sendy-logistics-riders-pkl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d54c86fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "579b3160",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "riders = pd.read_csv(\"data/Riders.csv\")\n",
    "riders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "71f90dfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order_No</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_19248</td>\n",
       "      <td>567.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Order_No_12736</td>\n",
       "      <td>4903.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Order_No_768</td>\n",
       "      <td>5649.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Order_No_15332</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Order_No_21373</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Order_No  Time from Pickup to Arrival\n",
       "0  Order_No_19248                        567.0\n",
       "1  Order_No_12736                       4903.0\n",
       "2    Order_No_768                       5649.0\n",
       "3  Order_No_15332                          NaN\n",
       "4  Order_No_21373                          NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = pd.read_csv(\"data/SampleSubmission.csv\")\n",
    "sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c16d1dd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Pickup - Weekday (Mo = 1)</th>\n",
       "      <th>Pickup - 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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Order_No_19248</td>\n",
       "      <td>User_Id_3355</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>4:44:10 PM</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5:06:47 PM</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.333275</td>\n",
       "      <td>36.870815</td>\n",
       "      <td>-1.305249</td>\n",
       "      <td>36.822390</td>\n",
       "      <td>Rider_Id_192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Order_No_12736</td>\n",
       "      <td>User_Id_3647</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>12:57:35 PM</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1:25:37 PM</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.272639</td>\n",
       "      <td>36.794723</td>\n",
       "      <td>-1.277007</td>\n",
       "      <td>36.823907</td>\n",
       "      <td>Rider_Id_868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Order_No_768</td>\n",
       "      <td>User_Id_2154</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>11:08:14 AM</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>11:57:54 AM</td>\n",
       "      <td>5</td>\n",
       "      <td>22.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.290894</td>\n",
       "      <td>36.822971</td>\n",
       "      <td>-1.276574</td>\n",
       "      <td>36.851365</td>\n",
       "      <td>Rider_Id_26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Order_No_15332</td>\n",
       "      <td>User_Id_2910</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1:51:35 PM</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2:16:52 PM</td>\n",
       "      <td>5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.290503</td>\n",
       "      <td>36.809646</td>\n",
       "      <td>-1.303382</td>\n",
       "      <td>36.790658</td>\n",
       "      <td>Rider_Id_685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Order_No_21373</td>\n",
       "      <td>User_Id_1205</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>11:30:28 AM</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>11:56:04 AM</td>\n",
       "      <td>6</td>\n",
       "      <td>24.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.281081</td>\n",
       "      <td>36.814423</td>\n",
       "      <td>-1.266467</td>\n",
       "      <td>36.792161</td>\n",
       "      <td>Rider_Id_858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
       "0  Order_No_19248  User_Id_3355         Bike              3   \n",
       "1  Order_No_12736  User_Id_3647         Bike              3   \n",
       "2    Order_No_768  User_Id_2154         Bike              3   \n",
       "3  Order_No_15332  User_Id_2910         Bike              3   \n",
       "4  Order_No_21373  User_Id_1205         Bike              3   \n",
       "\n",
       "  Personal or Business  Placement - Day of Month  \\\n",
       "0             Business                        27   \n",
       "1             Business                        17   \n",
       "2             Business                        27   \n",
       "3             Business                        17   \n",
       "4             Business                        11   \n",
       "\n",
       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
       "0                             3       4:44:10 PM                           27   \n",
       "1                             5      12:57:35 PM                           17   \n",
       "2                             4      11:08:14 AM                           27   \n",
       "3                             1       1:51:35 PM                           17   \n",
       "4                             2      11:30:28 AM                           11   \n",
       "\n",
       "   Confirmation - Weekday (Mo = 1)  ... Pickup - Weekday (Mo = 1)  \\\n",
       "0                                3  ...                         3   \n",
