This project involves building a sophisticated hotel recommendation system based on various features such as hotel name, location, and detailed review data. The model aims to provide accurate and personalized hotel recommendations for users, making travel planning more efficient and enjoyable.
Hotel Name and Location: Critical identifiers for each hotel, helping to narrow down recommendations based on user preferences.
Additional Number of Scoring: Reflects the volume of additional ratings that can provide more context to the reviews.
Review Data: Includes both positive and negative reviews, providing a balanced view of each hotel.
Review Date: Helps in analyzing the timeliness of feedback.
Reviewer Nationality: Allows for insights into how different demographics perceive the hotel experience.
Negative and Positive Review Content: Used to gauge overall sentiment and key pain points or highlights mentioned by guests.
Review Word Counts: Tracks the verbosity of reviews, often correlating with the intensity of opinions.
Total Reviews: Helps assess the popularity and overall user experience with the hotel.
Reviewer Score: A direct measure of satisfaction, influencing the overall recommendation.
Geographic Coordinates (lat, lng): Enables location-based filtering and proximity analysis.
Sentiment Analysis: Through analyzing positive and negative reviews, the system can identify common trends and potential areas for hotel improvement.
Reviewer Demographics: Insights into how different nationalities perceive their stays can help hotels cater to a global audience more effectively.
Location-Based Recommendations: By incorporating latitude and longitude, the model can suggest hotels near key attractions or business centers, enhancing user convenience.
Review Dynamics: The temporal aspect of reviews, coupled with the number of reviews, provides a sense of how a hotel’s reputation evolves over time.
This project not only showcases your ability to work with diverse data types but also your capability to extract actionable insights from unstructured data, which is crucial in many data science roles. The combination of sentiment analysis, demographic segmentation, and location-based filtering demonstrates a comprehensive approach to problem-solving.
The hotel recommendation system project is a testament to your data science skills, particularly in natural language processing, sentiment analysis, and geospatial data handling. These skills are highly valued by recruiters and can set you apart in the job market. Hotel Recommendation System
This project involves building a sophisticated hotel recommendation system based on various features such as hotel name, location, and detailed review data. The model aims to provide accurate and personalized hotel recommendations for users, making travel planning more efficient and enjoyable.
Hotel Name and Location: Critical identifiers for each hotel, helping to narrow down recommendations based on user preferences.
Additional Number of Scoring: Reflects the volume of additional ratings that can provide more context to the reviews.
Review Data: Includes both positive and negative reviews, providing a balanced view of each hotel.
Review Date: Helps in analyzing the timeliness of feedback.
Reviewer Nationality: Allows for insights into how different demographics perceive the hotel experience.
Negative and Positive Review Content: Used to gauge overall sentiment and key pain points or highlights mentioned by guests.
Review Word Counts: Tracks the verbosity of reviews, often correlating with the intensity of opinions.
Total Reviews: Helps assess the popularity and overall user experience with the hotel.
Reviewer Score: A direct measure of satisfaction, influencing the overall recommendation.
Geographic Coordinates (lat, lng): Enables location-based filtering and proximity analysis.
Sentiment Analysis: Through analyzing positive and negative reviews, the system can identify common trends and potential areas for hotel improvement.
Reviewer Demographics: Insights into how different nationalities perceive their stays can help hotels cater to a global audience more effectively.
Location-Based Recommendations: By incorporating latitude and longitude, the model can suggest hotels near key attractions or business centers, enhancing user convenience.
Review Dynamics: The temporal aspect of reviews, coupled with the number of reviews, provides a sense of how a hotel’s reputation evolves over time.
This project not only showcases my ability to work with diverse data types but also your capability to extract actionable insights from unstructured data, which is crucial in many data science roles. The combination of sentiment analysis, demographic segmentation, and location-based filtering demonstrates a comprehensive approach to problem-solving.
The hotel recommendation system project is a testament to my data science skills, particularly in natural language processing, sentiment analysis, and geospatial data handling. These skills are highly valued by recruiters and can set you apart in the job market.