Skip to content

Jupyter notebook and "Streamlit" python scripts for identifying features that can predict employee turn over rates at 250 senior care centers across the US. Combines multiple repetition of Lasso regression and linear regression. Integrates U.S. census data, employee salary, and employee tenure with data on employee satisfaction and engagement to…

Notifications You must be signed in to change notification settings

MamiyaA/FeatureFinder

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

53 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

FeatureFinder

Data exploration and pre-processing:

DataExplorationAndPreProcessing.ipynb: Load the employee turnover rate, employee’s answers to the questions, tenure of key positions, and median household income for the location (from US Census). Transform the data to make it normally distributed, standardize the data, save the data.

AddingEmployeeSalary.ipynb: Load the salary for each employee and calculate the median salary for each location. Standardize the data and save it.

AddingResponseStandardDeviation.ipynb: Load the individual employee’s answers to the questions and calculate how varied the responses are at each location. Standardize and save it.

Regression Analysis:

Run_LassoRegression_and_Average.ipynb: Load all the features saved by the notebooks above, and run Lasso regression (1000 times). Produce a model that predicts employee turnover rate using few selected features. Save the results for linear regression step.

LinearRegressionWithInteractions.ipynb: Load the results of the lasso regression. Choose the top 3 features and run linear regression using these features and their interactions. A model confirms the validity of the features and shows that interactions are not significantly big. Save the results for the use in Streamlit app.

Interactive Web App:

FeaturePredictionForTurnover_Streamlit.py: A Python script for "Streamlit" Web app. The app will interactively show how much each senior care center has to improve on key features in order to meet their "target" emplyee turn over rate.

For running the Web App, please install “Streamlit” from (https://www.streamlit.io/), and run the file by typing: streamlit run FeaturePredictionForTurnover_Streamlit.py

About

Jupyter notebook and "Streamlit" python scripts for identifying features that can predict employee turn over rates at 250 senior care centers across the US. Combines multiple repetition of Lasso regression and linear regression. Integrates U.S. census data, employee salary, and employee tenure with data on employee satisfaction and engagement to…

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published