'rb') as file_1:
+  model_lin_reg= joblib.load(file_1)
+
+with open('model_scaler.pkl', 'rb') as file_2:
+  model_scaler=joblib.load(file_2)
+
+with open('model_encoder.pkl', 'rb') as file_3:
+  model_encoder= joblib.load(file_3)
+
+with open('list_num_cols.txt', 'rb') as file_4:
+  num_cols= joblib.load(file_4)
+
+with open('list_cat_cols.txt', 'rb') as file_5:
+  cat_cols= joblib.load(file_5)
+
+
+hour = st.slider('Masukan Jam : ',0, 24)
+distance = st.number_input('Masukan Jarak dalam Mile : ')
+cab_type = st.radio('Lyft/Uber : ',('Lyft', 'Uber'))
+name = st.selectbox('Masukan Jenis Layanan : ',('Shared', 'Lux', 'UberPool', 'Lyft XL', 'Black', 'Lyft', 'UberXL',
+                      'UberX', 'WAV', 'Lux Black', 'Black SUV', 'Lux Black XL'))
+destination = st.selectbox('Masukan Tujuan : ',('North Station', 'Fenway', 'West End', 'Back Bay',
+                          'Haymarket Square', 'Theatre District', 'South Station',
+                          'Northeastern University', 'North End', 'Financial District',
+                          'Beacon Hill', 'Boston University'))
+icon = st.selectbox('Masukan Cuaca Sekarang : ',(' cloudy ', ' partly-cloudy-day ', ' rain ', ' clear-night ',
+                     ' partly-cloudy-night ', ' fog ', ' clear-day '))
+
+if st.button('Predict'):
+    data_inf = pd.DataFrame({'hour' : hour, 'distance' : distance, 'cab_type' : cab_type, 'name' : name, 'destination' : destination, 'icon' : icon})
+    data_inf_scaled = model_scaler.transform(data_inf[num_cols])
+    data_inf_encoded1 = model_encoder.transform(data_inf[cat_cols])
+
+    data_inf_fix = np.concatenate([data_inf_scaled,data_inf_encoded1],axis=1)
+    hasil = model_lin_reg.predict(data_inf_fix)
+    hasil

