Dynamic-Hotel-Recommendation-System-Using-NLP

Overview

This project implements a Hotel Recommendation System using Machine Learning techniques. It leverages natural language processing (NLP) and collaborative filtering to recommend hotels based on user preferences and descriptions.

Table of Contents

• Introduction
• Overview
• Table of Contents
• Prerequisites
• Installation
• Usage
• Recommendation Algorithm
• Example Usage
• Contributing
• License

Prerequisites

Make sure you have the following dependencies installed:

• Python (>=3.6)
• Jupyter Notebook
• Pandas
• NumPy
• NLTK

Installation

1. Clone the repository:

git clone https://github.com/raghavendranhp/Dynamic-Hotel-Recommendation-System-Using-NLP.git
cd hotel-recommendation-system

2. Install the dependencies:

pip install -r requirements.txt

Usage

The project is structured as follows:

• data/: Contains the dataset (Hotel_Reviews.csv)(splitted file).
• notebooks/: Jupyter notebooks for data exploration and model development.
• src/: Source code for data preprocessing and recommendation algorithm.
• README.md: Documentation for the project.

Data Preprocessing

We use NLTK for natural language processing and Pandas for data manipulation. The dataset is preprocessed to extract relevant features and create a user-hotel interaction matrix.

Recommendation Algorithm

The recommendation algorithm is based on collaborative filtering and NLP. It calculates the similarity between user descriptions and hotel tags to provide personalized recommendations.

Example Usage

To use the recommendation system:

from recommendation_system import recommend_hotel

# Example: recommend a hotel for a user in Italy with the description "Business trip"
recommendations = recommend_hotel('Italy', 'Business trip')
print(recommendations)

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvement, please open an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Raghavendran S,
Aspiring Data Scientist
LinkedIN Profile
raghavendranhp@gmail.com

Thank You !
Happy Enjoying !