🏨 Dynamic Hotel Pricing Optimization

📊 01_EDA - Exploratory Data Analysis

Goal: Explore the dataset, understand key patterns and prepare features for modeling.

Code

# 1️⃣ Load Data
import pandas as pd

df = pd.read_csv('../data/raw/hotel_bookings.csv')

df.shape
df.head()

	hotel	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_week_nights	adults	...	deposit_type	agent	company	customer_type	adr	total_of_special_requests	reservation_status	reservation_status_date
0	Resort Hotel	342	2015	July	27	1	0	2	...	No Deposit	NaN	NaN	Transient	0.0	0	Check-Out	2015-07-01
1	Resort Hotel	737	2015	July	27	1	0	2	...	No Deposit	NaN	NaN	Transient	0.0	0	Check-Out	2015-07-01
2	Resort Hotel	7	2015	July	27	1	1	1	...	No Deposit	NaN	NaN	Transient	75.0	0	Check-Out	2015-07-02
3	Resort Hotel	13	2015	July	27	1	1	1	...	No Deposit	304.0	NaN	Transient	75.0	0	Check-Out	2015-07-02
4	Resort Hotel	14	2015	July	27	1	2	2	...	No Deposit	240.0	NaN	Transient	98.0	1	Check-Out	2015-07-03

5 rows × 32 columns

Code

# 2️⃣ Data Summary
df.info()

df.isnull().sum()

df.describe()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 119390 entries, 0 to 119389
Data columns (total 32 columns):
 #   Column                          Non-Null Count   Dtype  
---  ------                          --------------   -----  
 0   hotel                           119390 non-null  object 
 1   is_canceled                     119390 non-null  int64  
 2   lead_time                       119390 non-null  int64  
 3   arrival_date_year               119390 non-null  int64  
 4   arrival_date_month              119390 non-null  object 
 5   arrival_date_week_number        119390 non-null  int64  
 6   arrival_date_day_of_month       119390 non-null  int64  
 7   stays_in_weekend_nights         119390 non-null  int64  
 8   stays_in_week_nights            119390 non-null  int64  
 9   adults                          119390 non-null  int64  
 10  children                        119386 non-null  float64
 11  babies                          119390 non-null  int64  
 12  meal                            119390 non-null  object 
 13  country                         118902 non-null  object 
 14  market_segment                  119390 non-null  object 
 15  distribution_channel            119390 non-null  object 
 16  is_repeated_guest               119390 non-null  int64  
 17  previous_cancellations          119390 non-null  int64  
 18  previous_bookings_not_canceled  119390 non-null  int64  
 19  reserved_room_type              119390 non-null  object 
 20  assigned_room_type              119390 non-null  object 
 21  booking_changes                 119390 non-null  int64  
 22  deposit_type                    119390 non-null  object 
 23  agent                           103050 non-null  float64
 24  company                         6797 non-null    float64
 25  days_in_waiting_list            119390 non-null  int64  
 26  customer_type                   119390 non-null  object 
 27  adr                             119390 non-null  float64
 28  required_car_parking_spaces     119390 non-null  int64  
 29  total_of_special_requests       119390 non-null  int64  
 30  reservation_status              119390 non-null  object 
 31  reservation_status_date         119390 non-null  object 
dtypes: float64(4), int64(16), object(12)
memory usage: 29.1+ MB

	is_canceled	lead_time	arrival_date_year	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	children	babies	is_repeated_guest	previous_cancellations	previous_bookings_not_canceled	booking_changes	agent	company	days_in_waiting_list	adr	required_car_parking_spaces	total_of_special_requests
count	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119386.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	103050.000000	6797.000000	119390.000000	119390.000000	119390.000000	119390.000000
mean	0.370416	104.011416	2016.156554	27.165173	15.798241	0.927599	2.500302	1.856403	0.103890	0.007949	0.031912	0.087118	0.137097	0.221124	86.693382	189.266735	2.321149	101.831122	0.062518	0.571363
std	0.482918	106.863097	0.707476	13.605138	8.780829	0.998613	1.908286	0.579261	0.398561	0.097436	0.175767	0.844336	1.497437	0.652306	110.774548	131.655015	17.594721	50.535790	0.245291	0.792798
min	0.000000	0.000000	2015.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	6.000000	0.000000	-6.380000	0.000000	0.000000
25%	0.000000	18.000000	2016.000000	16.000000	8.000000	0.000000	1.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	9.000000	62.000000	0.000000	69.290000	0.000000	0.000000
50%	0.000000	69.000000	2016.000000	28.000000	16.000000	1.000000	2.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	14.000000	179.000000	0.000000	94.575000	0.000000	0.000000
75%	1.000000	160.000000	2017.000000	38.000000	23.000000	2.000000	3.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	229.000000	270.000000	0.000000	126.000000	0.000000	1.000000
max	1.000000	737.000000	2017.000000	53.000000	31.000000	19.000000	50.000000	55.000000	10.000000	10.000000	1.000000	26.000000	72.000000	21.000000	535.000000	543.000000	391.000000	5400.000000	8.000000	5.000000

Code

# 3️⃣ Feature Engineering
df['stay_length'] = df['stays_in_weekend_nights'] + df['stays_in_week_nights']
df['booking_month'] = pd.to_datetime(df['reservation_status_date']).dt.month
df['weekday'] = pd.to_datetime(df['reservation_status_date']).dt.weekday

df[['stay_length', 'lead_time', 'booking_month', 'weekday']].head()

	stay_length	lead_time	booking_month	weekday
0	0	342	7	2
1	0	737	7	2
2	1	7	7	3
3	1	13	7	3
4	2	14	7	4

Code

# 4️⃣ Target Exploration - Cancellations
import seaborn as sns
import matplotlib.pyplot as plt

cancel_rate = df['is_canceled'].mean()
print(f"Cancellation Rate: {cancel_rate:.2%}")

sns.countplot(data=df, x='booking_month', hue='is_canceled')
plt.title('Cancellations by Month')
plt.show()

Matplotlib is building the font cache; this may take a moment.

Cancellation Rate: 37.04%

Code

# 5️⃣ Price Analysis
sns.histplot(df['adr'], kde=True)
plt.title('Average Daily Rate (ADR) Distribution')
plt.xlabel('ADR (Average Daily Rate)')
plt.show()

sns.boxplot(data=df, x='market_segment', y='adr')
plt.xticks(rotation=45)
plt.title('ADR by Market Segment')
plt.show()

Code

# 6️⃣ Booking Patterns
df['reservation_status_date'] = pd.to_datetime(df['reservation_status_date'])
bookings_by_date = df.groupby('reservation_status_date').size()

bookings_by_date.plot(figsize=(12,6))
plt.title('Bookings Over Time')
plt.xlabel('Date')
plt.ylabel('Number of Bookings')
plt.show()