       "1                                5  ...                         5   \n",
       "2                                4  ...                         4   \n",
       "3                                1  ...                         1   \n",
       "4                                2  ...                         2   \n",
       "\n",
       "   Pickup - Time  Distance (KM) Temperature  Precipitation in millimeters  \\\n",
       "0     5:06:47 PM              8         NaN                           NaN   \n",
       "1     1:25:37 PM              5         NaN                           NaN   \n",
       "2    11:57:54 AM              5        22.8                           NaN   \n",
       "3     2:16:52 PM              5        24.5                           NaN   \n",
       "4    11:56:04 AM              6        24.4                           NaN   \n",
       "\n",
       "   Pickup Lat Pickup Long  Destination Lat  Destination Long      Rider Id  \n",
       "0   -1.333275   36.870815        -1.305249         36.822390  Rider_Id_192  \n",
       "1   -1.272639   36.794723        -1.277007         36.823907  Rider_Id_868  \n",
       "2   -1.290894   36.822971        -1.276574         36.851365   Rider_Id_26  \n",
       "3   -1.290503   36.809646        -1.303382         36.790658  Rider_Id_685  \n",
       "4   -1.281081   36.814423        -1.266467         36.792161  Rider_Id_858  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv(\"data/Test.csv\")\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea24d76e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(960, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "riders.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "86818283",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop duplicates (no duplicates in data)\n",
    "riders = riders.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "63a14c7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename columns\n",
    "riders.rename(columns={\"Rider Id\":\"rider_id\"}, inplace=True)\n",
    "riders.rename(columns={\"No_Of_Orders\":\"no_of_orders\"}, inplace=True)\n",
    "riders.rename(columns={\"Age\":\"age\"}, inplace=True)\n",
    "riders.rename(columns={\"Average_Rating\":\"average_rating\"}, inplace=True)\n",
    "riders.rename(columns={\"No_of_Ratings\":\"no_of_ratings\"}, inplace=True)\n",
    "riders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74e22736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rider_id           object\n",
       "no_of_orders        int64\n",
       "age                 int64\n",
       "average_rating    float64\n",
       "no_of_ratings       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "riders.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c818f5ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reformat data under Rider_Id to only ID numbers\n",
    "riders.rider_id = riders[\"rider_id\"].str.split(\"Rider_Id_\").str.get(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c875826",
   "metadata": {},
   "outputs": [
    {
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       "    <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>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>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>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>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>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      396          2946  2298            14.0           1159\n",
       "1      479           360   951            13.5            176\n",
       "2      648          1746   821            14.3            466\n",
       "3      753           314   980            12.5             75\n",
       "4      335           536  1113            13.7            156"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "riders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "22c05eee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change Rider_Id type to integer\n",
    "riders.loc[:, \"rider_id\"] = riders.rider_id.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b4f2c927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(riders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "83838e24",
   "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>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>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>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>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>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>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       396          2946  2298            14.0           1159\n",
       "1       479           360   951            13.5            176\n",
       "2       648          1746   821            14.3            466\n",
       "3       753           314   980            12.5             75\n",
       "4       335           536  1113            13.7            156"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no columns to be dropped\n",
    "# no columns to be filled\n",
    "riders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e1cb9b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rider_id            int64\n",
       "no_of_orders        int64\n",
       "age                 int64\n",
       "average_rating    float64\n",
       "no_of_ratings       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "riders.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8cf6d178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='average_rating', ylabel='no_of_orders'>"
