Hotel Booking Dataset: Kaggle
Preprocessing:
- Load Dataset
- Exploratory Data Analysis
- Cleaning Data
- Remove Outliers
- Encoding Categorical Variables
- Visualize Correlation Between Variables
- Feature Selection
- Split Data into Train & Test Data
Models Used:
Deployment:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from ydata_profiling import ProfileReport
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectKBest, f_classif
import warnings
warnings.filterwarnings('ignore')
%matplotlib inlinebooking = pd.read_csv("booking.csv")
# Make index start from 1 instead of 0 & drop the old index
booking.drop(["Booking_ID"], axis=1, inplace=True)
booking.index = booking.index + 1
print(booking.shape)
booking.head()(36285, 16)
| number of adults | number of children | number of weekend nights | number of week nights | type of meal | car parking space | room type | lead time | market segment type | repeated | P-C | P-not-C | average price | special requests | date of reservation | booking status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 2 | 5 | Meal Plan 1 | 0 | Room_Type 1 | 224 | Offline | 0 | 0 | 0 | 88.00 | 0 | 10/2/2015 | Not_Canceled |
| 2 | 1 | 0 | 1 | 3 | Not Selected | 0 | Room_Type 1 | 5 | Online | 0 | 0 | 0 | 106.68 | 1 | 11/6/2018 | Not_Canceled |
| 3 | 2 | 1 | 1 | 3 | Meal Plan 1 | 0 | Room_Type 1 | 1 | Online | 0 | 0 | 0 | 50.00 | 0 | 2/28/2018 | Canceled |
| 4 | 1 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 211 | Online | 0 | 0 | 0 | 100.00 | 1 | 5/20/2017 | Canceled |
| 5 | 1 | 0 | 1 | 2 | Not Selected | 0 | Room_Type 1 | 48 | Online | 0 | 0 | 0 | 77.00 | 0 | 4/11/2018 | Canceled |
profile = ProfileReport(booking, title="Pandas Profiling Report")booking.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36285 entries, 1 to 36285
Data columns (total 16 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 number of adults 36285 non-null int64
1 number of children 36285 non-null int64
2 number of weekend nights 36285 non-null int64
3 number of week nights 36285 non-null int64
4 type of meal 36285 non-null object
5 car parking space 36285 non-null int64
6 room type 36285 non-null object
7 lead time 36285 non-null int64
8 market segment type 36285 non-null object
9 repeated 36285 non-null int64
10 P-C 36285 non-null int64
11 P-not-C 36285 non-null int64
12 average price 36285 non-null float64
13 special requests 36285 non-null int64
14 date of reservation 36285 non-null object
15 booking status 36285 non-null object
dtypes: float64(1), int64(10), object(5)
memory usage: 4.4+ MB
booking.describe()| number of adults | number of children | number of weekend nights | number of week nights | car parking space | lead time | repeated | P-C | P-not-C | average price | special requests | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 36285.000000 | 36285.000000 | 36285.000000 | 36285.000000 | 36285.000000 | 36285.000000 | 36285.000000 | 36285.000000 | 36285.000000 | 36285.000000 | 36285.000000 |
| mean | 1.844839 | 0.105360 | 0.810693 | 2.204602 | 0.030977 | 85.239851 | 0.025630 | 0.023343 | 0.153369 | 103.421636 | 0.619733 |
| std | 0.518813 | 0.402704 | 0.870590 | 1.410946 | 0.173258 | 85.938796 | 0.158032 | 0.368281 | 1.753931 | 35.086469 | 0.786262 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 2.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 17.000000 | 0.000000 | 0.000000 | 0.000000 | 80.300000 | 0.000000 |
| 50% | 2.000000 | 0.000000 | 1.000000 | 2.000000 | 0.000000 | 57.000000 | 0.000000 | 0.000000 | 0.000000 | 99.450000 | 0.000000 |
| 75% | 2.000000 | 0.000000 | 2.000000 | 3.000000 | 0.000000 | 126.000000 | 0.000000 | 0.000000 | 0.000000 | 120.000000 | 1.000000 |
