For this Project, imagine you have been hired by a large German bank as a data scientist in their business analytics unit.
The bank wants to use its historical loan data to predict the credit rating (risk) of new loan prospects. Using these credit-rating predictions, the bank can make more informed loan approval or denial decisions that preserve the bank’s assets and reduce its loan defaults.
The bank collects historical data about the loans it extended to its customers. This historical data includes the final classification of the loans. There are two classifications:
• If the loan has been fully paid off, then the loan is classified as good credit rating (low risk to the bank).
• If the loan has defaulted (not paid off), then the loan is classified as bad credit rating (high risk to the bank).
Your first task at the bank is to use the bank’s historical data to design, build, and evaluate an ensemble prediction model to predict the credit rating of new loan prospects. The ensemble model should include at least two different classification algorithms. You are free to use any of the ensemble methods (voting, bagging, boosting, or random forest) for your model.
One of the important challenges in the manufacture of dental ceramics is the production of a compound with sufficient strength but keeping a natural translucency. When a significant reconstruction of teeth is required, the material used in the reconstruction should provide a mechanical performance compatible with the tooth function without compromising the appearance of the normal tooth. In these cases, ceramics are the preferred materials in dentistry. [Christensen 2007, Goznelli et al. 2013, Kidd et al. 2003, Qualtrough et al. 2005, Robertson et al. 2006, Summit et al. 2000]. Dental ceramics are generally hard and brittle, with a range of thermal and electrical conductivity compatible with oral conditions and are typically chemically stable [Carter et al. 2007, Chu et al. 2005, Heimann 2010, McLaren et al. 2009]. Changes in microstructure of the ceramics can be used to modify, and usually improve, the mechanical properties of dental ceramics [Gonzaga et al. 2011, Xie at al. 1999]. The simplest way to modify the microstructure of a ceramic is using a different sintering process, such as microwave hybrid sintering (MHS), a process in which sintering is achieved using microwaves instead of a radiating or convective heating, with several advantages. Dental ceramics are usually transparent to microwaves, and therefore MHS is achieved using external heating elements (in our experiments, silicon carbide susceptors), to heat the ceramic material up to a temperature at which the ceramics couple with the microwaves, starting a volumetric heating phenomena (thermal energy produces across the whole sample, not from the surface to the core as in conventional heating systems)[Almazdi et al. 2012, Kimrey et al. 1991, Krage et al. 1981, Kashi 2010, Menezes et al. 2012]. MHS allows shorter processing times, with reduced energy consumption and improvement of mechanical properties (bending strength, hardness) [Almazdi et al. 2012, Agrawal 1998, Agrawal 2006, Clark et al. 2000, Kashi et al. 2005, Kashi et al. 2008, Ma et al. 2007, Menezes et al. 2007, Menezes et al. 2012, Pendola and Saha 2015, Upadhyay et al. 2001]. ———————————Figure 1———————————— Although MHS seems a good alternative to improve the sintering process of dental ceramics [Kashi et al. 2005, Kashi et al 2008, Pendola and Saha 2015], the impact of MHS in the clinical life of dental ceramics is one of the most important elements to test whether MHS is a convenient method to produce dental >GET ANSWER