<prompt> Etsa lenaneo ho tseba hore na motho o bua Senyesemane kapa Sepanishe.nan  <response>: Instruction in English: Create a program to identify whether a person is speaking English or Spanish. . Response in English: One possible approach to achieve this is by using a machine learning algorithm to classify between English and Spanish. Heres some example code:python Import necessary librariesimport nltkfrom nltk.classify import NaiveBayesClassifierfrom nltk.corpus import stopwordsfrom nltk.tokenize import word_tokenizefrom sklearn.externals import joblibimport pandas as pd Define stopwordsen_stopwords setstopwords.wordsenglishes_stopwords setstopwords.wordsspanish Define feature extractor functiondef featurestext: words word_tokenizetext return word:True for word in words Load datasetdata pd.read_csvenglish-spanish-dataset.csv Initialize the trainertrainer NaiveBayesClassifier.train Extract features and labels from the datasetX y for index,row in data.iterrows: X.appendfeaturesrow1 y.appendrow2 Train the machine learning modelclassifier trainernltk.classify.util.apply_featuresX,y Take input from the usertext inputEnter text: Tokenize the input textwords word_tokenizetext Remove stop wordswords word for word in words if word.lower not in en_stopwords and word.lower not in es_stopwords Make predictionprediction classifier.classifyfeatures .joinwordsif prediction en: printThe text is in Englishelse: printThe text is in SpanishThis code makes use of the Naive Bayes algorithm to classify between English and Spanish languages by predicting the class of input text. The feature extractor extracts features, which are a dictionary of words, from the text. The machine learning model is trained on these features to make predictions.Instructions:1. Import the necessary libraries. This code relies on the nltk, nltk.classify, nltk.corpus, nltk.tokenize, sklearn.externals, and pandas libraries.2. Define the stopwords for both English and Spanish, as these words will be removed from the input text before making predictions.3. Define a features function that takes in text as an input and returns a dictionary of words present in the text. This will serve as the feature extractor for the machine learning model.4. Load the dataset, which contains labeled . Response in Sesotho: Mokhoa o mong o ka khonehang oa ho fihlela sena ke ho sebelisa algorithm ea ho ithuta ka mochini ho hlophisa lipakeng tsa Senyesemane le Sepanish. Mona ke mohlala oa khoutu: python Kenya lilaeborari tse hlokahalang kenya nltk ho tsoa nltk.classify import NaiveBayesClassifier from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from sklearn.externals import joblib import pandas as pd Define stopwords en_stopwords stopwords.wordssekhooa es_stopwords setstopwords.wordsspanish Hlalosa tšobotsi ea extractor def featuresmongolo: mantsoe word_tokenizetext kgutlisetsa lentsoe:Nete bakeng sa lentsoe ka mantsoe Laola dataset ya data pd.read_csvenglish-spanish-dataset.csv Qalisa mokoetlisi oa mokoetlisi NaiveBayesClassifier.train Ntša likarolo le lileibole ho tsoa pokellong ea data X y bakeng sa index, mola oa data. iterrows: X.appendlikarolomola1 y.appendmola2 Koetlisa sehlopha sa mohlala oa ho ithuta ka mochini mokoetlisinltk.classify.util.apply_featuresX,y Nka ho kenya mongolo ho tswa ho mosebedisi ho kenyakenya mongolo: Tokenize the input text words word_tokenizetext Tlosa mantsoe a emisang lentsoe ka lentsoe ka mantsoe haeba lentsoe.tlase eseng ka en_stopwords le word.low not in es_stopwords Etsa ponelopele classifier.classifylikarolo .joinmantsoe haeba ho bolela esale pele en: hatisaMongolo o ka Senyesemane hape: hatisaThe mongolo o ngotsoe ka Sepanishe Khoutu ena e sebelisa algorithm ea Naive Bayes ho arola lipakeng tsa lipuo tsa Senyesemane le Sepanishe ka ho lepa pele sehlopha sa mongolo. Sesebelisoa se ntša likarolo, e leng bukantswe ya mantswe, ho tswa sengolweng. Moetso oa ho ithuta ka mochini o koetliselitsoe likarolo tsena ho etsa likhakanyo. Litaelo: 1. Kenya lilaebrari tse hlokahalang. Khouto ena e itshetlehile ho dilaeborari tsa nltk, nltk.classify, nltk.corpus, nltk.tokenize, sklearn.externals, le pandas. 2. Hlalosa li-stopwords tsa Senyesemane le Sepanishe, kaha mantsoe ana a tla tlosoa ho mongolo pele o etsa likhakanyo. 3. Hlalosa tšebetso ea likarolo e nkang mongolo e le kenyeletso ebe e khutlisa bukantswe ya mantswe e teng mongolong. Sena se tla sebetsa e le sesebelisoa sa moetso oa ho ithuta oa mochini. 4. Laola dataset, e nang le mangolo 