random_forest_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0c968be9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial imports.\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "db01ee22",
   "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>Booking_ID</th>\n",
       "      <th>no_of_adults</th>\n",
       "      <th>no_of_children</th>\n",
       "      <th>no_of_weekend_nights</th>\n",
       "      <th>no_of_week_nights</th>\n",
       "      <th>required_car_parking_space</th>\n",
       "      <th>room_type_reserved</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_year</th>\n",
       "      <th>arrival_month</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>market_segment_type</th>\n",
       "      <th>repeated_guest</th>\n",
       "      <th>no_of_previous_cancellations</th>\n",
       "      <th>no_of_previous_bookings_not_canceled</th>\n",
       "      <th>avg_price_per_room</th>\n",
       "      <th>no_of_special_requests</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>total_nights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>INN00001</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Room_Type 1</td>\n",
       "      <td>224</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>Offline</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Not_Canceled</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>INN00002</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Room_Type 1</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>Online</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>106.68</td>\n",
       "      <td>1</td>\n",
       "      <td>Not_Canceled</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INN00003</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Room_Type 1</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>Online</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>INN00004</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Room_Type 1</td>\n",
       "      <td>211</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>Online</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>INN00005</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Room_Type 1</td>\n",
       "      <td>48</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>Online</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.50</td>\n",
       "      <td>0</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Booking_ID  no_of_adults  no_of_children  no_of_weekend_nights  \\\n",
       "0   INN00001             2               0                     1   \n",
       "1   INN00002             2               0                     2   \n",
       "2   INN00003             1               0                     2   \n",
       "3   INN00004             2               0                     0   \n",
       "4   INN00005             2               0                     1   \n",
       "\n",
       "   no_of_week_nights  required_car_parking_space room_type_reserved  \\\n",
       "0                  2                           0        Room_Type 1   \n",
       "1                  3                           0        Room_Type 1   \n",
       "2                  1                           0        Room_Type 1   \n",
       "3                  2                           0        Room_Type 1   \n",
       "4                  1                           0        Room_Type 1   \n",
       "\n",
       "   lead_time  arrival_year  arrival_month  arrival_date market_segment_type  \\\n",
       "0        224          2017             10             2             Offline   \n",
       "1          5          2018             11             6              Online   \n",
       "2          1          2018              2            28              Online   \n",
       "3        211          2018              5            20              Online   \n",
       "4         48          2018              4            11              Online   \n",
       "\n",
       "   repeated_guest  no_of_previous_cancellations  \\\n",
       "0               0                             0   \n",
       "1               0                             0   \n",
       "2               0                             0   \n",
       "3               0                             0   \n",
       "4               0                             0   \n",
       "\n",
       "   no_of_previous_bookings_not_canceled  avg_price_per_room  \\\n",
       "0                                     0               65.00   \n",
       "1                                     0              106.68   \n",
       "2                                     0               60.00   \n",
       "3                                     0              100.00   \n",
       "4                                     0               94.50   \n",
       "\n",
       "   no_of_special_requests booking_status  total_nights  \n",
       "0                       0   Not_Canceled             3  \n",
       "1                       1   Not_Canceled             5  \n",
       "2                       0       Canceled             3  \n",
       "3                       0       Canceled             2  \n",
       "4                       0       Canceled             2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = ('Resources/clean_hotel_dataset.csv')\n",
    "model_df = pd.read_csv(file)\n",
    "model_df.reset_index(inplace=True, drop=True)\n",
    "model_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f487237",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>no_of_adults</th>\n",
       "      <th>no_of_children</th>\n",
       "      <th>no_of_weekend_nights</th>\n",
       "      <th>no_of_week_nights</th>\n",
       "      <th>required_car_parking_space</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_year</th>\n",
       "      <th>arrival_month</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>repeated_guest</th>\n",
       "      <th>no_of_previous_cancellations</th>\n",
       "      <th>no_of_previous_bookings_not_canceled</th>\n",
       "      <th>avg_price_per_room</th>\n",
       "      <th>no_of_special_requests</th>\n",
       "      <th>total_nights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>2017</td>\n",
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       "      <td>3</td>\n",
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       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
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       "      <td>1</td>\n",
