DATA: USING THE COLLEGE FOOTBALL CONFERENCE THAT IS ASSIGNED TO YOUR GROUP COMPL

DATA: USING THE COLLEGE FOOTBALL CONFERENCE THAT IS ASSIGNED TO YOUR GROUP COMPLETE ALL OF THE FOOTBALL STATISTICS FOR 2009 FOR each TEAM. YOUR COMPLETED DATA FOR YOUR TEAM FOR 2009 WILL HAVE NO MISSING VALUES, AND YOUR SUBMISSION MUST MEET THE FOLLOWING CRITERIA:(1) Submit only the data for your selected teams for 2009. The completed data will contain all football statistics and will contain no missing values.
(2) Create a document detailing how you filled in the missing values (reasoning, sources, etc.)
(3) Create a data dictionary for all of the football statistics.
(4) Submit a xlsx or csv file containing the completed data file, a xlsx file containing the data dictionary, and a word file containing the documentation.
(5) Use python and submit the jupyter notebook.
Please use any of these below methods to replace the missing values:
Imputation involves estimating missing values based on available data. There are several imputation techniques, each with its strengths and weaknesses. Common techniques include:
Mean/Median/Mode imputation: Replace missing values with the average, median, or most frequent value for that variable.
Hot Deck imputation: Here the missing values with existing values from similar cases.
Regression imputation
K-Nearest Neighbors (KNN) imputation
○Missing Completely at Random (MCAR): Data is missing entirely by chance, unrelated to any other variables.
○Missing at Random (MAR): Missingness depends on observed variables but not on the missing variable itself.
○Missing Not at Random (MNAR): Missingness depends on the missing variable itself. This is the most challenging type to handle.