Hotel Reservation Booking Status Prediction

Project Overview

This project predicts the booking status of hotel reservations (such as confirmed, canceled, or no-show) using multiple machine learning models. The solution includes data preprocessing, feature engineering, model building, hyperparameter tuning, and evaluation.

The models implemented are:

• Decision Tree Classifier
• Random Forest Classifier
• AdaBoost Classifier
• XGBoost Classifier

The goal is to compare different algorithms and identify the best-performing model.

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Project Structure

Hotel-Reservation-Booking-Status-Prediction-Project/ │ ├── Hotel-Reservation-Booking-Status-Prediction-using-DecisionTree,RandomForest,AdaBoost,XGBoost-EndtoEnd-Project.ipynb ├── README.md └── dataset/ (https://www.kaggle.com/datasets/ahsan81/hotel-reservations-classification-dataset)

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Technologies Used

• Programming Language: Python
• Libraries:
  • pandas, numpy (Data manipulation)
  • matplotlib, seaborn (Data visualization)
  • scikit-learn (Machine Learning)
  • xgboost (Gradient Boosting)
• IDE: Jupyter Notebook

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Dataset

The dataset contains hotel booking details such as:

• Customer Information: Name, age, contact
• Booking Details: Number of adults, children, special requests
• Reservation Info: Room type, meal plan, arrival date
• Target Variable: booking_status (e.g., Confirmed, Canceled, No-show)

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Workflow

1. Data Loading & Exploration

   • Understand the structure and characteristics of the data
   • Handle missing values and outliers

2. Data Preprocessing

   • Encoding categorical variables
   • Feature scaling (if required)
   • Train-test split with stratification

3. Model Building

   • Train multiple models:
     • Decision Tree
     • Random Forest
     • AdaBoost
     • XGBoost

4. Hyperparameter Tuning

   • GridSearchCV for optimal parameters

5. Model Evaluation

   • Accuracy, Precision, Recall, F1-score
   • Confusion Matrix
   • Feature Importance Visualization

6. Model Selection

   • Choose the best-performing model based on metrics

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Results

• Best model: Random Forest
• Achieved accuracy: 89.81 %
• Feature importance visualization included
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Future Enhancements

-Deploy the model using Streamlit or Flask

-Integrate deep learning models for comparison

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How to Run the Project

1. Clone the repository:

   git clone https://github.com/NirmalanSK/hotel-reservation-prediction.git
   cd hotel-reservation-prediction