hotel_cancelations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "54a61455",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "import category_encoders as ce\n",
    "\n",
    "from numpy import mean\n",
    "from numpy import std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a11b8d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "using the perceptron alg to predict hotel cancellations\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "73fd7958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119390, 32)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotels_df = pd.read_csv('hotel.csv')\n",
    "hotels_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "37189d48",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>hotel</th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>...</th>\n",
       "      <th>deposit_type</th>\n",
       "      <th>agent</th>\n",
       "      <th>company</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>adr</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>reservation_status</th>\n",
       "      <th>reservation_status_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>342</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>737</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>304.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          hotel  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0  Resort Hotel            0        342               2015               July   \n",
       "1  Resort Hotel            0        737               2015               July   \n",
       "2  Resort Hotel            0          7               2015               July   \n",
       "3  Resort Hotel            0         13               2015               July   \n",
       "4  Resort Hotel            0         14               2015               July   \n",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                        27                          1   \n",
       "1                        27                          1   \n",
       "2                        27                          1   \n",
       "3                        27                          1   \n",
       "4                        27                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  deposit_type  \\\n",
       "0                        0                     0       2  ...    No Deposit   \n",
       "1                        0                     0       2  ...    No Deposit   \n",
       "2                        0                     1       1  ...    No Deposit   \n",
       "3                        0                     1       1  ...    No Deposit   \n",
       "4                        0                     2       2  ...    No Deposit   \n",
       "\n",
       "   agent company days_in_waiting_list customer_type   adr  \\\n",
       "0    NaN     NaN                    0     Transient   0.0   \n",
       "1    NaN     NaN                    0     Transient   0.0   \n",
       "2    NaN     NaN                    0     Transient  75.0   \n",
       "3  304.0     NaN                    0     Transient  75.0   \n",
       "4  240.0     NaN                    0     Transient  98.0   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  reservation_status  \\\n",
       "0                            0                          0           Check-Out   \n",
       "1                            0                          0           Check-Out   \n",
       "2                            0                          0           Check-Out   \n",
       "3                            0                          0           Check-Out   \n",
       "4                            0                          1           Check-Out   \n",
       "\n",
       "  reservation_status_date  \n",
       "0              2015-07-01  \n",
       "1              2015-07-01  \n",
       "2              2015-07-02  \n",
       "3              2015-07-02  \n",
       "4              2015-07-03  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotels_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "50c4293a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hotel\n",
      "is_canceled\n",
      "lead_time\n",
      "arrival_date_year\n",
      "arrival_date_month\n",
      "arrival_date_week_number\n",
      "arrival_date_day_of_month\n",
      "stays_in_weekend_nights\n",
      "stays_in_week_nights\n",
      "adults\n",
      "children\n",
      "babies\n",
      "meal\n",
      "country\n",
      "market_segment\n",
      "distribution_channel\n",
      "is_repeated_guest\n",
      "previous_cancellations\n",
      "previous_bookings_not_canceled\n",
      "reserved_room_type\n",
      "assigned_room_type\n",
      "booking_changes\n",
      "deposit_type\n",
      "agent\n",
      "company\n",
      "days_in_waiting_list\n",
      "customer_type\n",
      "adr\n",
      "required_car_parking_spaces\n",
      "total_of_special_requests\n",
      "reservation_status\n",
      "reservation_status_date\n"
     ]
    }
   ],
   "source": [
    "hotels_df['hotel'].value_counts()\n",
    "for col in hotels_df.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "706b1a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "hotels_df = hotels_df.drop(['meal','distribution_channel','market_segment','company','assigned_room_type','reserved_room_type', 'reservation_status_date'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "997cc318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102894, 25)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotels_df = hotels_df.dropna()\n",
    "hotels_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "61415b62",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel_0</th>\n",
       "      <th>hotel_1</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month_0</th>\n",
       "      <th>arrival_date_month_1</th>\n",
       "      <th>arrival_date_month_2</th>\n",
       "      <th>arrival_date_month_3</th>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>...</th>\n",
       "      <th>agent</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type_0</th>\n",
       "      <th>customer_type_1</th>\n",
       "      <th>customer_type_2</th>\n",
       "      <th>adr</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>reservation_status_0</th>\n",
       "      <th>reservation_status_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>304.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>303.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   hotel_0  hotel_1  lead_time  arrival_date_year  arrival_date_month_0  \\\n",
       "3        0        1         13               2015                     0   \n",
       "4        0        1         14               2015                     0   \n",
       "5        0        1         14               2015                     0   \n",
       "7        0        1          9               2015                     0   \n",
       "8        0        1         85               2015                     0   \n",
       "\n",
       "   arrival_date_month_1  arrival_date_month_2  arrival_date_month_3  \\\n",
       "3                     0                     0                     1   \n",
       "4                     0                     0                     1   \n",
       "5                     0                     0                     1   \n",
       "7                     0                     0                     1   \n",
