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Here, you will get the Data Analysis with Python Cognitive Class Course Exam Answers in bold color which are given below.

These are the answers to Data Analysis with Python Cognitive Class Course Exam Answers certification course. These answers have been updated recently and are 100% correct. The final exam is on Monday, April. All answers are 100%.

Module 1 – Introduction

Question 1: In the data set what represents an attribute or feature?

• Row
• Column
• Each element in the data set

Question 2: What is the command to display the first five rows of a dataframe df?

• df.tail()

Question 3: How do you get a statistical summary of a dataframe df?

• df.describe()
• df,tails()

Question 4: What does CSV stand for ?

• Comma Separated Values
• Car Sold values
• Car State values
• None of the above

Question 5: What is another name for the variable that we want to predict?

• Target
• Feature
• Dataframe

Question 6: what command do you use to get the data type of each row of the dataframe df?

• df.dtypes
• df.tail()

Question 7: If you use the method describe() without changing any of the arguments you will get a statistical summary of all the columns of type object?

• False
• True

Module 2 – Data Wrangling

Question 1: Consider the dataframe “df” what is the result of the following operation df[‘symbolling’] = df[‘symbolling’] + 1?:

• Every element in the column “symbolling” will increase by one
• Every element in the row “symbolling” will increase by one
• Every element in the dataframe will increase by one

Question 2: Consider the dataframe “df” , what is the result of the following operation df[‘price’] = df[‘price’].astype(int) ?

• convert or cast the row ‘price’ to an integer value
• convert or cast the column ‘price’ to an integer value
• convert or cast the entire dataframe to an integer value

Question 3: Consider the column of the dataframe df[‘Fuel’], with two values ‘gas’ and’ diesel’. What will be the name of the new colunms pd.get_dummies(df[‘Fuel’]) ?

• 1 and 0
• Just diesel
• Just gas
• Gas and diesel

Question 4: Consider the dataframe “df”, what does the command df.rename(columns={‘a’:’b’}) change about the dataframe “df”

• rename column “a” of the dataframe to “b”
• rename the row “a” to “b”
• nothing as you must set the parameter “inplace =True “

Question 5: Consider the column of the dataframe df[‘a’]. The colunm has been standardized. What is the standard deviation of the values, i.e the result of applying the following operation df[‘a’].std() :

• 1
• 0
• 3

Question 6: What are the values of the new columns from part 5 a)

• 1 and 0
• Just diesel
• Just gas
• Gas and diesel

Module 3 – Exploratory Data Analysis

Question 1: What is the minimum possible value of Pearson’s Correlation :

• 1
• -100
• -1

Question 2: What is the Pearson correlation between variables X and Y, if X=Y:

• -1
• 1
• 0
• X
• Y

Question 3: Consider the dataframe “df”. Which method provides the summary statistics?

• df.describe()
• df.tail()
• df.summary()

Question 4: Correlation implies causation :

• False
• True

Question 5: Consider the following dataframe:

df_test = df[‘body-style’, ‘price’]

The following operations is applied:

df_grp = df_test.groupby([‘body-style’], as_index=False).mean()

What are resulting values of df_grp[‘price’]:

• The average price for each body style
• The average price
• The average body style

Module 4 – Model Development

Question 1: Let X be a dataframe with 100 rows and 5 columns, let y be the target with 100 samples,assuming all the relevant libraries and data have been imported, the following line of code has been executed:

LR = LinearRegression()

LR.fit(X, y)

yhat = LR.predict(X)

How many samples does yhat contain :

• 5
• 500
• 100
• 0

Question 2: What statement is true about Polynomial linear regression

• Polynomial linear regression is not linear in any way
• Although the predictor variables of Polynomial linear regression are not linear the relationship between the parameters or coefficients is linear.
• Polynomial linear regression uses wavelets

Question 3: Assume all the libraries are imported, y is the target and X is the features or dependent variables, consider the following lines of code:

Input = [(‘scale’, StandardScaler()), (‘model’, LinearRegression())]

pipe = Pipeline(Input)

pipe.fit(X,y)

ypipe = pipe.predict(X)

What have we just done in the above code?

• Polynomial transform, Standardize the data, then perform a prediction using a linear regression model
• Standardize the data, then perform prediction using a linear regression model
• Polynomial transform then Standardize the data

Question 4: What value of R^2 (coefficient of determination) indicates your model performs best ?

• -100
• -1
• 0
• 1

Question 5: The larger the mean square error, the better your model has performed

• False
• True

Module 5 – Model Evaluation:

Question 1: What is the  use of the “train_test_split” function such that 40% of the data samples will be utilized for testing, the parameter “random_state” is set to zero, and the input variables for the features and targets are_data, y_data respectively.

