<prompt> Melemo ea ho sebelisa marang-rang a neural bakeng sa analytics ea pele ke efe?nan  <response>: Instruction in English: What are the advantages of using neural networks for predictive analytics? . Response in English: Neural networks have several advantages when used for predictive analytics, including:1. Non-Linear Modeling: Neural networks excel at modelling complex, non-linear relationships between inputs and outputs. This enables them to accurately capture subtle patterns and trends in data and make more accurate predictions.2. Adaptability: Neural networks are highly adaptable, and can learn to improve their predictive accuracy over time. This is especially useful for predictive analytics, where data can change frequently and models need to be continually refined.3. Multi-Dimensional Data Handling: Neural networks are capable of processing and analyzing large and complex datasets with many variables. This makes them well-suited for big data applications and predictive analytics where large quantities of data are often involved.4. Robustness: Neural networks are remarkably robust to noisy, incomplete or corrupted data. They are able to learn and generalize from such datasets and still provide reliable predictions.5. Parallel Processing: The architecture of neural networks allows for parallel processing, which speeds up computations and allows for real-time predictions. This is a valuable characteristic for predictive analytics applications where timeliness of predictions is crucial. . Response in Sesotho: Marang-rang a Neural a na le melemo e maloa ha a sebelisoa bakeng sa li-analytics tse boletsoeng esale pele, ho kenyelletsa le: 1. Non-Linear Modeling: Li-network tsa Neural li ipabola ho li-modeling complex, likamano tse se nang moeli pakeng tsa lintho tse kenang le tse hlahisoang. Sena se ba nolofalletsa ho hapa mekhoa e poteletseng le mekhoa ea data ka nepo le ho etsa likhakanyo tse nepahetseng haholoanyane. 2. Ho ikamahanya le maemo: Marang-rang a Neural a fetoha haholo, me a ka ithuta ho ntlafatsa ho nepahala ha bona ho bolela esale pele ka nako. Sena se bohlokoa haholo bakeng sa li-analytics tsa ponelopele, moo data e ka fetohang khafetsa le mehlala e hlokang ho ntlafatsoa khafetsa. 3. Multi-Dimensional Data Handling: Marang-rang a Neural a khona ho sebetsana le ho hlahloba li-dataset tse kholo le tse rarahaneng tse nang le mefuta e mengata. Sena se etsa hore li tšoanelehe hantle bakeng sa litsebetso tse kholo tsa data le li-analytics tse boletsoeng esale pele moo hangata ho amehang lintlha tse ngata. 4. Matla: Marang-rang a Neural a matla ka mokhoa o makatsang ho data e lerata, e sa fellang kapa e senyehileng. Ba khona ho ithuta le ho akaretsa ho tsoa ho li-dataset tse joalo mme ba ntse ba fana ka likhakanyo tse tšepahalang. 5. Parallel Processing: Mehaho ea marang-rang a marang-rang e lumella ho sebetsa ka mokhoa o tsoanang, o potlakisang lipalo le ho lumella ho bolela esale pele ka nako ea sebele. Ena ke tšobotsi ea bohlokoa bakeng sa litsebetso tsa ponelopele ea litlhahlobo moo nako ea likhakanyo e leng bohlokoa. 