Machine Learning Exercise

We are interested in an analytic and/or predictive model to predict claims that would be denied with the following Denial.Reason.Code: F13, J8G, JO5, JB8, JE1, JC9, JF1, JF9, JG1, JPA and JES.

Basic Work Flow

graph TB
    P[Prepare Data]-->C[Choose an Algorithm]
    C-->F[Fit a Model]
    F-->M[Choose a Validation Method]
    M-->E[Examine Fit and Update Until Satisfied]
    E-->FP[Use Fitted Model for Predictions]

Understand Your Data

• How To Load Machine Learning Data in Python
• Understand Your Data With Descriptive Statistics
• Understand Your Machine Learning Data With Descriptive Statistics in Python
• Visualize Machine Learning Data in Python With Pandas

Preprocessing data

• Categorical Varible to digital
• Object to float
• Simple Methods to deal with Categorical Variables in Predictive Modeling

Understand Your Data on the processed data

• Correlations, find the columns need to be reduced
• Skew, Histograms, Box, find the columns need to be normalized
• Scater plat, find the category columns that does not help much

Transform Data

• Preprocessing data
• How to Prepare Data For Machine Learning
• How To Prepare Your Data For Machine Learning in Python with Scikit-Learn

Feature Selection

• An Introduction to Feature Selection
• Feature Selection in Python with Scikit-Learn
• machine_learning_mastery_with_python_sample
• Data Dimensionality Reduction

Examples of dimensionality reduction methods include Principal Component Analysis, Singular Value Decomposition and Sammon’s Mapping.