10. import pandas as pdfrom sklearn.preprocessing import train_test_splitdf = pd.read_csv('insurance_claims.csv')xtrain, xtest, ytrain, ytest = train_test_split(df.drop("is_claim", axis=1), df.is_claim, test_size=0.3, random_state=42)Which of the following is true about the code above?The code reads a csv file named insurance_claims. It splits the data into train and test sets. The test split contains 30% of the data. The random state makes sure that the data is split at random to remove inherent order which may be in the data. When the code is run multiple times it produces the diffent splits since `train_test_split` with the parameter `random_state` splits data at random.None of the given answersThe code reads a csv file names insurance claims. The `train_test_split` function will give an error since the second position argument `df.is_claim` is referencing a column that has been drop on the first position argument `df.drop("is_claim", axis=1)The code reads a csv file named insurance_claims. It splits the data into train and test sets. The train split contains 70% of the data. The random state makes sure that when the code is run multiple times it produces the same identical splits since `train_test_split` splits data at random.

The default value of test_size parameter in train_test_split() is _____.1 point0.250.20.80.32. The confusion_matrix() function comes under _____ module.1 pointsklearn.utilssklearn.metricssklearn.model_selectionsklearn.calibration3. Pandas ______ is used to view some basic statistical details like percentile, mean, std etc. of a data frame.1 pointdescribe()desc()details()info()4. Consider a dataframe df containg two tuples. Then df.head() will return1 pointFive tuples where bottom 3 containing NoneFive tuples where bottom 3 containing garbage valuesTwo tuplesError5. To select a specific column (say ‘col3’) from a dataframe (say ‘df’), we have to write1 pointdf(‘col3’)df[['col3']]df.col3df[3]6. To implement linear regression, we can use _____.1 pointsklearn.model_selection.LinearRegression()sklearn.multiclass.LinearRegression()sklearn.preprocessing.LinearRegression()sklearn.linear_model.LinearRegression()7. What is the effect of following line:                                                 df = df.dropna(axis=0)1 pointDrops all rowsDrops all columnsDrop rows with null valuesDrop columns with null values8. Following data points represents ___________.1 pointPositive CorrelationNegative CorrelationNegative CovarianceZero Covariance9. Regression is one of the types of supervised learning models, where data is classified according to labels and output data need not be continuous. (True/False)1 pointTrueFalse10. Which of the following is defined as the measure of balance between precision and recall?1 pointAccuracyF1-scoreReliabilityPunctuality11. _____ helps to find the best model that represents our data and how well the chosen model will work in future.1 pointEvaluationPerformance MeasureLearningValidation12. While evaluating a model's performance, recall parameter considers _____.1 pointFalse PositiveFalse NegativeTrue PositiveTrue Negative13. Two conditions when prediction matches with the reality are true positive and __________.1 pointFalse PositiveFalse NegativeTrue PositiveTrue Negative14. Odd man out:Regression, Classification, Clustering1 pointRegressionClassificationClustering15. Which of the following talks about how true the predictions are by any model?1 pointAccuracyReliablityRecallF1-score16. Which of the following tasks can be best solved using reinforcement learning?1 pointPredicting the amount of rainfall based on various cuesDetecting fraudulent credit card transactionsTraining a robot to solve a maze17. During linear regression, with regard to residuals, which among the following is true?1 pointLower is betterHigher is betterDepends upon the dataNone of the above18. We can handle missing values in Machine Learning by1 pointDeleting rows with missing valuesReplacing with the mean, median, or mode of remaining values in the columnReplacing with the most frequent categoryAll of the mentioned19. Which of the following is NOT supervised learning?1 pointPCADecision TreeLinear RegressionNaive Bayesian20. A computer program is said to learn if1 pointIt improves with experienceIt learns from experienceIt learns from mistakesIt learns from supervisor21. A well-defined learning problem must include1 pointTaskPerformance measureTraining experienceAll of the mentioned22. Inductive bias is the assumption made by the learner.1 pointTrueFalse23. If X represents a matrix of feature, then1 pointA row in the X represents one data point or one instanceA column in the X represents one feature or one attributeAll of the mentionedNone of the mentioned24. Semi-supervised Learning combines a __________ with a __________ during training.1 pointsmall amount of labelled data, large amount of unlabelled datasmall amount of labelled data, small amount of unlabelled datalarge amount of labelled data, large amount of unlabelled datalarge amount of labelled data, small amount of unlabelled data25. In multiple regression, we have ____ independent variable and _____ dependent variable.1 pointsingle, singlemore than one, singlemore than one, more than onesingle, more than one26.  Entropy([9+,5-]) = ?1 point0.2460.2830.940.6527.  Entropy([5+,0-]) = ?1 point0.50.25010.7528. To measure the overall strength of the model in regression analysis, we use _______.1 pointFactor analysisCoefficient of partial correlationCoefficient of partial regressionCoefficient of determination29.  What is the purpose of performing cross-validation?1 pointTo assess the predictive performance of the modelsTo judge how the trained model performs outside the sample on test dataAll of the mentionedNone of the above30. What does p indicate in the following figure?1 pointProportionProbabilityPrecisionPercentage

