This presentation details the exploration of various machine learning algorithms/models, which are tested on different market research studies collected using face-to-face (F2F) data collection in East African markets. The supervised machine learning techniques such as Decision Tree, Random Forest, Gradient Boosting Machine (GBM), Deep Learning and unsupervised machine learning techniques such as K-means clustering and Isolation Random Forest have been explored. The results are very promising and show great potential to bring such AI-based techniques to mainstream data quality control and quality assurance, as well as to address some of the key challenges faced by F2F data collection, which is still prominent in emerging markets, such as (East) Africa.