A company wants to know more about their employees. This company has gotten a data set from their sister company about elements about people staying/leaving. Now that they’ve hired a data analyst, they want to see if we can accurately build a model that can predict who will stay or go.
Part 1:
Build a Logistic Regression model to predict, LeaveOrNot (leave is 1 and not is 0). Discuss the accuracy and fit of the model. Talk about which features were important from the model.
Part 2:
Build a Random Forest model to predict LeaveOrNot. Discuss the accuracy, fit, feature importance and some of the challenges that you had.
Part 3:
Build a Neural Network model to predict handwriting. Discuss the accuracy and some of the challenges that you had.
Part 4:
Using multiple benchmarking metrics (as covered in class) to compare and contrast both models. Summarize your findings and suggest a final model for the school to use. Focus on business interpretation, but your conclusion should reveal your reasoning on why/why not this model could be used based on benchmarking metrics.
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