       "8                     0                     0                     1   \n",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  ...  agent  \\\n",
       "3                        27                          1  ...  304.0   \n",
       "4                        27                          1  ...  240.0   \n",
       "5                        27                          1  ...  240.0   \n",
       "7                        27                          1  ...  303.0   \n",
       "8                        27                          1  ...  240.0   \n",
       "\n",
       "   days_in_waiting_list  customer_type_0  customer_type_1  customer_type_2  \\\n",
       "3                     0                0                0                1   \n",
       "4                     0                0                0                1   \n",
       "5                     0                0                0                1   \n",
       "7                     0                0                0                1   \n",
       "8                     0                0                0                1   \n",
       "\n",
       "     adr  required_car_parking_spaces  total_of_special_requests  \\\n",
       "3   75.0                            0                          0   \n",
       "4   98.0                            0                          1   \n",
       "5   98.0                            0                          1   \n",
       "7  103.0                            0                          1   \n",
       "8   82.0                            0                          1   \n",
       "\n",
       "   reservation_status_0  reservation_status_1  \n",
       "3                     0                     1  \n",
       "4                     0                     1  \n",
       "5                     0                     1  \n",
       "7                     0                     1  \n",
       "8                     1                     0  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = hotels_df.copy()\n",
    "y = X.pop('is_canceled')\n",
    "\n",
    "\n",
    "#Create object for binary encoding\n",
    "encoder= ce.BinaryEncoder(cols=['arrival_date_month'],return_df=True)\n",
    "#Fit and Transform Data \n",
    "X = encoder.fit_transform(X) \n",
    "#Create object for binary encoding\n",
    "encoder= ce.BinaryEncoder(cols=['hotel'],return_df=True)\n",
    "#Fit and Transform Data \n",
    "X = encoder.fit_transform(X)\n",
    "#Create object for binary encoding\n",
    "encoder= ce.BinaryEncoder(cols=['deposit_type'],return_df=True)\n",
    "#Fit and Transform Data \n",
    "X = encoder.fit_transform(X)\n",
    "#Create object for binary encoding\n",
    "encoder= ce.BinaryEncoder(cols=['customer_type'],return_df=True)\n",
    "#Fit and Transform Data \n",
    "X = encoder.fit_transform(X)\n",
    "#Create object for binary encoding\n",
    "encoder= ce.BinaryEncoder(cols=['reservation_status'],return_df=True)\n",
    "#Fit and Transform Data \n",
    "X = encoder.fit_transform(X)\n",
    "encoder= ce.BinaryEncoder(cols=['country'],return_df=True)\n",
    "#Fit and Transform Data \n",
    "X = encoder.fit_transform(X)\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0356c437",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n#Create object for binary encoding\\nencoder= ce.BinaryEncoder(cols=['hotel'],return_df=True)\\n#Fit and Transform Data \\nX = encoder.fit_transform(X)\\n\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "~this is code for explicitly mapping the vals~\n",
    "X['arrival_date_month'] = \\\n",
    "    X['arrival_date_month'].map(\n",
    "        {'January':1, 'February': 2, 'March':3,\n",
    "         'April':4, 'May':5, 'June':6, 'July':7,\n",
    "         'August':8, 'September':9, 'October':10,\n",
    "         'November':11, 'December':12}\n",
    "    )\n",
    "\n",
    "X['hotel'] = \\\n",
    "    X['hotel'].map(\n",
    "          {'City Hotel':1, 'Resort Hotel':2}\n",
    ")\n",
    "X['deposit_type'] = \\\n",
    "    X['deposit_type'].map(\n",
    "          {'No Deposit':1, 'Non Refund':2, 'Refundable':3}\n",
    ")\n",
    "X['customer_type'] = \\\n",
    "    X['customer_type'].map(\n",
    "          {'Transient':1, 'Transient-Party':2, 'Contract':3, 'Group':4}\n",
    ")\n",
    "X['reservation_status'] = \\\n",
    "    X['reservation_status'].map(\n",
    "          {'Check-Out':1, 'Canceled':2, 'No-Show':3}\n",
    ")\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "7d6c2739",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(102894, 39) (102894,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "d7601702",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "f359c7d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc = StandardScaler()\n",
    "sc.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "7003fb2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the scaler to the X training data\n",
    "X_train_std = sc.transform(X_train)\n",
    "\n",
    "# Apply the SAME scaler to the X test data\n",
    "X_test_std = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "8ac0a52f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Perceptron(eta0=1, max_iter=50)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a perceptron object with the parameters: 40 iterations (epochs) over the data, and a learning rate of 0.1\n",
    "ppn = Perceptron(max_iter = 50, eta0=0.1, random_state=0) #need to figure out why my accuracy is....\n",
    "\n",
    "# Train the perceptron\n",
    "ppn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "ca507eb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lucie\\Miniconda3\\envs\\mlvenv\\lib\\site-packages\\sklearn\\base.py:441: UserWarning: X does not have valid feature names, but Perceptron was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Apply the trained perceptron on the X data to make predicts for the y test data\n",
    "y_pred = ppn.predict(X_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "b65e1106",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "cf29e205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110563    0\n",
       "81449     0\n",
       "7416      1\n",
       "116523    0\n",
       "111808    0\n",
       "         ..\n",
       "96272     0\n",
       "73712     1\n",
       "49593     1\n",
       "70109     1\n",
       "80884     0\n",
       "Name: is_canceled, Length: 30869, dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "139ccb13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.00\n"
     ]
    }
   ],
   "source": [
    "# View the accuracy of the model, which is: 1 - (observations predicted wrong / total observations)\n",
    "print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "eb144ff9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Accuracy: 0.846 (0.193)\n"
     ]
    }
   ],
   "source": [
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "# evaluate model\n",
    "scores = cross_val_score(ppn, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n",
    "# summarize result\n",
    "print('Mean Accuracy: %.3f (%.3f)' % (mean(scores), std(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b67ddea",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "sources: https://chrisalbon.com/code/machine_learning/basics/perceptron_in_scikit-learn/\n",
    "https://www.kaggle.com/lucienmensah/exercise-binary-classification/edit\n",
    "https://machinelearningmastery.com/perceptron-algorithm-for-classification-in-python/\n",
    "'''"
   ]
  }
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