h8dsft_P1G1_Inference_KeziaIntan.ipynb

@@ -0,0 +1,610 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Inference"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "import the library that we use"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import joblib"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Model"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "load the model that we saved"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load all files\n",
+    "\n",
+    "with open('model_lin_reg.pkl', 'rb') as file_1:\n",
+    "  model_lin_reg= joblib.load(file_1)\n",
+    "\n",
+    "with open('model_scaler.pkl', 'rb') as file_2:\n",
+    "  model_scaler=joblib.load(file_2)\n",
+    "\n",
+    "with open('model_encoder.pkl', 'rb') as file_3:\n",
+    "  model_encoder= joblib.load(file_3)\n",
+    "\n",
+    "with open('list_num_cols.txt', 'rb') as file_4:\n",
+    "  num_cols= joblib.load(file_4)\n",
+    "\n",
+    "with open('list_cat_cols.txt', 'rb') as file_5:\n",
+    "  cat_cols= joblib.load(file_5)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get Data for Model Inference"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "getting random sample for inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>hour</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>cab_type</th>\n",
+       "      <th>name</th>\n",
+       "      <th>destination</th>\n",
+       "      <th>icon</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.316562</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>Lux Black XL</td>\n",
+       "      <td>South Station</td>\n",
+       "      <td>clear-day</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>23</td>\n",
+       "      <td>3.954514</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>Black SUV</td>\n",
+       "      <td>South Station</td>\n",
+       "      <td>clear-night</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12</td>\n",
+       "      <td>6.066497</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>UberPool</td>\n",
+       "      <td>Theatre District</td>\n",
+       "      <td>partly-cloudy-night</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2.545956</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>UberPool</td>\n",
+       "      <td>Northeastern University</td>\n",
+       "      <td>clear-night</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1.456490</td>\n",
+       "      <td>Lyft</td>\n",
+       "      <td>Lux Black XL</td>\n",
+       "      <td>Theatre District</td>\n",
+       "      <td>partly-cloudy-night</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>16</td>\n",
+       "      <td>5.954119</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>UberX</td>\n",
+       "      <td>Boston University</td>\n",
+       "      <td>rain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.345548</td>\n",
+       "      <td>Lyft</td>\n",
+       "      <td>Lux Black</td>\n",
+       "      <td>North Station</td>\n",
+       "      <td>fog</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>17</td>\n",
+       "      <td>5.626220</td>\n",
+       "      <td>Lyft</td>\n",
+       "      <td>Lux Black XL</td>\n",
+       "      <td>North Station</td>\n",
+       "      <td>rain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>12</td>\n",
+       "      <td>1.640055</td>\n",
+       "      <td>Lyft</td>\n",
+       "      <td>Lux</td>\n",
+       "      <td>Boston University</td>\n",
+       "      <td>cloudy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>19</td>\n",
+       "      <td>7.819139</td>\n",
+       "      <td>Lyft</td>\n",
+       "      <td>Black SUV</td>\n",
+       "      <td>South Station</td>\n",
+       "      <td>fog</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>18</td>\n",
+       "      <td>7.116573</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>Black SUV</td>\n",
+       "      <td>North End</td>\n",
+       "      <td>clear-day</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>23</td>\n",
+       "      <td>4.357456</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>Shared</td>\n",
+       "      <td>West End</td>\n",
+       "      <td>clear-day</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>4</td>\n",
+       "      <td>3.194852</td>\n",
+       "      <td>Lyft</td>\n",
+       "      <td>Lyft</td>\n",
+       "      <td>North End</td>\n",
+       "      <td>rain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1.256166</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>Lux Black XL</td>\n",
+       "      <td>Financial District</td>\n",
+       "      <td>rain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>19</td>\n",
+       "      <td>2.904539</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>Lux</td>\n",
+       "      <td>Beacon Hill</td>\n",
+       "      <td>rain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>13</td>\n",
+       "      <td>6.218676</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>Lyft XL</td>\n",
+       "      <td>South Station</td>\n",
+       "      <td>fog</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>7.757917</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>Lyft</td>\n",
+       "      <td>North End</td>\n",
