Projects

Behigu Gizachew

Highlights

Smart Ad A/B Testing

A/B testing is a user experience research methodology. A/B tests consist of a randomized experiment with two variants, A and B., which are identical except for one variation that might affect a user's behavior. It includes application of statistical hypothesis testing or "two-sample hypothesis testing" as used in the field of statistic

A/B TESTING

Metric Choice:

  • Invariant metrics-Used this to ensure that the experiment (the way we presented a change to a part of the population )is not inherently wrong. eg number of users in both groups

  • Evaluation metrics-metrics we expect to change and are relevant to the goals we aim to achieve eg (brand awareness) Hypothesis testing for A/B testing

  • We use hypothesis testing to test the two hypotheses: Null Hypothesis :There is no difference in brand awareness between the exposed and control groups in the current case. Alternative Hypothesis:There is a difference in brand awareness between the exposed and control groups in the current case.

Machine Learning

  • Carried out 3 types of classification analysis to predict whether a user responds yes to brand awareness,namely: Logistic Regression Decision Trees XGboost ,then compared the different classification models to assess the best performing one(s).


Pharmacy sales prediction


An SIR model is an epidemiological model that computes the theoretical number of people infected with a contagious illness in a closed population over time,representing susceptible(S), infected (I), and removed or recovered (R).

APPROACH

  • Combined the SIR model with Bayesian parameter inference and augmented the model with a time-dependent spreading rate. The time dependence was implemented as potential change points in the spreading rate, which we assume to be driven by governmental interventions and the associated change of individual behavior (nonpharmaceutical interventions).

  • On the basis of three distinct measures taken in Rwanda, we detected three corresponding change points in the reported COVID-19 case numbers

  • For this project,the framework is designed to infer the effectiveness of past measures and to explore potential future scenarios, along with propagating the respective uncertainties.

Metrics:

  • Growth/spread Rate-Plotting this against time gave insights on changes in the trend with respect to interventions at particular points.