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Regression analysis


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33162123616990010010013950 34132123812999510011003970 4116192716994010011004040 2912293712996011001004050 25122113312891501001004050 31122103816991100010014050 41152123416997110010014090 421611239128108511001004111 24154143515898010100104139 3812232149913010100104210 42162122616995110011004224 35122123512896210010014259 33162123516891310010014315 49112123916990300100104470 43156113612891110100104536 301251238573210100014610 411421247168910410100104660 33152153813880101001004678 31172643149108410100104791 39172103812890000011004933 Regression Model Development In this assignment, you are tasked with using the information in our course case to build a predictive model on a continuous response variable (Y-variable). This assignment encompasses feature engineering, model preparation, variable selection, and model development.    A) Deliverable: Jupyter Notebook or .zip file containing your Jupyter Notebook. Please use the following naming convention: FamilyName_FirstName_A1_Regression_Analysis.ipynb   B) Modeling Criteria and Violation Penalties Your deliverable needs to meet the following criteria. Failure to meet the coding criteria listed below will result and a reduction in your model points score.   Your grade will be determined by the performance of your final model as follows:   Final Model Points = Final Model R-Square on the Test Set – Modeling Violation Penalties   Criterion 1 – Train-Test Gap Gap between training and testing scores must be less than or equal to 0.05. In train-test split, make sure your random_state is set to 219 and your test_size is set to 0.25.   Violation Penalty If the gap is greater than 0.05, your final model points will be reduced by the amount of the gap that exceeds 0.05. For example, if the train-test gap is 0.06, your final model points will be reduced by 0.01. If your random_state is not set to 219 or your test_size is not set to 0.25, this will be manually adjusted when your deliverable is being graded and your grade will be reduced by by 0.025.     Criterion 2 – Response Variable Usage The response variable cannot be used in any form as an explanatory variable (the Y-variable cannot be used on the X-side). This includes logarithmic versions of the y-variable, and features that were engineered using the y-variable.   Violation Penalty Both of the following will occur if the response variable was used as an explanatory variable: · The model will be rescored after this variable has been removed. This will likely result in a major reduction in your final score, so be careful!     Criterion 3 – Model Types Model types are appropriate for the task at hand and come from statsmodels or scikit-learn (other packages and engines are not permitted).   Permitted Model Types · OLS Regression (standard linear regression) · Lasso Regression · Bayesian Automatic Relevance Determination (ARD) · K-Nearest Neighbors Regression (KNN)   Note that you are permitted to adjust the optional arguments of the permitted model types.   Violation Penalty Final models that are not in the list of permitted model types will be discarded and the last appropriate model that ran in your code will be used as your final model. Final model points will be reduced by 0.025.     Criterion 4 – Code is Well-Commented and Runs Without Errors For this assignment, aim for a minimum one quality comment for every 10 lines of code.   Violation Penalty · Not being well-commented will reduce your final model points by 0.025. · Submitting a code with at least one error will reduce your final model points by 0.025.     Criterion 5 – Code Processing Time Your code must process from beginning to end in 60 seconds or less, based on your computer’s processing speed. There is no need to calculate processing time in your code.   Violation Penalty · Going over the processing limit will result in a 0.25 reduction in model points.   Criterion 6 – Model Output Model results are outputted as a dynamic string (i.e., f-string) at the end of your script. This must be the last thing that your Jupyter Notebook outputs. Writing this as markdown or exporting as an Excel file are not acceptable (must be a dynamic string). Output table of candidate models is well-formatted and contains the following information: · Model Type · Training Score · Testing Score · Train-Test Gap · It is clear which model is your final model (label it accordingly). The final model MUST be labeled in your dynamic string to meet this criterion.   Violation Penalty Not including all of the above information in a well-formatted dynamic string will result in a 0.25 reduction in model points.   If it is unclear which model was selected as the final model, the following will occur, the last model in the dynamic string will be utilized as your final model.   Criterion 7 – X-variable usage The original and logarithmic versions of an x-variable may not be used in the same model. This does not include engineered features based on these variables.   Violation Penalty Using both the original and logarithmic versions of an x-variable will result in the logarithmic version of the x-variable being removed from the model, and the model will be run and rescored again. Your final model points will also be reduced by 0.025.   Criterion 8 - Full Dataset Usage You are not permitted to remove or modify any observations from the original dataset, with the exception of imputing missing values (you are not permitted to remove observations with missing values). Also, your Jupyter Notebook must be able to be run from the original dataset (no feature engineering or alterations in Excel or other tools are permitted).   Violation Penalty If the above is violated, your Jupyter Notebook will be rerun from the original dataset and any errors that result from this will be subject to Criterion 4 above. Also, your final model points will be reduced by 0.025.