Binary Classification with a Tabular Reservation Cancellation Dataset

1. Problem Statement

You are a data analyst working for a large hotel chain that faces significant revenue losses due to frequent reservation cancellations. The management is concerned about the unpredictable nature of these cancellations, which leads to occupancy rate inconsistencies and inefficient resource allocation. They've tasked you with developing a data-driven solution to mitigate these losses and improve the hotel's revenue stability.

To achieve this, you are given detailed information about customer booking patterns, hotel characteristics, and booking details in a tabular format. Your task is to build a machine-learning model that accurately identifies reservations at high risk of cancellation. By analyzing factors such as booking lead time, customer demographics, room type reserved, and past cancellation history, you can predict which reservations are most likely to be canceled. This insight enables the hotel to take proactive measures, such as sending reminders or offering incentives, to retain these bookings, thus improving revenue and occupancy rates. Your work will directly influence the hotel chain's ability to optimize operations and enhance customer satisfaction.

Goal: The goal of this project is to build a machine learning model that can accurately predict reservation cancellations. By identifying potential cancellations, businesses can take steps to retain reservations, optimize resource allocation, and improve overall operational efficiency.

2. Data Description

The dataset for this competition (both train and test) was generated from a deep learning model trained on the Reservation Cancellation Prediction dataset. Feature distributions are close to, but not exactly the same, as the original.

• Rows: 42101
• Columns: 19
• Variables:
  • id: Unique identifier
  • no_of_adults: Number of adults
  • no_of_children: Number of children
  • no_of_weekend_nights: Number of weekend nights
  • no_of_week_nights: Number of week nights
  • type_of_meal_plan: Type of meal plan
  • required_car_parking_space: Whether car parking space is required
  • room_type_reserved: Type of room reserved
  • lead_time: Lead time
  • arrival_year: Arrival year
  • arrival_month: Arrival month
  • arrival_date: Arrival date
  • market_segment_type: Market segment type
  • repeated_guest: Whether the guest is repeated
  • no_of_previous_cancellations: Number of previous cancellations
  • no_of_previous_bookings_not_canceled: Number of previous bookings not canceled
  • avg_price_per_room: Average price per room
  • no_of_special_requests: Number of special requests
  • booking_status: Booking status (target variable)

Data Source: Kaggle Playground Series S3E7

3. Your Task

Your task is to build a classification model to predict the booking status.

1. Data Exploration and Preprocessing:
   • Load the dataset using Pandas.
   • Explore the data to understand the distribution of features.
   • Handle missing values, if any.
   • Convert categorical variables to numerical format using techniques like one-hot encoding.
2. Feature Engineering (Optional):
   • Create new features that may improve model performance.
3. Model Building:
   • Split the data into training and validation sets.
   • Choose a suitable classification algorithm (e.g., Logistic Regression, Random Forest, Gradient Boosting).
   • Train the model on the training data.
4. Model Evaluation:
   • Evaluate the model's performance on the validation data.
   • Use metrics such as accuracy, precision, recall, F1-score, and AUC-ROC.
   • Visualize the results using a confusion matrix and ROC curve.
5. Prediction and Submission:
   • Make predictions on the test data.

Python Libraries: Pandas, NumPy, scikit-learn, Matplotlib/Seaborn.