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Used Python and the CRISP-DM methodology to analyze visitor patterns at Portuguese attractions and compare them with key competitors. Our study includes visitor associations, attraction similarities, and visitor segmentation using RFM analysis, offering strategic insights for the Portuguese tourism sector.

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JoaoDias13/National-tourism-promotion---Data-Science-for-Marketing

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4 Commits
1. Business Understanding.ipynb
1. Business Understanding.ipynb
 
 
2. Sentiment & Time Series Analysis.ipynb
2. Sentiment & Time Series Analysis.ipynb
 
 
3. Data understanding & preparation.ipynb
3. Data understanding & preparation.ipynb
 
 
4. Data_Preparation_Geo_Clusters.ipynb
4. Data_Preparation_Geo_Clusters.ipynb
 
 
5. Modeling&Evaluation (New variables + Analysis + RFM).ipynb
5. Modeling&Evaluation (New variables + Analysis + RFM).ipynb
 
 
6. Apriori_Similarity.ipynb
6. Apriori_Similarity.ipynb
 
 
DS4MKT - Presentation - Group 12.pdf
DS4MKT - Presentation - Group 12.pdf
 
 
Data Science - Report -Group 12.pdf
Data Science - Report -Group 12.pdf
 
 
EuropeTop100Attractions_ENG_20190101_20210821.xlsx
EuropeTop100Attractions_ENG_20190101_20210821.xlsx
 
 
Holidays.csv
Holidays.csv
 
 
README.md
README.md
 
 

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Project Description

This project analyzes the impact of the COVID-19 pandemic on tourism in Europe, with a specific focus on Portugal and its main tourism competitors. Utilizing the CRISP-DM process model, we serve as consultants for the Portuguese National Tourism Board Organization (NTBO), analyzing user-generated content from TripAdvisor reviews to identify and understand visitor patterns pre- and post-pandemic.

Objectives

  • Characterize and describe visitor patterns at Portuguese attractions.
  • Compare these patterns with those of Portugal's main tourism competitors.
  • Analyze visitor associations, similarities between attractions or visitors, and segment visitors using RFM analysis (with ratings instead of monetary values).

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Used Python and the CRISP-DM methodology to analyze visitor patterns at Portuguese attractions and compare them with key competitors. Our study includes visitor associations, attraction similarities, and visitor segmentation using RFM analysis, offering strategic insights for the Portuguese tourism sector.

Topics

data-science clustering similarity apriori-algorithm turismo rfm-analysis

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