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home / study / engineering / computer science / computer science questions and answers / # variables in the data housing_df.columns output: in…
Question: # variables in the data housing_df.columns output: Index(['CR…
# variables in the data
housing_df.columns
output:
Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',
'PTRATIO', 'LSTAT', 'MEDV', 'CAT. MEDV'],
dtype='object')
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# Create a new dataframe called predictors_df with only numerical predictors
#MISSING 1-5 lines of code (many different ways we have done this before)
predictors_df.columns
My Textbook Solutions
output:
Index(['CRIM', 'ZN', 'INDUS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',
'PTRATIO', 'LSTAT'],
dtype='object')
#Create a correlation table called corr using the predictors_df dataframe
#MISSING 1 line of code
# Use seaborn to create a correlation/heatmap (color and such does not matter)
#MISSING 1 line of code
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# correlation table
corr
CRIM ZN INDUS NOX RM AGE DIS RAD TAX PTRATIO LSTAT
CRIM 1.000000-0.2004690.4065830.420972-0.2192470.3527340.3796700.6255050.5827640.2899460.455621
ZN -0.2004691.000000-0.533828-0.5166040.311991-0.5695370.664408-0.311948-0.314563-0.3916790.412995
INDUS 0.406583-0.5338281.0000000.763651-0.3916760.6447790.7080270.5951290.7207600.3832480.603800
NOX0.420972-0.5166040.7636511.000000-0.3021880.7314700.7692300.6114410.6680230.1889330.590879
RM -0.2192470.311991-0.391676-0.3021881.000000-0.2402650.205246-0.209847-0.292048-0.3555010.613808
AGE 0.352734-0.5695370.6447790.731470-0.2402651.0000000.7478810.4560220.5064560.2615150.602339
DIS -0.3796700.664408-0.708027-0.7692300.205246-0.7478811.000000-0.494588-0.534432-0.2324710.496996
RAD 0.625505-0.3119480.5951290.611441-0.2098470.4560220.4945881.0000000.9102280.4647410.488676
TAX 0.582764-0.3145630.7207600.668023-0.2920480.5064560.5344320.9102281.0000000.4608530.543993
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# Variables In The Data Housing_df.columns Output:... | Chegg.com
PTRATIO 0.289946-0.3916790.3832480.188933-0.3555010.2615150.2324710.4647410.4608531.0000000.374044
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LSTAT 0.455621-0.4129950.6038000.590879-0.6138080.6023390.4969960.4886760.5439930.3740441.000000
If the correlation between variables if greater than 0.7 we can say that the two variables are highly
correlated. From the above table, the pairs of highly correlated variables are:
1) ?????
2) ?????
3) ?????
4) ?????
5) ?????
6) ?????
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Answer
Please do comment if any doubt in that,
PLease do like it will encourage me a lot,
There are 3 columns of data. Each column represents a different variable. What are the 3 variables represented in the dataset?
Answer: The three variables are:
1) M=0 F=1 (Gender)
2) Resting
3) After Exercise
Identify each of the 3 variables as qualitative, quantitative discrete, or quantitative continuous
Answer:
1) M=0 F=1 (Gender) = Qualitative because it contains the categorizes like male and female
2) Resting = Quantitative continuous because it is numeric with infinite possible values between any two values
3) After Exercise = Quantitative continuous because it is numeric with infinite possible values between any two values
Identify the possible values of each of the 3 variables in this dataset.
Answer: The possible values for variable M=0 F=1 are 0 and 1
The possible values for variable Resting are 86.3,86.4,86.5. There will be infinite possible values for this variable.
The possible values for variable After Exercise are 86.3,86.4,86.5. There will be infinite possible values for this variable.
Briefly describe what information each of the 3 variables tells us about the data
Answer: The variable M=0 F=1 gives us information about the gender of the respondent.
The variable Resting represents the heart rate of the respondent before exercise
The variable After exercise represents the heart rate of the respondent after exercise
Explanation
Please refer to solution in this step.
Answer
I explained all details above
Questions viewed by other students
Q: Question 1: # load the data into a dataframe called housing data #MISSING 1 line of code housing_df =
pd.read_csv('BostonHousing.csv') # display column/variable names #Create a list called columns with all of the
housing_df columns names in it #MISSING 1 line of code print("Variables in the data are: ") print(columns) # review
first 5 records in the data print("\nFirst 5 records in...
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# Variables In The Data Housing_df.columns Output:... | Chegg.com
A: See step-by-step answer
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Q: Question1: # partition the data into training (60%) and validation (40%) sets predictors = ['CRIM', 'ZN', 'INDUS', 'CHAS',
'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'LSTAT'] outcome = 'MEDV' # partition the data #Create a dataframe
called X with the columns in the predictors[] list above # Make sure to turn text columns (categorical) values into
dummy variable columns...
A: See step-by-step answer
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