2.4. Customs Revenues Prediction

Generation of Synthetic data using LLM API

Synthetic data: save money, time and carbon with open source

https://huggingface.co/blog/synthetic-data-save-costs

Themes for Customs Context

Inspecting less to inspect better: The use of data mining for risk management by customs administrations

https://ferdi.fr/dl/df-pBwYKbpMwnFKeaKVHJEPzwR5/ferdi-p46-inspecting-less-to-inspect-better-the-use-of-data-mining-for-risk.pdf

Risk management systems: using data mining in developing countries’ customs administrations Bertrand Laporte

https://worldcustomsjournal.org/Archives/Volume%205%2C%20Number%201%20(Mar%202011)/03%20Laporte.pdf

1) Uncertainty Quantification and Customs

https://www.youtube.com/watch?v=mduw_ROmYZ8

Risk models based on uncertainty quantification for illicit traffic time series in customs context (no free access, no free data set)

Bag-intel project

https://www.bag-intel.eu/

No free access, no free data set.

2) Ramadhany (article_2023). The Effect of Service Speed, Employee Integrity, and Employee Empathy...

https://www.youtube.com/watch?v=xl22sqiYS0A 

No free access, no free data set.

Product price predictions

Customs Revenues Prediction Using Ensemble Methods (Statistical Modelling vs Machine Learning)

Forecasting tax revenues using times series techniques - a case of Pakistan

https://www.tandfonline.com/doi/epdf/10.1080/1331677X.2018.1442236?needAccess=true 

Combining regression and clustering

https://dl.acm.org/doi/10.1145/3449726.3463141 

https://www.kaggle.com/datasets/harlfoxem/housesalesprediction?select=kc_house_data.csv 

Creating an Ensemble of Clustering Algorithms

https://pdfs.semanticscholar.org/a5c6/ad07374fb05d57eda005629a6474f0135bdd.pdf?_gl=1*1hqhkpo*_ga*MTk3ODY0OTQ2OC4xNzE1NTQyNzcx*_ga_H7P4ZT52H5*MTcxNTU0Mjc3MS4xLjEuMTcxNTU0MzE1Ny4xOC4wLjA

https://medium.com/@rais.sulaiman/stp-marketing-model-and-k-modes-clustering-for-segmentation-b98da17722fb

https://medium.com/@pratama.adib/customer-segmentation-with-rfm-and-python-1b8665baf6f4

https://www.kaggle.com/datasets/carrie1/ecommerce-data?select=data.csv

https://www.kaggle.com/code/farzadnekouei/customer-segmentation-recommendation-system

https://www.kaggle.com/code/allunia/e-commerce-sales-forecast

https://www.kaggle.com/code/fabiendaniel/customer-segmentation

https://medium.com/@rais.sulaiman/part-2-types-of-time-series-for-forecasting-naive-simple-average-and-moving-average-e431cd0f403e

https://medium.com/@mugeshk/time-series-forecasting-5b27a1b119c8

https://www.bounteous.com/insights/2020/09/15/forecasting-time-series-model-using-python-part-two

https://medium.com/@sampurn10chouksey/time-series-analysis-introduction-part-1-0bf2459cd958

https://medium.com/@sampurn10chouksey/time-series-analysis-example-with-python-part-2-6a7d9aab2be2

https://medium.com/@sampurn10chouksey/time-series-analysis-forecasting-with-python-part-3-c04c30c2e256

https://medium.com/@saibhargavkarnati/customer-segmentation-using-k-means-clustering-c55810070e59

More documents

Safeguarding demand forecasting with causal graphs

https://towardsdatascience.com/safeguarding-demand-forecasting-with-causal-graphs-591511fc8e0e

How to Handle Imbalanced Data for Machine Learning in Python

https://semaphoreci.medium.com/how-to-handle-imbalanced-data-for-machine-learning-in-python-b6d56c9a0489

Classical time-series forecasting methods in Python and R for beginners

https://medium.com/codex/classical-time-series-forecasting-methods-in-python-and-r-for-beginners-df0731b3199f