Quiz

📌 Each column contains a:

• attribute or feature
• different used car

📌 What description best describes the library Pandas?

• Includes functions for some advanced math problems as listed in the slide as well as data visualization.
• Offers data structure and tools for effective data manipulation and analysis. It provides fast access to structured data. The primary instrument of Pandas is a two-dimensional table consisting of columns and rows labels which are called a DataFrame. It is designed to provide an easy indexing function.
• Uses arrays as their inputs and outputs. It can be extended to objects for matrices, and with a little change of coding, developers perform fast array processing.

📌 What task does the following lines of code perform?

path='C:\Windows\…\ automobile.csv'
df.to_csv(path)

• Exports your Pandas dataframe to a new csv file, in the location specified by the variable path.

• Loads a csv file.

📌 What does csv stand for?

• Comma Separated Values
• Car Sold values
• none of the above

📌 What library is primarily used for machine learning

• scikit-learn
• Python
• Matplotlib

📌 What task does the following command perform?

df.to_csv("A.csv")

• 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”

📌 Consider the segment of the following dataframe: What is the type of the column make?

• int64
• float64
• object

📌 How would you access the column “symboling” from the dataframe df?

• df[“symboling”]
• df==“symboling”

📌 What is the correct symbol for missing data?

• nan
• no-data

📌 How would you rename the column “city_mpg” to “city-L/100km”?

• df.rename(columns={”city_mpg”: “city-L/100km”})
• df.rename(columns={”city_mpg”: “city-L/100km”}, inplaice=True)

📌 Why do we convert values of Categorical Variables into numerical values?

• To save memory
• Most statistical models cannot take in objects or strings as inputs

📌 Consider the dataframe df; what method provides the summary statistics?

• describe()
• head()
• tail()

📌 If we have 10 columns and 100 samples, how large is the output of df.corr()?

• 10 x 100
• 10x10
• 100x100

📌 If the p-value of the Pearson Correlation is 1, then …

• The variables are correlated
• The variables are not correlated
• None of the above

📌 Consider the following dataframe:

df_test = df[['body-style', 'price']]

The following operation is applied:

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

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

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

📌 What is the Pearson Correlation between variables X and Y, if X=-Y?

• -1
• 1
• 0

📌 What does the following line of code do?

lm = LinearRegression()

• Fit a regression object lm

• Create a linear regression object

• Predict a value

📌 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

📌 If X is a dataframe with 100 rows and 5 columns, and y is the target with 100 samples, and assuming all the relevant libraries and data have been imported, and the following line of code has been executed:

LR = LinearRegression()
LR.fit(X, y)
yhat = LR.predict(X)

How many samples does ㅅhat contain?

• 500
• 5
• 100

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

• -1
• 1
• 0

📌 Consider the following equation:

Y = b0 + b1x

What is the parameter b_0 (b subscript 0)?

• The predictor or independent variable
• The target or dependent variable
• The intercept
• The slope

📌 What is the output of the following code?

cross_val_predict (lr2e, x_data, y_data, cv=3)

• 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

📌 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]} ]

📌 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. After this step, your average R^2 is 0.99; which of the following comments is correct?

• You should always use the simplest model
• 100-th order polynomial will work better on unseen data
• 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 better idea

📌 What type of file allows data to be saved in a tabular format?

• html
• pdf
• csv

📌 What Python libraries were considered “Algorithmic Libraries” in this course?

• Pandas, Numpy, SciPy
• Scikit-learn, Statsmodels
• Matplotlib, Seaborn

📌 What path tells us where the data is stored?

• Scheme path
• File path
• Encoding path

📌 What does the head() method return?

• It returns the data types of each column
• It returns the last five rows
• It returns the first five rows

📌 The Pandas library allows us to read what?

• Only headers
• Various datasets into a data frame
• Only rows

📌 The Pandas library is mostly used for what?

• Data analysis
• Machine learning
• Data visualization

📌 What would the following code segment output from a dataframe df? df.head(5)

• It would return the first 5 rows of the dataframe
• It would return the last 5 rows of the dataframe
• It would return all of the rows of the dataframe

📌 What does the following code segment perform in a dataframe?

mean = df["normalized-losses"].mean() df["normalized-losses"].replace(np.nan, mean)

• It replaces the missing values in the column “normalized-losses” with the mean of that column

• It drops rows that contain missing values

• It drops all of the rows in the column “normalized-losses”

📌 How would you multiply each element in the column df[“c”] by 5 and assign it back to the column df[“c”]?

• 5 * df[“b”]
• df[“c”] = 5 * df[“c”]
• df[“a”] = df[“c”] * 5

📌 What function returns the maximum of the values requested for the requested column?

• max()
• std()
• min()

📌 Since most statistical models cannot take objects or strings as inputs, what action needs to be performed?

• Convert numerical values into categorical variables
• Convert categorical variables into numerical values

📌 What function will change the name of a column in a dataframe?

• rename()
• replace()
• exchange()