• train_test_split(x_data, y_data, test_size=0, random_state=0.4)
• train_test_split(x_data, y_data, test_size=0.4, random_state=0)
• train_test_split(x_data, y_data)

Question 2: What is the code to create a ridge regression object “RR” with an alpha term equal 10

• RR=LinearRegression(alpha=10)
• RR=Ridge(alpha=10)
• RR=Ridge(alpha=1)

Question 3: In the following plot, the vertical access shows the mean square error andthe horizontal axis represents the order of the polynomial. The red line represents the training error the blue line is the test error. What is the best order of the polynomial given the possible choices in the horizontal axis?

• 2
• 8
• 16

Question 4: What is the output of cross_val_score(lre, x_data, y_data, cv=2)?

• The predicted values of the test data using cross validation.
• The average R^2 on the test data for each of the two folds
• This function finds the free parameter alpha

Question 5: What dictionary value would we use to perform a grid search for the following values of alpha: 1,10, 100. No other parameter values should be tested

• alpha=[1,10,100]
• [{‘alpha’: [1,10,100]}]
• [{‘alpha’: [0.001,0.1,1, 10, 100, 1000,10000,100000,100000],’normalize’:[True,False]} ]

Data Analysis with Python Final Exam Answers

Question 1: How would you provide many of the summery statistics for all the columns in the dataframe “df”:

• df.describe(include = “all”)
• type(df)
• df.shape

Question 2:What task does the following command to df.to_csv(“A.csv”) perform

• change the name of the column to “A.csv”
• load the data from a csv file called “A” into a dataframe
• Save the dataframe df to a csv file called “A.csv”

Question 3: What task does the following line of code perform:

df[‘peak-rpm’].replace(np.nan, 5,inplace=True)

• replace the not a number values with 5 in the column ‘peak-rpm’
• rename the column ‘peak-rpm’ to 5
• add 5 to the data frame

Question 4: If we have 10 columns and 100 samples how large is the output of df.corr()

• 10 x 100
• 10 x 10
• 100×100
• 100×100

Question 5: What does the vertical axis in a scatter plot represent

• independent variable
• dependent variable

Question 6: if the Pearson Correlation of two variables is zero:

• the two variable have zero mean
• the two variables are not correlated

Question 7: What does the following line of code do: lm = LinearRegression()

• fit a regression object lm
• create a linear regression object
• predict a value

Question 8: What steps do the following lines of code perform:

Input=[(‘scale’,StandardScaler()),(‘model’,LinearRegression())]

pipe=Pipeline(Input)

pipe.fit(Z,y)

ypipe=pipe.predict(Z)

• Standardize the data, then perform a polynomial transform on the features Z
• find the correlation between Z and y
• Standardize the data, then perform a prediction using a linear regression model using the features Z and targets y

Question 9: We create a polynomial feature as follows “PolynomialFeatures(degree=2)”, what is the order of the polynomial

• 0
• 1
• 2

Question 10: You have a linear model the average R^2 value on your training data is 0.5, you perform a 100th order polynomial transform on your data then use these values to train another model, your average R^2 is 0.99 which comment is correct

• 100-th order polynomial will work better on unseen data
• You should always use the simplest model
• the results on your training data is not the best indicator of how your model performs, you should use your test data to get a beter idea

Question 11: You train a ridge regression model, you get a R^2 of 1 on your training data and you get a R^2 of 0 on your validation data, what should you do:

• Nothing your model performs flawlessly on your test data
• your model is under fitting perform a polynomial transform
• your model is overfitting, increase the parameter alpha

Question 12: Question 1: What does the following command do:

df.dropna(subset=[“price”], axis=0)

• Drop the “not a number” from the column price
• Drop the row price
• Rename the data frame price

Question 13: How would you find the shape of the dataframe df

• df.describe()
• type(df)
• df.shape

Question 14: What is the maximum value of R^2 that can be obtained

• 10
• 1
• 0

Question 15: What task does the following line of code perform:

df[‘peak-rpm’].replace(np.nan, 5,inplace=True)

• replace the not a number values with 5 in the column ‘peak-rpm’
• rename the column ‘peak-rpm’ to 5
• add 5 to the data frame

Question 16: How do you “one hot encode” the column ‘fuel-type’ in the dataframe df

• pd.get_dummies(df[“fuel-type”])
• df.mean([“fuel-type”])
• df[df[“fuel-type”])==1 ]=1

Question 17: What does the horizontal axis in a scatter plot represent

• independent variable
• dependent variable

Question 18: what is the largest possible element resulting in the following operation “df.corr()”

• 100
• 1000
• 1

Question 19: if the p value of the Pearson Correlation is 1:

• the variables are correlated
• the variables are not correlated
• none of the above

Question 20: If the predicted function is:

Yhat = a + b1 X1 + b2 X2 + b3 X3 + b4 X4

The method is

• Polynomial Regression
• Multiple Linear Regression

Conclusion

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