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Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!

**Module 1 : Introduction**

**Question: What does CSV stand for?**

- Comma-separated values
- Car sold values
- Car state values
- None of the above

**Question: In the data set, which of the following represents an attribute or feature?**

- Row
**Column**- Each element in the dataset

**Question: What is the name of what we want to predict?**

**Target**- Feature
- Dataframe

**Question: What is the command to display the first five rows of a dataframe** `df`

**?**

**df.head()**- df.tail()

**Question: What command do you use to get the data type of each row of the dataframe** `df`

**?**

**df.dtypes**- df.head()
- df.tail()

**Question: How do you get a statistical summary of a dataframe** `df`

**?**

**df.describe()**- df.head()
- df.tail()

**Question: 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: 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: Consider the dataframe** `df`

. **What does the command **`df.rename(columns={'a':'b'})`

**change about the dataframe **`df`

**?**

- Renames column “a” of the dataframe to “b”.
- Renames row “a” to “b”.
**Nothing. You must set the parameter “inplace = True”.**

**Question: 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: Consider the column of the dataframe** `df['a']`

. **The column has been standardized. What is the standard deviation of the values as a result of applying the following operation:** `df['a'].std()`

**?**

**1**- 0
- 3

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

- 1 and 0
- Just ‘diesel’
- Just ‘gas’
**‘gas’ and ‘diesel’**

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

**1 and 0**- Just ‘diesel’
- Just ‘gas’
- ‘gas’ and ‘diesel’

**Module 3 – Exploratory Data Analysis**

**Question: Consider the dataframe “df”. What method provides the summary statistics?**

**df.describe()**- df.head()
- df.tail()

**Question: 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 resulting values of** `df_grp[‘price’]`

?

**The average price for each body style.**- The average price.
- The average body style.

**Question: Correlation implies causation:**

**False**- True

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

- 1
- -100
**-1**

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

- -1
**1**- 0

**Module 4 – Model Development**

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

- -100
- -1
- 0
**1**

**Question: Which 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: The larger the mean squared error, the better your model performs:**

**False**- True

**Question: 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 is the result of ypipe?**

- 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.

**Module 5 – Model Evaluation**

**Question: In the following plot, the vertical axis shows the mean square error and the 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: What is the correct 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: 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: 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: 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]} ]

**Final Exam**

**Question: What does the following command do?**

`df.dropna(subset=["price"], axis=0)`

**Drop the “not a number” values from the column “price”.**- Drop the row “price”.
- Rename the dataframe “price”.

**Question: How would you provide many of the summary statistics for all the columns in the dataframe “df”?**

**df.describe(include = “all”)**- df.head()
- type(df)
- df.shape

**Question: How would you find the shape of the dataframe df?**

- df.describe()
- df.head()
- type(df)
**df.shape**

**Question: What task does the following command, 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: What task does the following line of code perform?**

`result = np.linspace(min(df["city-mpg"]), max(df["city-mpg"]), 5)`

**Builds a bin array ranging from the smallest value to the largest value of “city-mpg” in order to build 4 bins of equal length.**- Builds a bin array ranging from the smallest value to the largest value of “city-mpg” in order to build 5 bins of equal length.
- Determines which bin each value of “city-mpg” belongs to.

**Question: 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 dataframe.

**Question: 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: What does the vertical axis on a scatterplot represent?**

- Independent variable
**Dependent variable**

**Question: What does the horizontal axis on a scatterplot represent?**

**Independent variable**- Dependent variable

**Question: 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: What is the largest possible element resulting in the following operation “df.corr()”?**

- 100
- 1000
**1**

**Question: If the Pearson Correlation of two variables is zero:**

- The two variable have zero mean.
**The two variables are not correlated.**

**Question: If the p-value of the Pearson Correlation is 1:**

- The variables are correlated.
- The variables are not correlated.
**None of the above.**

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

- Fit a regression object “lm”.
**Create a linear regression object.**- Predict a value.

**Question: If the predicted function is:**

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

**The method is:**

- Polynomial Regression
**Multiple Linear Regression**

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

- 10
**1**- 0

**Question: We create a polynomial feature PolynomialFeatures(degree=2). What is the order of the polynomial?**

- 0
- 1
**2**

**Question: 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?**

- 100th 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: You train a ridge regression model. You get a R^2 of 1 on your validation data and you get a R^2 of 0.5 on your training data. What should you do?**

**Nothing. Your model performs flawlessly on your validation data.**- Your model is under fitting perform a polynomial transform.
- Your model is overfitting, increase the parameter alpha.

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