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(" First 5 records in the data are:") #MISSING 1 line of code

output:

Variables in the data are: ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'LSTAT', 'MEDV', 'CAT. MEDV'] First 5 records in the data are: 

	CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	LSTAT	MEDV	CAT. MEDV
0	0.00632	18.0	2.31	0	0.538	6.575	65.2	4.0900	1	296	15.3	4.98	24.0	0
1	0.02731	0.0	7.07	0	0.469	6.421	78.9	4.9671	2	242	17.8	9.14	21.6	0
2	0.02729	0.0	7.07	0	0.469	7.185	61.1	4.9671	2	242	17.8	4.03	34.7	1
3	0.03237	0.0	2.18	0	0.458	6.998	45.8	6.0622	3	222	18.7	2.94	33.4	1
4	0.06905	0.0	2.18	0	0.458	7.147	54.2	6.0622	3	222	18.7	5.33	36.2	1

Question 2:

# select columns for regression analysis outcome = 'MEDV' predictors = ['CRIM', 'CHAS', 'RM']

#Create a dataframe called x containing the predictor columns #MISSING 1 line of code

#Create a dataframe (technically a series) containing the outcome variable. Call it y #MISSING 1 line of code

Question 3:

#Create a model called housing_lm and set it to be a LinearRegression() model #MISSING 1 line of code

# fit the regression model y on x #MISSING 1 line of code

# print the intercept #MISSING 1 line of code

#print the list of predictor columns and the coefficients #MISSING 1 line of code

output:

intercept -28.81068250635914 Predictor coefficient 0 CRIM -0.260724 1 CHAS 3.763037 2 RM 8.278180

Questionn 4:

new_df = pd.DataFrame( [[0.1, 0, 6]], columns=['CRIM', 'CHAS', 'RM'] ) new_df

output:

CRIM CHAS RM

0 0.1 0 6

#Run the prediction model that you created using the above created dataframe containing # the new predictor values the the results housing_lm_pred #MISSING 1 line of code

print('Predicted value for median house price based on the model built using dataset is:', housing_lm_pred)

output:

Predicted value for median house price based on the model built using dataset is: [20.83232392]

Question 5:

# 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')

# 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

output:

Index(['CRIM', 'ZN', 'INDUS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'LSTAT'], dtype='object')