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question:Optimize the Python code snippet (below) for performance efficiency. def process_data(data): processed_data = [] for item in data: if item % 10 == 0: processed_data.append(item) return processed_d
answer:def process_data(data): processed_data = [] for item in data: if (item % 10) == 0: processed_data.append(item) return processed_data def process_data_fast(data): return [item for item in data if item % 10 == 0]
question:Create a machine learning model in Python to classify emails into two classes: spam and ham. Not applicable
answer:import pandas as pd from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC # read the data data = pd.read_csv("spam_emails.csv") # split the data for training and testing X_train, X_test, y_train, y_test = train_test_split(data['text'],data['label'],test_size=0.2,random_state=42) # create an instance of the vectorizer and fit it tfidf = TfidfVectorizer() X_train_vec = tfidf.fit_transform(X_train) X_test_vec = tfidf.transform(X_test) # create the classifier clf = LinearSVC() clf.fit(X_train_vec, y_train) # predict and measure the accuracy prediction = clf.predict(X_test_vec) accuracy = clf.score(X_test_vec, y_test) # print the accuracy print(accuracy)
question:Generate a function in Python that takes a natural number as argument and returns the product of all numbers up to it. n = 4
answer:def getProduct(n): result = 1 for i in range(n): result *= i+1 return result
question:mplement a storage API in Python that supports the CRUD methods.
answer:class StorageAPI: def __init__(self): self._data = {} def create(self, key, data): """Creates a new entry in the storage API""" self._data[key] = data def read(self, key): """Reads a entry from the storage API""" return self._data.get(key, None) def update(self, key, data): """Updates a entry in the storage API""" self._data[key] = data def delete(self, key): """Deletes a entry from the storage API""" del self._data[key]