| max | 4.000000 | 10.000000 | 7.000000 | 17.000000 | 1.000000 | 443.000000 | 1.000000 | 13.000000 | 58.000000 | 540.000000 | 5.000000 |
booking.nunique()number of adults 5
number of children 6
number of weekend nights 8
number of week nights 18
type of meal 4
car parking space 2
room type 7
lead time 352
market segment type 5
repeated 2
P-C 9
P-not-C 59
average price 3930
special requests 6
date of reservation 553
booking status 2
dtype: int64
print(booking.isnull().sum().sort_values(ascending=False))number of adults 0
number of children 0
number of weekend nights 0
number of week nights 0
type of meal 0
car parking space 0
room type 0
lead time 0
market segment type 0
repeated 0
P-C 0
P-not-C 0
average price 0
special requests 0
date of reservation 0
booking status 0
dtype: int64
# Create box plots for every variable before droping outliers
plt.figure(figsize=(12, 8))
sns.set(style="darkgrid")
sns.boxplot(data=booking, orient="h")
plt.title("Box Plot for Every Variable")
plt.show()
print(booking.shape)
(36285, 16)
outliers_cols = ["lead time", "average price"]
for column in outliers_cols:
if booking[column].dtype in ["int64", "float64"]:
q1 = booking[column].quantile(0.25)
q3 = booking[column].quantile(0.75)
iqr = q3 - q1
lower_bound = q1 - 1.5 * iqr
upper_bound = q3 + 1.5 * iqr
booking = booking[
(booking[column] >= lower_bound) & (booking[column] <= upper_bound)
]# Create box plots for every variable after dropping outliers
plt.figure(figsize=(12, 8))
sns.set(style="darkgrid")
booking_boxplot = sns.boxplot(data=booking, orient="h")
plt.title("Box Plot for Every Variable")
plt.show()
print(booking.shape)
(33345, 16)
Canceled = 1
Not_Canceled = 0
booking["booking status"] = booking["booking status"].replace("Canceled", 1)
booking["booking status"] = booking["booking status"].replace("Not_Canceled", 0)
booking.head()| number of adults | number of children | number of weekend nights | number of week nights | type of meal | car parking space | room type | lead time | market segment type | repeated | P-C | P-not-C | average price | special requests | date of reservation | booking status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 2 | 5 | Meal Plan 1 | 0 | Room_Type 1 | 224 | Offline | 0 | 0 | 0 | 88.00 | 0 | 10/2/2015 | 0 |
| 2 | 1 | 0 | 1 | 3 | Not Selected | 0 | Room_Type 1 | 5 | Online | 0 | 0 | 0 | 106.68 | 1 | 11/6/2018 | 0 |
| 3 | 2 | 1 | 1 | 3 | Meal Plan 1 | 0 | Room_Type 1 | 1 | Online | 0 | 0 | 0 | 50.00 | 0 | 2/28/2018 | 1 |
| 4 | 1 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 211 | Online | 0 | 0 | 0 | 100.00 | 1 | 5/20/2017 | 1 |
| 5 | 1 | 0 | 1 | 2 | Not Selected | 0 | Room_Type 1 | 48 | Online | 0 | 0 | 0 | 77.00 | 0 | 4/11/2018 | 1 |
booking = booking[~booking["date of reservation"].str.contains("-")]
booking["date of reservation"] = pd.to_datetime(booking["date of reservation"])
booking["day"] = booking["date of reservation"].dt.day
booking["month"] = booking["date of reservation"].dt.month
booking["year"] = booking["date of reservation"].dt.year
# Drop the original datetime column
booking = booking.drop(columns=["date of reservation"])
booking.info()<class 'pandas.core.frame.DataFrame'>
Index: 33312 entries, 1 to 36285
Data columns (total 18 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 number of adults 33312 non-null int64
1 number of children 33312 non-null int64
2 number of weekend nights 33312 non-null int64
3 number of week nights 33312 non-null int64
4 type of meal 33312 non-null object
5 car parking space 33312 non-null int64
6 room type 33312 non-null object
7 lead time 33312 non-null int64
8 market segment type 33312 non-null object
