build an OLS regression model with python on Jupiter
Budget: $30 – $250 USD
(1) R-Square value for your model (aim for the highest value you can attain).
(2) The response variable cannot be used in any form as an explanatory variable (i.e., the y-variable cannot be used on the X-side). This includes any features derived from the y-variable.
(3) All of the variables in your model are significant based on p-values.
(4) You must build two models (their results should be extremely similar, if not the same):
OLS (statsmodels)
LinearRegression( ) (scikit-learn)*
(5)Get the R-Square value for scikit-learn's LinearRegression() (i.e., the .score step).
(6) A minimum one quality "#" comment for every 5 lines of code.
(7) Submission is error free.
(8) You must use the entire dataset (i.e., you cannot drop/remove any rows of data).
(2) The response variable cannot be used in any form as an explanatory variable (i.e., the y-variable cannot be used on the X-side). This includes any features derived from the y-variable.
(3) All of the variables in your model are significant based on p-values.
(4) You must build two models (their results should be extremely similar, if not the same):
OLS (statsmodels)
LinearRegression( ) (scikit-learn)*
(5)Get the R-Square value for scikit-learn's LinearRegression() (i.e., the .score step).
(6) A minimum one quality "#" comment for every 5 lines of code.
(7) Submission is error free.
(8) You must use the entire dataset (i.e., you cannot drop/remove any rows of data).
Related categories:
Python
Statistics
Machine Learning (ML)
R Programming Language
Statistical Analysis