Conclusions

Linear Regression:

Pitfalls of using different packages:

1. Read the documentation of the package or library.
2. Understand what the given function is trying to accomplish.

Example:

Using sklearn in Python and lm() in R had the same R Squared, coefficients, and other metrics. However, the statsmodels.api package had different results for every variable. The reason was that the statsmodels.api package doesn’t immediately include the intercept - the user must create a constant based on the X (predictor) in order to get the same results.

This was my mistake:

X = data["age"]
model = sm.OLS(y, X)

results = model.fit()
print(result.summary())

Here is the correction to that mistake:

X = data["age"]
X = sm.add_constant(X)
model = sm.OLS(y, X) 

result = model.fit()
print(result.summary())

Note: The only line that changed was X = sm.add_constant(X).

If doing multiple regressions for every single variable, consider doing:

predictors = df.iloc[:,:6] #all columns except 'charges'

for i in predictors: 
	X = predictors[i]
	X = sm.add_constant(X)
	model = sm.OLS(y, X) 
	results = model.fit()
    #results.summary()
	print(f"{i} vs. charges: ", results.summary())
	print("**********************************************")
	print("**********************************************")
	print("**********************************************")
	print("P-Values:", results.pvalues)