Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

A prediction model to detect non-compliant taxpayers using a supervised machine learning approach: evidence from Tunisia

Domain:

socioeconomic

Record type:

paper
Creator:
AicRahSao
Host:avatar

This study aims to develop a tax non-compliance prediction model in Tunisia using supervised machine learning algorithms. A data mining analysis was conducted following the Knowledge Discovery in Databases (KDD) process, utilizing a dataset of 20,930 labeled observations from 2013 to 2017, comprising 110 attributes. We employed supervised learning algorithms, including K-Nearest Neighbors, Decision Trees, Naïve Bayes, Gradient Boosting, and Random Forest, to identify the most accurate model. Notably, Random Forest outperformed the other algorithms, achieving a prediction accuracy of 83%. Furthermore, through a combined interpretation of feature importance derived from Random Forest, SHAP value analysis, and ANOVA, our findings provide tax auditors with insights into the most influential attributes for predicting tax non-compliance. This study holds significant practical implications by enhancing the efficiency of tax audits and supporting tax authorities in their efforts to combat tax non-compliance.

Visit

figshare.com

Tasks

text classification

Tags

Plant BiologyVirologyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedTax non-complianceTunisian tax authoritiesdata miningKDDsupervised learning algorithms+1

Licenses

CC BY 4.0

Similar

Solar Energy Prediction in Algeria: A Supervised Machine Learning ApproachA Machine Learning Based Prediction Approach To Non-Communicable Diseases InterventionImproving retention: predicting ART retention among PLHIV using a supervised machine learning approachFine-Tuning a Machine Learning Model to Detect Covid-19 Misinformation in XitsongaDeveloping a Flight Price Prediction Model Using Ensemble Machine Learning: A Case Study of Ethiopian Airlines Developing Flight Price Prediction Model Using Ensemble Machine LearningMachine Learning Models for Climate Prediction and Adaptation Planning in Tunisia: A Methodological Approach

Solar Energy Prediction in Algeria: A Supervised Machine Learning Approach

This research investigates the influence of climatic variables on photovoltaic (PV) module performan

A Machine Learning Based Prediction Approach To Non-Communicable Diseases Intervention

This study aims to utilize machine learning techniques to predict Non-Communicable Diseases (NCDs) i

Improving retention: predicting ART retention among PLHIV using a supervised machine learning approach

Poor ART care retention due to loss to follow up have dire clinical consequence including poor treat

Fine-Tuning a Machine Learning Model to Detect Covid-19 Misinformation in Xitsonga

Developing a Flight Price Prediction Model Using Ensemble Machine Learning: A Case Study of Ethiopian Airlines Developing Flight Price Prediction Model Using Ensemble Machine Learning

We have attached the below list of data and model development processes: ·    Processed and aggrega

Machine Learning Models for Climate Prediction and Adaptation Planning in Tunisia: A Methodological Approach

Climate change poses significant challenges to urban planning in Tunisia, necessitating pre