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By using the data from assembly choices 2019, we present a process grounded on Feature Engineering and a set of machine learning algorithms to assay and compare the performance by accessing the same dataset. The general principles therefore, won't only aid analysts and intelligencers to present their preceptions more frequently and effectively but also empower the readers, depending on their position of interest and communal engagement to go beyond what is presented and to discover new Preceptivity for themselves.
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By using the data from assembly choices 2019, we present a process grounded on Feature Engineering and a set of machine learning algorithms to assay and compare the performance by accessing the same dataset. The general principles therefore, won't only aid analysts and intelligencers to present their preceptions more frequently and effectively but also empower the readers, depending on their position of interest and communal engagement to go beyond what is presented and to discover new Preceptivity for themselves.