A Decision Tree Based Recommendation System for Tourists

Project Code :TCMAPY579

Objective

The main goal of this research is to figure out which Travel Recommendation Systems are which best place in town. To know that, we applied classification techniques of Random Forest, XGBoost, and Gradient Boosting.

Abstract

Choosing a tourist destination from the information that is available on the Internet and through other sources is one of the most complex tasks for tourists when planning travel, both before and during travel. Previous Travel Recommendation Systems (TRSs) have attempted to solve this problem. However, some of the technical aspects such as system accuracy and the practical aspects such as usability and satisfaction have been neglected. To address this issue, it requires a full understanding of the tourists’ decision-making and novel models for their information search process. This paper proposes a novel human-centric TRS that recommends destinations to tourists in an unfamiliar city. It considers both technical and practical aspects using a real. The system is developed using a two-steps feature selection method to reduce number of inputs to the system and recommendations are provided by decision tree C4.5. The experimental results show that the proposed TRS can provide personalized recommendation on tourist destinations that satisfy the tourists.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

Hardware:

Operating system                    :  Windows 7 or 7+

RAM                                       :  8 GB

Hard disc or SSD                    :  More than 500 GB  

Processor                                 :  Intel 3rd generation or high or Ryzen with 8 GB Ram

Software:

Software’s                               :  Python 3.6 or high version

IDE                                          :  PyCharm.

Framework                              :  Flask

Learning Outcomes

LEARNING OUTCOMES:           

·         About Classification in machine learning.

·         About preprocessing techniques.

·         About XGBoost.

·         About Random Forest.

·         About Gradient Boosting.

·         Knowledge on PyCharm Editor.

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