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abstract: 'We investigate machine learning (ML) techniques for predicting the number of galaxies ($N_{gal}$) that occupy a halo, given the halo’s properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for analyses of large-scale structure. The ML techniques proposed here distinguish themselves from traditional halo occupation distribution (HOD) modeling as they do not assume a prescribed relationship between halo properties and $N_{gal}$. In addition, our ML approaches are only dependent on parent halo properties (like HOD methods), which are advantageous over subhalo-based approaches as identifying subhalos correctly is difficult. We test 2 algorithms: support vector machines (SVM) and k-nearest-neighbour (kNN) regression. We take galaxies and halos from the Millennium simulation and predict $N_{gal}$ by training our algorithms on the following 6 halo properties: number of particles, $M_{200}$, $\sigma_v$, $v_{max}$, half-mass radius and spin. For Millennium, our predicted $N_{gal}$ values have a mean-squared-error (MSE) of $\sim0.16$ for both SVM and kNN. Our predictions match the overall distribution of halos reasonably well and the galaxy correlation function at large scales to $\sim5-10\%$. In addition, we demonstrate a feature selection algorithm to isolate the halo parameters that are most predictive, a useful technique for understanding the mapping 