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Faulty K Roger Retails Data
Here's what it's all about: This presentation highlights an issue with K Roger's data stewardship. The data contains numerous anomalies, compromising its quality (DQ Dimension). These anomalies prevent us from using the data for trend analysis. To proceed, we need to address these data quality issues. Resolving the root cause of the anomalies (RCA) is crucial. A clear RACI matrix for the RCA process ensures everyone has defined roles. Defined roles (Responsible, Accountable, Consulted, Informed) lead to efficient problem-solving. Addressing the anomalies will ultimately improve data quality. This, in turn, allows for reliable trend analysis using the data. With accurate data, we can build more dependable models.
Daily Facebook Political Ad Spending
As of July 17, 2024
Learn R: ANOVAs
Random Forest and Logistic Regression
In this project, I will use two machine learning models, Random Forest and Logistic Regression, to predict heart disease. This is a simple model where I use all available variables to make the prediction. I won’t delve too deeply into the problem beyond the estimation part. In reality, establishing causal inference requires more subject expertise and looking beyond the data for analytics. However, the goal today is to demonstrate and practice running these two machine learning models.
two-parameters
P421- Simple Regression HW
Running a simple regression on the eammi2 data.
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Referencia
Referencia entre las unidades de primer nivel a las de segundo nivel, generales y comunitarios. Con la información podemos determinar la cobertura de servicio y la población que atiende.
qmetc