9 repeated 33312 non-null int64
10 P-C 33312 non-null int64
11 P-not-C 33312 non-null int64
12 average price 33312 non-null float64
13 special requests 33312 non-null int64
14 booking status 33312 non-null int64
15 day 33312 non-null int32
16 month 33312 non-null int32
17 year 33312 non-null int32
dtypes: float64(1), int32(3), int64(11), object(3)
memory usage: 4.4+ MB
booking["average price"] = booking["average price"].round().astype(int)True = 1
False = 0
object_columns = booking.select_dtypes(include=["object"]).columns
booking = pd.get_dummies(booking, columns=object_columns)
booking = booking.replace({True: 1, False: 0})
booking.info()<class 'pandas.core.frame.DataFrame'>
Index: 33312 entries, 1 to 36285
Data columns (total 30 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 number of adults 33312 non-null int64
1 number of children 33312 non-null int64
2 number of weekend nights 33312 non-null int64
3 number of week nights 33312 non-null int64
4 car parking space 33312 non-null int64
5 lead time 33312 non-null int64
6 repeated 33312 non-null int64
7 P-C 33312 non-null int64
8 P-not-C 33312 non-null int64
9 average price 33312 non-null int64
10 special requests 33312 non-null int64
11 booking status 33312 non-null int64
12 day 33312 non-null int32
13 month 33312 non-null int32
14 year 33312 non-null int32
15 type of meal_Meal Plan 1 33312 non-null int64
16 type of meal_Meal Plan 2 33312 non-null int64
17 type of meal_Not Selected 33312 non-null int64
18 room type_Room_Type 1 33312 non-null int64
19 room type_Room_Type 2 33312 non-null int64
20 room type_Room_Type 3 33312 non-null int64
21 room type_Room_Type 4 33312 non-null int64
22 room type_Room_Type 5 33312 non-null int64
23 room type_Room_Type 6 33312 non-null int64
24 room type_Room_Type 7 33312 non-null int64
25 market segment type_Aviation 33312 non-null int64
26 market segment type_Complementary 33312 non-null int64
27 market segment type_Corporate 33312 non-null int64
28 market segment type_Offline 33312 non-null int64
29 market segment type_Online 33312 non-null int64
dtypes: int32(3), int64(27)
memory usage: 7.5 MB
booking.head()| number of adults | number of children | number of weekend nights | number of week nights | car parking space | lead time | repeated | P-C | P-not-C | average price | ... | room type_Room_Type 3 | room type_Room_Type 4 | room type_Room_Type 5 | room type_Room_Type 6 | room type_Room_Type 7 | market segment type_Aviation | market segment type_Complementary | market segment type_Corporate | market segment type_Offline | market segment type_Online | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 2 | 5 | 0 | 224 | 0 | 0 | 0 | 88 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2 | 1 | 0 | 1 | 3 | 0 | 5 | 0 | 0 | 0 | 107 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 3 | 2 | 1 | 1 | 3 | 0 | 1 | 0 | 0 | 0 | 50 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 4 | 1 | 0 | 0 | 2 | 0 | 211 | 0 | 0 | 0 | 100 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 5 | 1 | 0 | 1 | 2 | 0 | 48 | 0 | 0 | 0 | 77 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
5 rows × 30 columns
plt.figure(figsize=(12, 8))
sns.heatmap(booking.corr(), cmap="icefire", linewidths=0.5)
plt.title("Correlation Heatmap")
plt.show()
features = booking.drop(["booking status"], axis=1)
target = booking["booking status"]
k_best = SelectKBest(score_func=f_classif, k=10)
X = k_best.fit_transform(features, target)
y = target
# Get the indices of the selected features
selected_features_indices = k_best.get_support(indices=True)
# Get the scores associated with each feature
feature_scores = k_best.scores_
