<prompt> Hlalosa mehato e mehlano ea tsebetso ea analytics ea ho bolela esale pele.nan  <response>: Instruction in English: Explain the five steps of a predictive analytics process. . Response in English: Predictive analytics is a process that involves using statistical techniques, machine learning algorithms and data mining to analyze historical data and make predictions about future events or trends. The five main steps in a predictive analytics process are:1. Data Collection: The first step in predictive analytics is to gather data from multiple sources such as transactional data, customer behavior, and demographics. This data is then combined, cleaned, transformed and pre-processed to create a consistent dataset that can be used for analysis.2. Data Analysis: Once the data has been collected, it is analyzed using statistical techniques, machine learning algorithms, and data mining methods to identify patterns, relationships, and trends in the data. 3. Model Development: Based on the insights obtained from the data analysis, a predictive model is developed. This model uses mathematical algorithms to make predictions about future events or trends based on the data. The model is then refined and fine-tuned to improve its accuracy.4. Model validation: The next step in the process is to validate the predictive model. This is done by comparing the predictions made by the model to actual data to determine how well the model performs in predicting future trends or events. If the models predictions are not accurate, it is refined and retested until it meets the desired level of accuracy.5. Deployment: Once the model has been validated and is found to be accurate, it is deployed for use in making predictions. The model can be integrated into a companys business processes, allowing it to make data-driven decisions that can help improve its performance and profitability. Additionally, the model is continuously monitored and refined to ensure its accuracy over time. . Response in Sesotho: Predictive analytics ke tsebetso e kenyelletsang ho sebelisa mekhoa ea lipalo-palo, li-algorithms tsa ho ithuta ka mochini le merafo ea data ho sekaseka lintlha tsa nalane le ho bolela esale pele ka liketsahalo kapa mekhoa e tlang. Mehato e mehlano e ka sehloohong ea tsebetso ea li-analytics tsa ho bolela esale pele ke: 1. Pokello ea Lintlha: Mohato oa pele oa li-analytics tse boletsoeng esale pele ke ho bokella lintlha ho tsoa mehloling e mengata e kang data ea transaction, boitšoaro ba bareki, le palo ea batho. Joale data ena e kopanngoa, ea hloekisoa, ea fetoloa le ho sebetsoa esale pele ho theha dataset e tsitsitseng e ka sebelisoang bakeng sa tlhahlobo. 2. Tlhahlobo ea Boitsebiso: Hang ha boitsebiso bo bokelloa, bo hlahlojoa ho sebelisoa mekhoa ea lipalo-palo, li-algorithms tsa ho ithuta mochine, le mekhoa ea merafo ea data ho khetholla mekhoa, likamano le mekhoa ea boitsebiso. 3. Ntšetso-pele ea Mohlala: Ho itšetlehile ka lintlha tse fumanoeng ho tsoa tlhahlobo ea lintlha, ho etsoa mohlala oa ho bolela esale pele. Moetso ona o sebelisa li-algorithms tsa lipalo ho bolela esale pele ka liketsahalo tsa kamoso kapa mekhoa e ipapisitseng le data. Joale mohlala oa hloekisoa le ho lokisoa hantle ho ntlafatsa ho nepahala ha oona. 4. Netefatso ea mohlala: Mohato o latelang tsebetsong ke ho netefatsa mohlala o boletsoeng esale pele. Sena se etsoa ka ho bapisa likhakanyo tse entsoeng ke mohlala ho data ea sebele ho fumana hore na mohlala o sebetsa hantle hakae ho bolela esale pele mekhoa kapa liketsahalo tse tlang. Haeba likhakanyo tsa mohlala li sa nepahala, li hloekisoa le ho hlahlojoa hape ho fihlela li kopana le boemo bo lakatsehang ba ho nepahala. 5. Phatlalatso: Hang ha mohlala o netefalitsoe mme o fumanoa o nepahetse, o sebelisoa ho sebelisoa ho etsa likhakanyo. Mohlala o ka kopanngoa le mekhoa ea khoebo ea khamphani, e lumella hore e etse liqeto tse tsamaisoang ke data tse ka thusang ho ntlafatsa tshebetso le phaello ea eona. Ho feta moo, mohlala o lula o behiloe leihlo le ho ntlafatsoa ho netefatsa ho nepahala ha nako. 