+       "      <td>clear-day</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1.756099</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>Lyft XL</td>\n",
+       "      <td>Financial District</td>\n",
+       "      <td>rain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>6.055154</td>\n",
+       "      <td>Lyft</td>\n",
+       "      <td>Black SUV</td>\n",
+       "      <td>North Station</td>\n",
+       "      <td>cloudy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>2.920731</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>UberX</td>\n",
+       "      <td>North Station</td>\n",
+       "      <td>cloudy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>17</td>\n",
+       "      <td>1.809670</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>Lyft</td>\n",
+       "      <td>Haymarket Square</td>\n",
+       "      <td>rain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>10</td>\n",
+       "      <td>2.911586</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>Lyft XL</td>\n",
+       "      <td>South Station</td>\n",
+       "      <td>clear-day</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>15</td>\n",
+       "      <td>4.871693</td>\n",
+       "      <td>Lyft</td>\n",
+       "      <td>Lux Black</td>\n",
+       "      <td>Financial District</td>\n",
+       "      <td>partly-cloudy-night</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>17</td>\n",
+       "      <td>2.090028</td>\n",
+       "      <td>Lyft</td>\n",
+       "      <td>Lux Black XL</td>\n",
+       "      <td>Theatre District</td>\n",
+       "      <td>partly-cloudy-day</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.646278</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>Shared</td>\n",
+       "      <td>Beacon Hill</td>\n",
+       "      <td>rain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>2</td>\n",
+       "      <td>7.818779</td>\n",
+       "      <td>Lyft</td>\n",
+       "      <td>UberX</td>\n",
+       "      <td>Northeastern University</td>\n",
+       "      <td>cloudy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>17</td>\n",
+       "      <td>5.054022</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>WAV</td>\n",
+       "      <td>Theatre District</td>\n",
+       "      <td>partly-cloudy-day</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>17</td>\n",
+       "      <td>2.589397</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>Lux Black</td>\n",
+       "      <td>Back Bay</td>\n",
+       "      <td>cloudy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.619987</td>\n",
+       "      <td>Lyft</td>\n",
+       "      <td>Lux Black XL</td>\n",
+       "      <td>Boston University</td>\n",
+       "      <td>clear-night</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>11</td>\n",
+       "      <td>2.514690</td>\n",
+       "      <td>Uber</td>\n",
+       "      <td>Black SUV</td>\n",
+       "      <td>Fenway</td>\n",
+       "      <td>rain</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    hour  distance cab_type          name              destination  \\\n",
+       "0      1  0.316562     Uber  Lux Black XL            South Station   \n",
+       "1     23  3.954514     Uber     Black SUV            South Station   \n",
+       "2     12  6.066497     Uber      UberPool         Theatre District   \n",
+       "3      1  2.545956     Uber      UberPool  Northeastern University   \n",
+       "4      8  1.456490     Lyft  Lux Black XL         Theatre District   \n",
+       "5     16  5.954119     Uber         UberX        Boston University   \n",
+       "6      2  2.345548     Lyft     Lux Black            North Station   \n",
+       "7     17  5.626220     Lyft  Lux Black XL            North Station   \n",
+       "8     12  1.640055     Lyft           Lux        Boston University   \n",
+       "9     19  7.819139     Lyft     Black SUV            South Station   \n",
+       "10    18  7.116573     Uber     Black SUV                North End   \n",
+       "11    23  4.357456     Uber        Shared                 West End   \n",
+       "12     4  3.194852     Lyft          Lyft                North End   \n",
+       "13    10  1.256166     Uber  Lux Black XL       Financial District   \n",
+       "14    19  2.904539     Uber           Lux              Beacon Hill   \n",
+       "15    13  6.218676     Uber       Lyft XL            South Station   \n",
+       "16    16  7.757917     Uber          Lyft                North End   \n",
+       "17     7  1.756099     Uber       Lyft XL       Financial District   \n",
+       "18    19  6.055154     Lyft     Black SUV            North Station   \n",
+       "19    20  2.920731     Uber         UberX            North Station   \n",
+       "20    17  1.809670     Uber          Lyft         Haymarket Square   \n",
+       "21    10  2.911586     Uber       Lyft XL            South Station   \n",
+       "22    15  4.871693     Lyft     Lux Black       Financial District   \n",
+       "23    17  2.090028     Lyft  Lux Black XL         Theatre District   \n",
+       "24    14  0.646278     Uber        Shared              Beacon Hill   \n",
+       "25     2  7.818779     Lyft         UberX  Northeastern University   \n",
