This work explores association rule mining, specifically focusing on the Apriori algorithm and the use of Bootstrap for enhancing the robustness of the results. The study explains how Apriori identifies frequent item sets and generates association rules based on support and confidence metrics, with the concept of "Lift" used to measure the strength of item relationships. The dataset used for the analysis is the Groceries dataset, where the algorithm identifies frequent item combinations like "if buying milk, then buying bread." The paper also demonstrates the application of Bootstrap to improve reliability and mitigate errors, ensuring that the identified rules are applicable to new datasets. Lastly, the work highlights the visual representation of results through tables and graphs to interpret the findings more effectively

The dataset has information about humanitarian and development activities carried out by various organizations in Uganda to provide insights into the types of projects, their funding sources, sectors of focus, and temporal distribution of activities.

Testing to see if this works

Ajuste estacional actividad económica

Seasonal Adj

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