# Create a list of tuples containing feature names and scores
feature_info = list(zip(features.columns, feature_scores))
# Sort the feature info in descending order based on scores
sorted_feature_info = sorted(feature_info, key=lambda x: x[1], reverse=True)
for feature_name, feature_score in sorted_feature_info[:10]:
print(f"{feature_name}: {feature_score:.2f}")lead time: 6755.25
special requests: 2136.14
year: 952.07
market segment type_Online: 646.78
average price: 614.84
market segment type_Corporate: 414.32
repeated: 343.90
market segment type_Offline: 250.21
number of week nights: 248.88
car parking space: 216.60
feature_names, feature_scores = zip(*sorted_feature_info[:])
# Create a bar chart
plt.figure(figsize=(10, 6))
plt.barh(feature_names, feature_scores, color="skyblue")
plt.xlabel("Feature Importance Score")
plt.title("Features Importance Scores")
plt.show()
selected_features_df = features.iloc[:, selected_features_indices]
selected_features_df.head()| number of week nights | car parking space | lead time | repeated | average price | special requests | year | market segment type_Corporate | market segment type_Offline | market segment type_Online | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 5 | 0 | 224 | 0 | 88 | 0 | 2015 | 0 | 1 | 0 |
| 2 | 3 | 0 | 5 | 0 | 107 | 1 | 2018 | 0 | 0 | 1 |
| 3 | 3 | 0 | 1 | 0 | 50 | 0 | 2018 | 0 | 0 | 1 |
| 4 | 2 | 0 | 211 | 0 | 100 | 1 | 2017 | 0 | 0 | 1 |
| 5 | 2 | 0 | 48 | 0 | 77 | 0 | 2018 | 0 | 0 | 1 |
X_train, X_test, y_train, y_test = train_test_split(
X, target, test_size=0.2, random_state=5
)from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
scores = {}log_reg = LogisticRegression()
params = {"C": [0.01, 0.1, 1, 10, 100], "penalty": ["l1", "l2"]}
grid_search = GridSearchCV(log_reg, param_grid=params, cv=5)
grid_search.fit(X_train, y_train)
print(f"Best Parameters: {grid_search.best_params_}")
print(f"Best Score: {grid_search.best_score_}")
best_log_reg = grid_search.best_estimator_
y_pred = best_log_reg.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred):.2f}")
scores["Logistic Regression"] = accuracy_score(y_test, y_pred)
print("---------------------------------------------------------")
print(f"Confusion Matrix: \n{confusion_matrix(y_test, y_pred)}")
sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, cmap="Blues", fmt=".0f")
print("---------------------------------------------------------")
print(f"Classification Report: \n{classification_report(y_test, y_pred)}")Best Parameters: {'C': 1, 'penalty': 'l2'}
Best Score: 0.7974411456024718
Accuracy: 0.79
---------------------------------------------------------
Confusion Matrix:
[[4108 469]
[ 909 1177]]
---------------------------------------------------------
Classification Report:
precision recall f1-score support
0 0.82 0.90 0.86 4577
1 0.72 0.56 0.63 2086
accuracy 0.79 6663
macro avg 0.77 0.73 0.74 6663
weighted avg 0.79 0.79 0.79 6663
knn = KNeighborsClassifier()
params = {"n_neighbors": np.arange(1, 10)}
grid_search = GridSearchCV(knn, param_grid=params, cv=5)
grid_search.fit(X_train, y_train)
print(f"Best Parameters: {grid_search.best_params_}")
print(f"Best Score: {grid_search.best_score_}")
best_knn = grid_search.best_estimator_
y_pred = best_knn.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred):.2f}")
scores["KNN"] = accuracy_score(y_test, y_pred)
print("---------------------------------------------------------")
print(f"Confusion Matrix: \n{confusion_matrix(y_test, y_pred)}")
sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, cmap="Blues", fmt=".0f")
print("---------------------------------------------------------")
print(f"Classification Report: \n{classification_report(y_test, y_pred)}")Best Parameters: {'n_neighbors': 2}
