## 1 Introduction  
In the context of the intense development of the world, the demand for machinery, paint, paper, and plastic is virtually unending with titanium minerals labelled as one of the primary materials [1; 2; 3]. This commodity can be exploited from the placer and hard rocks deposits [4]. In particular, placer deposits are abundant sources of titanium for coastal countries and Vietnam is one of those countries [5]. They are considered to be economically valuable minerals because of their ease of exploitation and flexibility [6]. In titanium placer, the major mineral components are ilmenite, rutile, anatase, leucoxene, zircon, and monazite [7]. Of those minerals, ilmenite and rutile are normally considered as the main types containing titanium because of their high proportion in the total heavy minerals [8; 9]. Although the concentration of TiO\({}_{2}\) is higher in natural rutile than ilmenite, the distribution of rutile is normally inadequate for processing in titanium placer. Furthermore, sulfate and chloride processes can be applied as the chemical methods to obtain higher TiO\({}_{2}\) content [10; 11]. In other words, TiO\({}_{2}\) can be enriched from ilmenite by chemical methods [12; 13; 14; 15]. Therefore, ilmenite can be considered as the main mineral used to extract TiO\({}_{2}\) in titanium placer. In recent years, mineral exploration based on data-driven or artificial intelligence (AI) techniques is considered as a cost-effective alternative to traditional methods. The spatial and geochemical datasets have been analyzed by traditional analytical or AI approaches with high reliability [16; 17; 18; 19; 20]. Mineralization, as well as minerals, can be forecasted using these techniques to highlight mineral potential based on similar data attributes [21]. For example, the ant colony algorithm was performed to recognize the geochemical anomalies in the interpolated concentration of Au, Cu, Ag, Zn, and Pb by [PERSON], [PERSON] [22]. The artificial neural network (ANN), \(k\)-nearest neighbors (kNN) models were also developed to investigate the components of mineral of coal and maceral groups by [PERSON], [PERSON] [23]. To evaluate the relationship between mineral potentials as well as establish mineral prospectivity maps, [PERSON], [PERSON] [24] used ANN, fuzzy logic, and logistics regression as powerful tools for the mapping of potential gold deposits. [PERSON], [PERSON] [25] also deployed several machine learning (ML) techniques to identify geochemical anomalies of Fe polymetallic deposits. They concluded that ML techniques are robust tools for discovering multivariate geochemistry anomalies. In another study, [PERSON] et al. [18] used ANN with 96 data points to evaluate the distribution of geochemical of gold deposits at Canning Basin (Western Australia). Their results showed that ANN was a feasible technique for assessing geochemical property distribution with a determination coefficient (R\({}^{2}\)) of 0.8. To establish another approach to geochemical mapping, [PERSON] et al. [26] applied deep learning and indicated that this method could deal with nonlinear and complex problems. In addition, various ML techniques have been used to detect the content as well as the potential of minerals and geochemical anomalies as the following works [18; 25; 27; 28; 29; 30; 31; 32; 33; 34]. Review of the published works shows that the mapping of mineral distribution can be achieved by using data-driven techniques. Although a number of studies have been carried out to model several commodities, such as Fe, Au, Zn, Cu, there is still a scarcity of research on titanium in general and ilmenite in particular. Motivated from the significance of this type of mineral on the local economy [35], this study aimed at evaluating the feasibility of predicting it using different AI techniques.