🏨 Dynamic Hotel Pricing Optimization

🚀 Advanced Elasticity Modeling → XGBoost Regressor

Goal: Model complex, non-linear price elasticity using XGBoost.

Code

# 1️⃣ Load Data
import pandas as pd

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

# Filter to completed bookings
df = df[df['is_canceled'] == 0]

# Basic feature engineering
df['reservation_status_date'] = pd.to_datetime(df['reservation_status_date'])
df['day_of_week'] = df['reservation_status_date'].dt.dayofweek
df['month'] = df['reservation_status_date'].dt.month
df['stay_length'] = df['stays_in_weekend_nights'] + df['stays_in_week_nights']

# Keep useful columns
df_model = df[['adr', 'lead_time', 'day_of_week', 'month', 'stay_length', 'market_segment', 'hotel']].copy()

Code

# 2️⃣ Encode categorical features

from sklearn.preprocessing import LabelEncoder

df_model['market_segment_encoded'] = LabelEncoder().fit_transform(df_model['market_segment'])
df_model['hotel_encoded'] = LabelEncoder().fit_transform(df_model['hotel'])

Code

# 3️⃣ Aggregate Bookings by Price & Features

# Group data → each row will represent (adr, segment, hotel, etc.) → number of bookings
# You can also do this as a time series → here simple aggregate per combination:

agg_df = df_model.groupby(['adr', 'market_segment_encoded', 'hotel_encoded', 'day_of_week', 'month']) \
    .size().reset_index(name='bookings')

agg_df.head()

# 4️⃣ Train XGBoost Regressor from xgboost import XGBRegressor from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error import numpy as np # Features and target features = ['adr', 'market_segment_encoded', 'hotel_encoded', 'day_of_week', 'month'] X = agg_df[features] y = agg_df['bookings'] # Train/test split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Model model = XGBRegressor(n_estimators=500, learning_rate=0.05, max_depth=5) model.fit(X_train, y_train) # Predict y_pred = model.predict(X_test) # Evaluation → safe for all sklearn versions mse = mean_squared_error(y_test, y_pred) rmse = np.sqrt(mse) print(f'RMSE: {rmse:.2f}')

# 5️⃣ Feature Importance → Business friendly import matplotlib.pyplot as plt import xgboost as xgb xgb.plot_importance(model, importance_type='gain', height=0.8) plt.title('Feature Importance - XGBoost Elasticity Model') plt.show()

# 6️⃣ Partial Dependence Plot for Price (adr) # Advanced way → use sklearn PartialDependenceDisplay if using sklearn interface # For now → manual simple PDP import numpy as np adr_values = np.linspace(50, 300, 50) pdp_values = [] # Fix other features at typical values (e.g., most common segment/hotel, median day/month) default_segment = agg_df['market_segment_encoded'].mode()[0] default_hotel = agg_df['hotel_encoded'].mode()[0] default_day = 3 # mid-week default_month = 6 # summer for adr in adr_values: row = pd.DataFrame({ 'adr': [adr], 'market_segment_encoded': [default_segment], 'hotel_encoded': [default_hotel], 'day_of_week': [default_day], 'month': [default_month] }) pred = model.predict(row)[0] pdp_values.append(pred) # Plot PDP plt.figure(figsize=(8,6)) plt.plot(adr_values, pdp_values, marker='o') plt.title('Partial Dependence Plot → Price Elasticity (XGBoost)') plt.xlabel('Price (ADR)') plt.ylabel('Predicted Bookings') plt.grid() plt.show()