<prompt> Tloo le algorithm ea ho bolela esale pele litheko tsa ntlo toropong e fanoeng.nan  <response>: Instruction in English: Come up with an algorithm for predicting house prices in a given city. . Response in English: An algorithm for predicting house prices in a given city can be designed as follows:1. Data Collection: The first step is to gather data about house prices and their features in the given city. The data should include a comprehensive list of home characteristics such as square footage, the number of bedrooms, bathrooms, location, age, etc., along with their respective selling prices. This data can be collected from various sources such as real estate websites, government records or data providing companies that specialize in real estate data.2. Data Preprocessing: After collecting the data, the next step is to clean and preprocess it. This step involves handling missing or invalid values, removing duplicates, converting text data into numerical values and normalizing the data. Preprocessing ensures that the data is in a format suitable for further analysis.3. Feature Selection: This step involves selecting the most important features that can affect the selling price of a house. Feature selection can be performed using techniques such as correlation analysis, mutual information, chi-square test, or by using machine learning algorithms such as decision trees or random forests. This step is important because it helps us to narrow down the factors that are most likely to impact the target variable house price and to remove any redundant or irrelevant features.4. Model Selection: Next, we need to choose an appropriate model to make predictions. This can be done by splitting the dataset into a training and test set, and then training different models on the training data. Some of the commonly used models for predicting house prices are linear regression, decision trees, random forests, gradient boosting, and neural networks. Each model has its own strengths and weaknesses, and the best model is the one that gives the lowest prediction error on the test data.5. Model Training: In this step, the selected model is trained on the entire dataset, and its parameters are tuned to get the best possible performance.6. Making Predictions: Once the model is trained, it can be used to make predictions on new data. The model will take in the characteristics of a house and generate an output that is an estimate of its selling price.7. Model Evaluation: Finally, the models performance needs to be evaluated regularly to ensure that it is still providing accurate predictions. This can be done by comparing the predicted prices with the actual selling prices of houses that were not included in the training dataset. If the models performance degrades over time, it may need to be re . Response in Sesotho: Algorithm bakeng sa ho bolela esale pele litheko tsa ntlo teropong e itseng e ka raloa ka tsela e latelang: 1. Pokello ea Lintlha: Mohato oa pele ke ho bokella lintlha tse mabapi le litheko tsa matlo le likarolo tsa tsona toropong e fanoeng. Lintlha li lokela ho kenyelletsa lethathamo le felletseng la litšobotsi tsa lehae joalo ka sekwere footage, palo ea likamore, likamore tsa ho hlapa, sebaka, lilemo, joalo-joalo, hammoho le litheko tsa tsona tsa thekiso. Lintlha tsena li ka bokelloa ho tsoa mehloling e fapaneng joalo ka liwebsaete tsa thekiso ea matlo, lirekoto tsa mmuso kapa data e fanang ka likhamphani tse sebetsanang le data ea thekiso ea matlo. 2. Tlhahiso-pele ea Boitsebiso: Kamora ho bokella lintlha, mohato o latelang ke ho li hloekisa le ho li lokisa esale pele. Mohato ona o kenyelletsa ho sebetsana le litekanyetso tse sieo kapa tse sa nepahaleng, ho tlosa tse kopitsoang, ho fetolela data ea mongolo hore e be lipalo tsa linomoro le ho etsa hore data e tloaelehe. Ho lokisa esale pele ho netefatsa hore data e ka sebopeho se loketseng tlhahlobo e tsoelang pele. 3. Khetho ea Litšobotsi: Mohato ona o kenyelletsa ho khetha likarolo tsa bohlokoahali tse ka amang theko ea thekiso ea ntlo. Khetho ea likarolo e ka etsoa ho sebelisoa mekhoa e kang tlhahlobo ea khokahano, tlhaiso-leseling, tlhahlobo ea chi-square, kapa ka ho sebelisa mekhoa ea ho ithuta ka mochini joalo ka lifate tsa liqeto kapa meru e sa reroang. Mohato ona o bohlokoa hobane o re thusa ho fokotsa lintlha tse ka nang tsa ama phetoho e reriloeng theko ea ntlo le ho tlosa likarolo leha e le life tse sa hlokahaleng kapa tse sa hlokahaleng. 4. Khetho ea Moetso: Ka mora moo, re hloka ho khetha mofuta o nepahetseng ho etsa likhakanyo. Sena se ka etsoa ka ho arola dataset hore e be setsi sa koetliso le tlhahlobo, ebe joale ho koetlisa mefuta e fapaneng ea data ea koetliso. E meng ea mefuta e sebelisoang haholo bakeng sa ho bolela esale pele litheko tsa ntlo ke ho theoha ha mela, lifate tsa liqeto, meru e sa reroang, ho eketseha ha likhahla, le marang-rang a methapo ea kutlo. Mohlala o mong le o mong o na le matla le mefokolo ea oona, me mohlala o motle ka ho fetisisa ke oona o fanang ka phoso e tlaase ka ho fetisisa ea ho bolela esale pele ho data ea teko. 5. Koetliso ea Mohlala: Mohato ona, mohlala o khethiloeng o koetlisetsoa ho dataset eohle, me mekhahlelo ea eona e lokisoa ho fumana tsebetso e ntle ka ho fetisisa. 6. Ho Etsa Likhakanyo: Hang ha mohlala o se o koetlisoa, o ka sebelisoa ho etsa likhakanyo ka lintlha tse ncha. Mohlala o tla nka litšobotsi tsa ntlo le ho hlahisa tlhahiso e leng khakanyo ea theko ea eona ea thekiso. 7. Tlhahlobo ea Mohlala: Qetellong, tsebetso ea mohlala e hloka ho hlahlojoa khafetsa ho netefatsa hore e ntse e fana ka likhakanyo tse nepahetseng. Sena se ka etsoa ka ho bapisa litheko tse boletsoeng esale pele le litheko tsa nete tsa thekiso ea matlo a neng a sa kenyelletsoa lethathamong la lithupelo. Haeba tsebetso ea mohlala e fokotseha ha nako e ntse e ea, e ka na ea hloka ho tsosolosoa 