https://github.com/kunalgarglibra/hotel_reservation_prediction
https://github.com/kunalgarglibra/hotel_reservation_prediction
Last synced: 8 months ago
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- Host: GitHub
- URL: https://github.com/kunalgarglibra/hotel_reservation_prediction
- Owner: kunalgarglibra
- Created: 2025-04-11T04:08:31.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-04-25T01:36:17.000Z (about 1 year ago)
- Last Synced: 2025-04-25T02:43:47.706Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 3.33 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 🏨 Hotel Reservation Prediction
This project aims to predict whether a hotel booking will be canceled based on various customer and reservation-related features. By leveraging machine learning techniques and modern deployment practices, the solution enables proactive management of hotel reservations to improve operational efficiency.
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## 🔍 Problem Statement
Hotel booking cancellations lead to significant revenue losses and poor resource planning. This project helps hotels **predict the likelihood of a booking being canceled** based on customer behavior, booking patterns, and preferences.
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## 📊 Features Used
### Categorical Columns
- `type_of_meal_plan`
- `required_car_parking_space`
- `room_type_reserved`
### Numerical Columns
- `no_of_adults`
- `no_of_children`
- `no_of_weekend_nights`
- `no_of_week_nights`
- `lead_time`
- `arrival_date`
- `avg_price_per_room`
- `no_of_special_requests`
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## 🧠 ML Workflow
### 1. **Data Infrastructure Setup**
- Data stored and maintained in a **Google Cloud Storage (GCP Bucket)**.
### 2. **Development Environment**
- Codebase built using **VS Code** with Python.
- Versioning via **GitHub**.
### 3. **Data Ingestion**
- Data pulled directly from GCP Bucket into local development environment.
### 4. **Exploratory Data Analysis (EDA) & Preprocessing**
- **Univariate & Bivariate Analysis** to explore data trends.
- **Label Encoding** for categorical features.
- **Variance Inflation Factor (VIF)** to detect multicollinearity.
- Handling **skewness**, **imbalanced datasets**.
- **Feature selection** using RandomForest importance scores.
### 5. **Model Selection**
Models compared based on key metrics:
- ✅ Accuracy
- 📌 Precision
- 🔁 Recall
- 🎯 F1 Score
**Models Tested**:
- Random Forest (RF)
- Gradient Boosting
- AdaBoost
- Logistic Regression
- Support Vector Classifier (SVC)
- K-Nearest Neighbors (KNN)
- Gaussian Naive Bayes (NB)
- LightGBM (LGBM)
- XGBoost (XGB)
✅ **Best Model**: `Random Forest Classifier`
### 6. **Hyperparameter Tuning**
- Using **RandomizedSearchCV** to optimize:
- `bootstrap`
- `max_depth`
- `min_samples_leaf`
- `min_samples_split`
- `n_estimators`
### 7. **Model Training & Saving**
- Final model trained and serialized using `joblib`.
### 8. **Experiment Tracking**
- Integrated **MLFlow** for tracking experiments, metrics, and models.
### 9. **Pipeline Development**
- Modular **training pipeline** created for scalability and reusability.
### 10. **Version Control**
- **Code Versioning**: Git + GitHub
- **Data Versioning**: GitHub + GCP bucket logging
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## 🌐 User Interface
### Flask Application
- Built using Flask to interact with the trained model.
- Accepts user input and predicts if the booking will be canceled.
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## 🚀 CI/CD Deployment
### Containerization & Deployment Workflow:
1. **Create virtual environment** using Jenkins pipeline.
3. **Dockerize** the project.
5. **Build Docker image** and push to **Google Cloud Container Registry (GCR)**.
7. **Deploy image** to **Google Cloud Run** for seamless scalability.
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## 🛠️ Testing Application:
## 🛠️ Tech Stack
- **Python** 🐍
- **Scikit-learn**, **Pandas**, **NumPy**, **Matplotlib**, **Seaborn**
- **MLFlow** for experiment tracking
- **Flask** for UI/API
- **Docker**, **Jenkins** for CI/CD
- **Google Cloud Platform** (GCS, GCR, Cloud Run)
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## 📦 Dataset
The dataset used for this project can be accessed [here](https://www.kaggle.com/datasets/ahsan81/hotel-reservations-classification-dataset) on Kaggle.
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## 🙏 Acknowledgements
This project is part of a course taught by **Sudhanshu Gusain** on **udemy.com**.
Thanks to the instructor for a practical, hands-on approach to solving real-world ML problems!
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