Best Score: 0.8175542299788372
Accuracy: 0.83
---------------------------------------------------------
Confusion Matrix:
[[4345 232]
[ 904 1182]]
---------------------------------------------------------
Classification Report:
precision recall f1-score support
0 0.83 0.95 0.88 4577
1 0.84 0.57 0.68 2086
accuracy 0.83 6663
macro avg 0.83 0.76 0.78 6663
weighted avg 0.83 0.83 0.82 6663
dt = DecisionTreeClassifier()
params = {"max_depth": np.arange(0, 30, 5), "criterion": ["gini", "entropy"]}
grid_search = GridSearchCV(dt, param_grid=params, cv=5)
grid_search.fit(X_train, y_train)
print(f"Best Parameters: {grid_search.best_params_}")
print(f"Best Score: {grid_search.best_score_}")
best_dt = grid_search.best_estimator_
y_pred = best_dt.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred):.2f}")
scores["Decision Tree"] = accuracy_score(y_test, y_pred)
print("---------------------------------------------------------")
print(f"Confusion Matrix: \n{confusion_matrix(y_test, y_pred)}")
sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, cmap="Blues", fmt=".0f")
print("---------------------------------------------------------")
print(f"Classification Report: \n{classification_report(y_test, y_pred)}")Best Parameters: {'criterion': 'entropy', 'max_depth': 15}
Best Score: 0.8595821581582879
Accuracy: 0.86
---------------------------------------------------------
Confusion Matrix:
[[4186 391]
[ 534 1552]]
---------------------------------------------------------
Classification Report:
precision recall f1-score support
0 0.89 0.91 0.90 4577
1 0.80 0.74 0.77 2086
accuracy 0.86 6663
macro avg 0.84 0.83 0.84 6663
weighted avg 0.86 0.86 0.86 6663
rf = RandomForestClassifier(max_depth=20, n_estimators=20)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred):.2f}")
scores["Random Forest"] = accuracy_score(y_test, y_pred)
print("---------------------------------------------------------")
print(f"Confusion Matrix: \n{confusion_matrix(y_test, y_pred)}")
sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, cmap="Blues", fmt=".0f")
print("---------------------------------------------------------")
print(f"Classification Report: \n{classification_report(y_test, y_pred)}")Accuracy: 0.87
---------------------------------------------------------
Confusion Matrix:
[[4247 330]
[ 507 1579]]
---------------------------------------------------------
Classification Report:
precision recall f1-score support
0 0.89 0.93 0.91 4577
1 0.83 0.76 0.79 2086
accuracy 0.87 6663
macro avg 0.86 0.84 0.85 6663
weighted avg 0.87 0.87 0.87 6663
svc = SVC(C=100, kernel="rbf", gamma=0.1)
svc.fit(X_train, y_train)
y_pred = svc.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred):.2f}")
scores["SVC"] = accuracy_score(y_test, y_pred)
print("---------------------------------------------------------")
print(f"Confusion Matrix: \n{confusion_matrix(y_test, y_pred)}")
sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, cmap="Blues", fmt=".0f")
print("---------------------------------------------------------")
print(f"Classification Report: \n{classification_report(y_test, y_pred)}")Accuracy: 0.83
---------------------------------------------------------
Confusion Matrix:
[[4003 574]
[ 569 1517]]
---------------------------------------------------------
Classification Report:
precision recall f1-score support
0 0.88 0.87 0.88 4577
1 0.73 0.73 0.73 2086
accuracy 0.83 6663
macro avg 0.80 0.80 0.80 6663
weighted avg 0.83 0.83 0.83 6663
for model, score in scores.items():
print(f"{model}: {score:.4f}")Logistic Regression: 0.7932
KNN: 0.8295
Decision Tree: 0.8612
Random Forest: 0.8744
SVC: 0.8285