       "      <td>5</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>211</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.50</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   no_of_adults  no_of_children  no_of_weekend_nights  no_of_week_nights  \\\n",
       "0             2               0                     1                  2   \n",
       "1             2               0                     2                  3   \n",
       "2             1               0                     2                  1   \n",
       "3             2               0                     0                  2   \n",
       "4             2               0                     1                  1   \n",
       "\n",
       "   required_car_parking_space  lead_time  arrival_year  arrival_month  \\\n",
       "0                           0        224          2017             10   \n",
       "1                           0          5          2018             11   \n",
       "2                           0          1          2018              2   \n",
       "3                           0        211          2018              5   \n",
       "4                           0         48          2018              4   \n",
       "\n",
       "   arrival_date  repeated_guest  no_of_previous_cancellations  \\\n",
       "0             2               0                             0   \n",
       "1             6               0                             0   \n",
       "2            28               0                             0   \n",
       "3            20               0                             0   \n",
       "4            11               0                             0   \n",
       "\n",
       "   no_of_previous_bookings_not_canceled  avg_price_per_room  \\\n",
       "0                                     0               65.00   \n",
       "1                                     0              106.68   \n",
       "2                                     0               60.00   \n",
       "3                                     0              100.00   \n",
       "4                                     0               94.50   \n",
       "\n",
       "   no_of_special_requests  total_nights  \n",
       "0                       0             3  \n",
       "1                       1             5  \n",
       "2                       0             3  \n",
       "3                       0             2  \n",
       "4                       0             2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the features set.\n",
    "X = model_df.copy()\n",
    "X = X.drop([\"Booking_ID\",\"booking_status\",\"room_type_reserved\",\"market_segment_type\"],axis =1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "25e6473e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Not_Canceled', 'Not_Canceled', 'Canceled', 'Canceled', 'Canceled'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the target set.\n",
    "y = model_df[\"booking_status\"].values\n",
    "y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "16c7e134",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting into Train and Test sets.\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=78)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dbf1da9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a StandardScaler instance.\n",
    "scaler = StandardScaler()\n",
    "# Fitting the Standard Scaler with the training data.\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Scaling the data.\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a2031f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a random forest classifier.\n",
    "rf_model = RandomForestClassifier(n_estimators=128, random_state=78) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c2633a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting the model\n",
    "rf_model = rf_model.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0f59a90a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making predictions using the testing data.\n",
    "predictions = rf_model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "956ffc0d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted Cancelled</th>\n",
       "      <th>Predicted Not Cancelled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual Cancelled</th>\n",
       "      <td>2269</td>\n",
       "      <td>704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual Not Cancelled</th>\n",
       "      <td>312</td>\n",
       "      <td>5784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                      Predicted Cancelled  Predicted Not Cancelled\n",
       "Actual Cancelled                     2269                      704\n",
       "Actual Not Cancelled                  312                     5784"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the confusion matrix.\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix.\n",
    "cm_df = pd.DataFrame(\n",
    "    cm, index=[\"Actual Cancelled\", \"Actual Not Cancelled\"], columns=[\"Predicted Cancelled\", \"Predicted Not Cancelled\"])\n",
    "\n",
    "cm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "06733eba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the accuracy score.\n",
    "acc_score = accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8ae7e2c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix Random Forest\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>Actual Not Cancelled</th>\n",
       "      <td>312</td>\n",
       "      <td>5784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Predicted Cancelled  Predicted Not Cancelled\n",
       "Actual Cancelled                     2269                      704\n",
       "Actual Not Cancelled                  312                     5784"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score : 0.8879700077186018\n",
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Canceled       0.88      0.76      0.82      2973\n",
      "Not_Canceled       0.89      0.95      0.92      6096\n",
      "\n",
      "    accuracy                           0.89      9069\n",
      "   macro avg       0.89      0.86      0.87      9069\n",
      "weighted avg       0.89      0.89      0.89      9069\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Displaying results\n",
    "print(\"Confusion Matrix Random Forest\")\n",
    "display(cm_df)\n",
    "print(f\"Accuracy Score : {acc_score}\")\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9aafd36",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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