Build an AI Hotel Recommender System with Python and Machine Learning

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Build an AI Hotel Recommender System with Python and Machine Learning

Table of Contents

  1. Introduction
  2. Data Exploration
  3. Data Cleaning
  4. Data Visualization
  5. Building the Recommender System
  6. Approaches to the Recommender System
    1. City-Based Approach
    2. Number of Guests-Based Approach
    3. User Requirement-Based Approach
    4. Price-Based Approach
  7. Conclusion
  8. FAQ

Building a Hotel Recommender System using Python and Streamlit

Are You tired of scrolling through endless hotel options when planning your next vacation? Look no further than a hotel recommender system! In this article, we will explore how to build a hotel recommender system using Python and Streamlit.

Introduction

The hotel recommender system we will build consists of four input streams: the number of guests, the city, the budget, and the description of the room that the user wants. Upon giving these inputs, the system will give the top 10 hotels that match the user's requirements. It will also provide a description of the room and a booking link where the user can book the hotel directly from the Website.

Data Exploration

To build our recommender system, we will use two datasets: hotel room details and hotel room attributes. We obtained this data from Kaggle. After exploring the data, we found that there were missing values in the hotel amenities and room description columns. We also found that there were duplicates in the data that needed to be removed.

Data Cleaning

To clean the data, we removed the columns that we did not need and dropped the null and empty values. We also merged the two data frames to Create a single data frame called "hotel." We removed the duplicates and created a unique column called "description" that contained all the descriptive details of the hotel.

Data Visualization

To Visualize the data, we used a correlation heatmap to Show the relationship between the variables in our data frame. We also used a regression map to show the relationship between the price and star rating correlation. We found that the majority of hotels were present in the United Kingdom, and the most booked hotel was the Millennium Mayfair.

Building the Recommender System

We built our recommender system using four approaches: city-based, number of guests-based, user requirement-based, and price-based. The city-based approach filters the data frame based on the city and gives the top 10 hotels present in that city. The number of guests-based approach filters the data frame based on the number of guests and gives the top 10 hotels that can accommodate that number of guests. The user requirement-based approach uses an NLTK library to filter the data frame based on the user's requirements. The price-based approach filters the data frame based on the price and gives the top 10 hotels that match the user's requirements.

Conclusion

In conclusion, building a hotel recommender system using Python and Streamlit is a simple and effective way to help users find the perfect hotel for their next vacation. By using different approaches, we can filter the data frame and provide users with the top 10 hotels that match their requirements.

FAQ

Q: What datasets did you use to build the recommender system? A: We used hotel room details and hotel room attributes datasets obtained from Kaggle.

Q: What approaches did you use to build the recommender system? A: We used four approaches: city-based, number of guests-based, user requirement-based, and price-based.

Q: What is the most booked hotel in your data frame? A: The most booked hotel in our data frame is the Millennium Mayfair.

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