+       "26    17  5.054022     Uber           WAV         Theatre District   \n",
+       "27    17  2.589397     Uber     Lux Black                 Back Bay   \n",
+       "28     3  0.619987     Lyft  Lux Black XL        Boston University   \n",
+       "29    11  2.514690     Uber     Black SUV                   Fenway   \n",
+       "\n",
+       "                     icon  \n",
+       "0              clear-day   \n",
+       "1            clear-night   \n",
+       "2    partly-cloudy-night   \n",
+       "3            clear-night   \n",
+       "4    partly-cloudy-night   \n",
+       "5                   rain   \n",
+       "6                    fog   \n",
+       "7                   rain   \n",
+       "8                 cloudy   \n",
+       "9                    fog   \n",
+       "10             clear-day   \n",
+       "11             clear-day   \n",
+       "12                  rain   \n",
+       "13                  rain   \n",
+       "14                  rain   \n",
+       "15                   fog   \n",
+       "16             clear-day   \n",
+       "17                  rain   \n",
+       "18                cloudy   \n",
+       "19                cloudy   \n",
+       "20                  rain   \n",
+       "21             clear-day   \n",
+       "22   partly-cloudy-night   \n",
+       "23     partly-cloudy-day   \n",
+       "24                  rain   \n",
+       "25                cloudy   \n",
+       "26     partly-cloudy-day   \n",
+       "27                cloudy   \n",
+       "28           clear-night   \n",
+       "29                  rain   "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# generate random data\n",
+    "random = np.random.default_rng(seed=2895)\n",
+    "hour = random.integers(0, 24, size=30)\n",
+    "distance = random.uniform(0.02, 7.86, size=30)\n",
+    "cab_type = random.choice(['Lyft', 'Uber'], size=30)\n",
+    "name = random.choice(['Shared', 'Lux', 'UberPool', 'Lyft XL', 'Black', 'Lyft', 'UberXL',\n",
+    "                      'UberX', 'WAV', 'Lux Black', 'Black SUV', 'Lux Black XL'], size=30)\n",
+    "destination = random.choice(['North Station', 'Fenway', 'West End', 'Back Bay',\n",
+    "                          'Haymarket Square', 'Theatre District', 'South Station',\n",
+    "                          'Northeastern University', 'North End', 'Financial District',\n",
+    "                          'Beacon Hill', 'Boston University'], size=30)\n",
+    "icon = random.choice([' cloudy ', ' partly-cloudy-day ', ' rain ', ' clear-night ',\n",
+    "                     ' partly-cloudy-night ', ' fog ', ' clear-day '], size=30)\n",
+    "data_inf = pd.DataFrame({'hour' : hour, 'distance' : distance, 'cab_type' : cab_type, 'name' : name, 'destination' : destination, 'icon' : icon})\n",
+    "data_inf"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Inferencing"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "define the numerical and categorical columns from data inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#split the data\n",
+    "data_inf_num = data_inf[num_cols]\n",
+    "data_inf_cat = data_inf[cat_cols]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "scaling and encoding the data inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_inf_scale = model_scaler.transform(data_inf_num)\n",
+    "data_inf_encode = model_encoder.transform(data_inf_cat)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "concate the data that we scaled and encoded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#concate the data\n",
+    "data_inf_final = np.concatenate([data_inf_scale,data_inf_encode], axis=1)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "now predict the inference data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([13.76511146, 19.99589464,  9.31714736,  8.03002182, 12.48483861,\n",
+       "        7.64271504, 14.43800148, 13.99893239, 15.81599645, 19.69485696,\n",
+       "       21.13920469, 10.32030018, 11.48120646, 14.12233045, 17.99273736,\n",
+       "       12.63970579, 14.82575042, 11.03057652, 19.0527246 ,  6.53215817,\n",
+       "       12.68647047, 11.43272228, 15.35915507, 12.71168744,  8.99184479,\n",
+       "        6.60013128,  4.03796725, 16.23093043, 12.17054192, 19.48791409])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#predict\n",
+    "y_pred_inf= model_lin_reg.predict(data_inf_final)\n",
+    "y_pred_inf"
+   ]
+  }
+ ],
+ "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": "3.9.5 (tags/v3.9.5:0a7dcbd, May  3 2021, 17:27:52) [MSC v.1928 64 bit (AMD64)]"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "e31aef8222fb7c235d2ed8e74ce17e973738f89b37261e7466b7a63a6dfb1214"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

list_cat_columns.txt

@@ -0,0 +1 @@
+['icon', 'name', 'cab_type']

list_num_columns.txt

@@ -0,0 +1 @@
+['distance']

model_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e53d92e2853797ce168048094e46bf055852cc761b7d54f8b1d342d5e970169
+size 1761

model_lin_reg.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:791885b36c69edf45f5cb102ff477bc9a3396ffe54d5135609fdc25c7f084723
+size 600

model_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d0c162d1831cdb44de404b4d712a190bbdb81f8902ee576d1c001bdf0ad6e704
+size 1023