      ]
     },
     "execution_count": 15,
     "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": [
    "# Relationship between average rating and number of orders\n",
    "sns.scatterplot(x=\"average_rating\", y=\"no_of_orders\", data=riders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8cda7ba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='no_of_orders', ylabel='no_of_ratings'>"
      ]
     },
     "execution_count": 16,
     "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": [
    "# Relationship between number of orders and number of ratings\n",
    "sns.scatterplot(x=\"no_of_orders\", y=\"no_of_ratings\", data=riders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ac589ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='no_of_orders'>"
      ]
     },
     "execution_count": 17,
     "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": [
    "# Relationship between age and number of orders\n",
    "sns.scatterplot(x=\"age\", y=\"no_of_orders\", data=riders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7eef1c1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nThe above three graphs show the relations between some of the more important columns.\\n\\nIn more detail:\\nThe following shows the correlation between the columns against each other.\\nie. strong/weak and positive/negative\\n\\nLastly, the cleaned data will be saved as a new pickle file.\\n'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NOTE:\n",
    "'''\n",
    "The above three graphs show the relations between some of the more important columns.\n",
    "\n",
    "In more detail:\n",
    "The following shows the correlation between the columns against each other.\n",
    "ie. strong/weak and positive/negative\n",
    "\n",
    "Lastly, the cleaned data will be saved as a new pickle file.\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e5e8cf50",
   "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>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>rider_id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.021960</td>\n",
       "      <td>-0.024707</td>\n",
       "      <td>-0.017260</td>\n",
       "      <td>-0.011321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_orders</th>\n",
       "      <td>-0.021960</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.710116</td>\n",
       "      <td>0.199810</td>\n",
       "      <td>0.836781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.024707</td>\n",
       "      <td>0.710116</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.188442</td>\n",
       "      <td>0.635270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_rating</th>\n",
       "      <td>-0.017260</td>\n",
       "      <td>0.199810</td>\n",
       "      <td>0.188442</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.181049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_ratings</th>\n",
       "      <td>-0.011321</td>\n",
       "      <td>0.836781</td>\n",
       "      <td>0.635270</td>\n",
       "      <td>0.181049</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                rider_id  no_of_orders       age  average_rating  \\\n",
       "rider_id        1.000000     -0.021960 -0.024707       -0.017260   \n",
       "no_of_orders   -0.021960      1.000000  0.710116        0.199810   \n",
       "age            -0.024707      0.710116  1.000000        0.188442   \n",
       "average_rating -0.017260      0.199810  0.188442        1.000000   \n",
       "no_of_ratings  -0.011321      0.836781  0.635270        0.181049   \n",
       "\n",
       "                no_of_ratings  \n",
       "rider_id            -0.011321  \n",
       "no_of_orders         0.836781  \n",
       "age                  0.635270  \n",
       "average_rating       0.181049  \n",
       "no_of_ratings        1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_matrix = riders.corr()\n",
    "correlation_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "32520349",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nStrong Positive:\\n- no_of_orders <--> age\\n- no_of_orders <--> no_of_ratings\\n\\nModerate Positive:\\n- age <--> no_of_ratings\\n\\nWeak Positive:\\n- no_of_orders <--> average_rating\\n- age <--> average_rating\\n- average_rating <--> no_of_ratings\\n\\nWeak Negative:\\n- rider_id <--> no_of_orders\\n- rider_id <--> age\\n- rider_id <--> average_rating\\n- rider_id <--> no_of_ratings\\n\\n\\nNo strong-negative, moderate-negative correlations\\n'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary:\n",
    "'''\n",
    "Strong Positive:\n",
    "- no_of_orders <--> age\n",
    "- no_of_orders <--> no_of_ratings\n",
    "\n",
    "Moderate Positive:\n",
    "- age <--> no_of_ratings\n",
    "\n",
    "Weak Positive:\n",
    "- no_of_orders <--> average_rating\n",
    "- age <--> average_rating\n",
    "- average_rating <--> no_of_ratings\n",
    "\n",
    "Weak Negative:\n",
    "- rider_id <--> no_of_orders\n",
    "- rider_id <--> age\n",
    "- rider_id <--> average_rating\n",
    "- rider_id <--> no_of_ratings\n",
    "\n",
    "\n",
    "No strong-negative, moderate-negative correlations\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "42f422d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "riders.to_pickle(\"clean-data/riders.pkl\")"
   ]
  }
 ],
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