<prompt> Koetlisa mofuta oa SVM bakeng sa pokello ea data e fanoeng.nan  <response>: Instruction in English: Train a SVM model for the given dataset. . Response in English: Okay, lets get started on training an SVM model for your dataset. Here are the steps to follow:1. First, youll need to preprocess your dataset. This will include steps like dealing with missing values, encoding categorical variables, and scaling numerical variables. You should only perform these steps on the training set and later apply the same transformation to the test set.2. Once your data is preprocessed, youll need to split it into a training and test set. This can be done using the train_test_split function from the sklearn.model_selection module.pythonfrom sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test train_test_splitX, y, test_size0.2, random_state0Make sure to set the random_state parameter to ensure reproducibility of your results.3. Next, lets import the SVM classifier from sklearn.svm and create an instance of the classifier.pythonfrom sklearn.svm import SVCclassifier SVCkernellinear, random_state0Here, were using a linear kernel which works well for many classification problems. You can experiment with other kernels like rbf and poly and see if they work better for your dataset.4. Now, lets fit the classifier to the training data.pythonclassifier.fitX_train, y_train5. Once the model is trained, you can make predictions on the test set and evaluate the performance of the model.pythonfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_reporty_pred classifier.predictX_testprintAccuracy:, accuracy_scorey_test, y_predprintConfusion Matrix:, confusion_matrixy_test, y_predprintClassification Report:, classification_reporty_test, y_predThis is a basic workflow for training an SVM model. You can experiment with different parameters, kernels, and data preprocessing techniques to improve the performance of the model. . Response in Sesotho: Ho lokile, ha re qaleng ka ho koetlisa mohlala oa SVM bakeng sa datha tsa hau. Mehato eo u lokelang ho e latela ke ena: 1. Pele, u tla hloka ho lokisa datha ea hau esale pele. Sena se tla kenyelletsa mehato e kang ho sebetsana le litekanyetso tse sieo, li-encoding variables categorical, le ho lekanya lipalo-palo. U lokela ho etsa mehato ena feela sethaleng sa koetliso me hamorao u sebelise phetoho e tšoanang ho sete ea liteko. 2. Hang ha data ea hau e se e hlophisitsoe, u tla hloka ho e arola ka sete ea koetliso le tlhahlobo. Sena se ka etsoa ka mosebetsi oa train_test_split ho tsoa mojuleng oa sklearn.model_selection. python ho tsoa ho sklearn.model_selection train_test_split X_train, X_test, y_train, y_test train_test_splitX, y, test_size0.2, random_state0 Etsa bonnete ba hore o seta paramethara ea maemo_e sa reroang ho netefatsa hore liphetho tsa hau li hlahisoa hape . 3. Ka mora moo, ha re ke re latang sehlopha sa SVM ho tsoa sklearn.svm me re thehe mohlala oa mokhethoa. python ho tsoa ho sklearn.svm import SVC classifier SVCkernellinear, random_state0 Mona, re sebelisa kernel e mela e sebetsang hantle bakeng sa mathata a mangata a lihlopha. U ka leka lithollo tse ling tse kang rbf le poly me u bone hore na li sebetsa betere bakeng sa datha tsa hau. 4. Joale, ha re kenyeng classifier ho data koetliso. python classifier.fitX_train, y_train 5. Hang ha mmotlolo o kwetlisitswe, o ka etsa boitsebelopele ka sete ya teko mme wa lekola tshebetso ya mmotlolo. python ho tsoa ho sklearn.metrics import accuracy_score, confusion_matrix, classification_report y_pred classifier.predictX_test printAccuracy:, accuracy_scorey_test, y_pred printConfusion Matrix:y_tepred_matrix y_tepred_matrix printClassification Report:, classification_reporty_test, y_pred Ena ke mokhoa oa motheo oa ho koetlisa mohlala oa SVM. U ka etsa liteko ka li-parameter tse fapaneng, li-kernels, le mekhoa ea ho lokisa data esale pele ho ntlafatsa tsebetso ea mohlala. 