🏨 Dynamic Hotel Pricing Optimization

📈 02_Demand Forecasting

Goal: Build models to forecast demand (number of bookings) per hotel and date.

Code

# 1️⃣ Load Data
import pandas as pd

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

# Preprocess date columns
df['reservation_status_date'] = pd.to_datetime(df['reservation_status_date'])

Code

# 2️⃣ Aggregate Demand by Date

# Example: total bookings per day
bookings_by_date = df.groupby('reservation_status_date').size().reset_index(name='bookings')

bookings_by_date.head()

# 3️⃣ Plot Demand Over Time import matplotlib.pyplot as plt bookings_by_date.set_index('reservation_status_date')['bookings'].plot(figsize=(12,6)) plt.title('Total Bookings Over Time') plt.xlabel('Date') plt.ylabel('Number of Bookings') plt.show()

# 4️⃣ Prophet Model from prophet import Prophet # Prepare data for Prophet df_prophet = bookings_by_date.rename(columns={'reservation_status_date': 'ds', 'bookings': 'y'}) # Initialize model m = Prophet() m.fit(df_prophet) # Create future dataframe future = m.make_future_dataframe(periods=60) # Forecast 60 days ahead # Forecast forecast = m.predict(future) # Plot forecast fig = m.plot(forecast)

# 5️⃣ XGBoost Model (Optional / Advanced) # Feature engineering for ML model bookings_by_date['dayofweek'] = bookings_by_date['reservation_status_date'].dt.dayofweek bookings_by_date['month'] = bookings_by_date['reservation_status_date'].dt.month bookings_by_date['year'] = bookings_by_date['reservation_status_date'].dt.year # Define features and target features = ['dayofweek', 'month', 'year'] X = bookings_by_date[features] y = bookings_by_date['bookings'] # Split data from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False) # Train XGBoost model from xgboost import XGBRegressor model = XGBRegressor() model.fit(X_train, y_train) # Predict y_pred = model.predict(X_test) # Plot predictions plt.figure(figsize=(12,6)) plt.plot(y_test.values, label='Actual') plt.plot(y_pred, label='Predicted') plt.title('XGBoost Demand Forecast - Test Set') plt.legend() plt.show()