Test Expected Got import pandas as pddataset_filename = 'penguins.csv'data_col = 'bill_depth_mm'tr, mn = clean_mean_df(name=dataset_filename, col_name=data_col)dataset_path = '/var/lib/seaborn-data/'filename = dataset_path + dataset_filenamedf = pd.read_csv(filename)sbefore =df.shapedf.dropna(inplace = True)safter =df.shapetotal_removed = sbefore[0]-safter[0]mean_val = df[data_col].mean()print(total_removed==tr)print(abs(mn-mean_val)/mn>=0 and abs(mn-mean_val)/mn<.1)TrueTrueFalseTrueimport pandas as pddataset_filename = 'mpg.csv'data_col = 'weight'tr, mn = clean_mean_df(name=dataset_filename, col_name=data_col)dataset_path = '/var/lib/seaborn-data/'filename = dataset_path + dataset_filenamedf = pd.read_csv(filename)sbefore =df.shapedf.dropna(inplace = True)safter =df.shapetotal_removed = sbefore[0]-safter[0]mean_val = df[data_col].mean()print(total_removed==tr)print(abs(mn-mean_val)/mn>=0 and abs(mn-mean_val)/mn<.1)TrueTrueFalseTrueimport pandas as pddataset_filename = 'iris.csv'data_col = 'petal_length'tr, mn = clean_mean_df(name=dataset_filename, col_name=data_col)dataset_path = '/var/lib/seaborn-data/'filename = dataset_path + dataset_filenamedf = pd.read_csv(filename)sbefore =df.shapedf.dropna(inplace = True)safter =df.shapetotal_removed = sbefore[0]-safter[0]mean_val = df[data_col].mean()print(total_removed==tr)print(abs(mn-mean_val)/mn>=0 and abs(mn-mean_val)/mn<.1)TrueTrueTrueTrueYour code must pass all tests to earn any marks. Try again.

14. import pandas as pdfrom sklearn import preprocessingdata = {"gender": list('FMMF')}df = pd.DataFrame(data)one_hot = preprocessing.OneHotEncoder(sparse_output=False,drop='if_binary')new_df = pd.DataFrame(one_hot.fit_transform(df), columns= one_hot.get_feature_names_out())new_dfWhat is the output of the code above? Gender_M01.010.020.031.0 Gender_FGender_M00.01.011.00.021.00.030.01.0 Gender_FGender_M01.00.010.01.020.01.031.00.0  Gender_M00.011.021.030.0

Which function in scikit-learn is used to split data into training and testing sets?Answer areatrain_test_split()split_data()data_split()train_test()

1.Question 1Which is the syntax code to split the data into 60% training data and 40% testing data? 1 pointtesting_data, training_data = data.randomSplit([40, 60]) training_data, testing_data = data.randomSplit([0.6, 0.4]) training_data, testing_data = data.randomSplit([0.4, 0.6]) testing_data, training_data = data.randomSplit([0.6, 0.4]) 2.Question 2What does a VectorAssembler do? 1 pointIt combines the individual data elements into a column. It combines a bunch of columns as a single vector column. It combines two DataFrames into one. It combines individual data elements into a row. 3.Question 3What is the primary purpose of Spark's in-memory processing capability? 1 pointTo enable real-time data stream processing To improve data ingestion performance To reduce disk-based I/O costs To support complex data transformation tasks 4.Question 4What is the role of data engineers in Spark cluster monitoring? 1 pointTo ensure the efficient running and health of the Spark cluster To troubleshoot issues related to data ingestion pipelines To optimize code and data structures for better performance To analyze and visualize data processed by Spark 5.Question 5Your goal is to predict the height of a child, given the age and the weight. Which of the following algorithms will help you achieve that? 1 pointLinear regression K-means Logistic regression RandomSplit 6.Question 6Which is the correct statement for a linear regression problem? 1 pointThere will be 1 label column, which is non-numeric and multiple numeric feature columns. There will be 1 label column, which is non-numeric and multiple non-numeric feature columns. There will be 1 label column, which is text and multiple numeric feature columns. There will be 1 label column, which is numeric and multiple numeric feature columns. 7.Question 7Which is the correct syntax to create a Spark session with application name "Test App"?1 pointspark = SparkSession.builder.appname("Test App").createSession() spark = Sparksession.builder.appName("Test App").getOrCreateSession() spark = SparkSession.builder.appname("Test App").getOrCreate spark = SparkSession.builder.appName("Test App").getOrCreate() 8.Question 8Which statement best defines Clustering using Spark ML? 1 pointIt is a supervised learning technique. It relies on predefined labels or target variables. It discovers patterns and structures based on their randomness. It is the process of grouping similar data points together into clusters. 9.Question 9Which is the correct syntax to display the columns "height" and "weight" from the dataframe named "health"? 1 pointhealth.select(["height","weight"]).show() health.selectcolumns("height","weight").show() health.show(["height","weight"]) health.show("height","weight") 10.Question 10Which statement best defines GraphFrames? 1 pointGraphFrames is an integral part of the Spark installation and need not be downloaded as a separate package. GraphFrames enables Spark to perform graph processing, run computations, and analyze standard graphs. GraphFrames does not contain any built-in algorithms; you can download them as a separate package as per your requirements. GraphFrames does not require setting a directory for checkpoints. Coursera Honor Code  Learn moreI, VANKADARI SAI SREE SUSHMITHA, understand that submitting work that isn’t my own may result in permanent failure of this course or deactivation of my Coursera account.SubmitSave draftLast saved on Jul 7, 9:13 AM PDTLikeDislikeReport an issue