SafariDine & Stay: Kenya's Culinary and Lodging Navigator

Introduction

"SafariDine & Stay: Kenya's Culinary and Lodging Navigator" is an innovative project that combines technology and gastronomy to create a personalized guide for travelers exploring Kenya. Using data and advanced machine learning, the system tailors recommendations based on individual tastes, budgets, and location preferences, ensuring a memorable dining and lodging experience. This project aims to celebrate Kenya's diverse culinary and hospitality landscape, simplifying the process of choosing restaurants and hotels while fostering a deeper connection between travelers and the nation's food culture and lodging options. It's a journey to offer the perfect plate and a cozy bed for every traveler's dream.

Business Understanding

Problem Statement

Tourists in Kenya face the challenge of finding restaurants and hotels that align with their dining and lodging preferences, budget limitations, and desired locations. This results in subpar dining experiences and uncomfortable stays, ultimately hindering overall trip satisfaction.

Key Components

User Profile: Collects user data, including dining and lodging preferences, location, budget, and ratings.

Restaurant and Hotel Databases: Store information on various establishments, including menus, cuisines, ratings, reviews, and pricing.

Recommendation Engine: Uses machine learning to generate personalized recommendations.

User Interface: Provides a user-friendly website or mobile app platform for interaction.

Business Benefits

• Improved Customer Experience

• Increased Revenue

• Enhanced Operational Efficiency

• Data Insights

Data Understanding

Merging Restaurant and Hotel Data We've combined scraped data from TripAdvisor and Booking.com to provide a complete travel experience in Kenya.

Data Sources

• TripAdvisor (Restaurant Data)
• Booking.com (Hotel Data)

Column Descriptions for the Combined Dataset

id: Unique identifier for establishments.

category: Indicates whether it is a restaurant or a hotel.

name: Name of the establishment, restaurant or hotel.

latitude and longitude: Geographic coordinates.

rating: Average rating based on user reviews.

numberOfReviews: Total reviews.

lowerPrice and upperPrice: Price range.

rawRanking: Ranking score.

website: Website URL.

cuisine: Cuisine type (for restaurants).

features: Special amenities (for hotels).

phone: Contact phone.

locationString: Location description.

Town: Location of the establishment.

Conclusions

Primary Objective Achievement - The primary objective of creating a hotel and restaurant recommender system was successfully achieved and deployed.

Model Performance - The model had a Mean RMSE of 0.75 and Mean MAE of 0.48, indicating good performance.

User Experience - The system offers a highly satisfying user experience, with personalization, ease of use, and comprehensive information.

Challenges and Limitations - Data scarcity and the absence of user-based information presented challenges.

Recommendations

• Continuous Improvement: Keep refining the recommender system algorithms to provide even more accurate and relevant recommendations. This may involve incorporating additional user data, improving feature engineering, or exploring advanced machine learning techniques.

• Feedback Mechanism: Implement a feedback mechanism that allows users to provide input on recommended hotels and restaurants. This can help in fine-tuning recommendations and addressing any mismatches.

• Mobile App Development: consider developing a mobile app for the recommender system to make it more accessible to users on the go.

• Integration with Booking Services: Explore partnerships or integrations with hotel and restaurant booking platforms, allowing users to seamlessly transition from recommendations to bookings.

• User Engagement Strategies: Develop strategies to keep users engaged with the platform even when they are not actively seeking recommendations. This could include content marketing, loyalty programs, or user-generated content features.

• Privacy and Data Security: Ensure that user data is handled securely and in compliance with privacy regulations. Transparency in data usage is crucial to building trust with users.

• Marketing and Promotion: Leverage the insights from user data to run targeted marketing campaigns for hotels and restaurants. Encourage establishments to offer exclusive deals and promotions to users of the recommender system.

• User Education: Provide resources and guides to help users make the most of the recommender system, including how to provide feedback, adjust preferences, and use the platform effectively. Regular Updates: Keep the system up to date with the latest information about hotels and restaurants, including changes in menus, prices, and user reviews.

• Monitor and Measure: Continuously monitor the performance of the recommender system through key metrics like user engagement, retention rates, and user feedback. Use these metrics to assess the impact and effectiveness of the system's recommendations.

• Expand Coverage: Consider expanding the coverage of the recommender system to include a wider range of restaurants and hotels in different locations to cater to a broader user base.

• Competitive Analysis: Keep an eye on competitors and their recommendation systems to stay ahead in the market and adapt to changing user preferences.