Apriori Analysis
- Hospital Data -

Before Data Transformation Snapshot

This is the original hospital dataset containing attributes like:

• Hospital Type, Hospital Ownership, ZIP Code, and various performance metrics. Many fields include "Not Available" or missing values, requiring data cleaning before modeling.

After Data Transformation Snapshot

This is the preprocessed binary matrix used as input for the Apriori algorithm.

Each row represents a hospital, and each column represents a hospital type or ownership category (e.g., Proprietary, Psychiatric, Voluntary non-profit - Private)

True = hospital has that attribute; False = does not

Results

This table shows the number of frequent association rules found under various combinations of:

• min_support (minimum frequency of pattern)

• min_lift (minimum strength of association)

As expected:

• Lower support and lift thresholds yield more rules (e.g., 22 rules at 0.01 support and 1.0 lift)

• Higher thresholds narrow results down to stronger but rarer rules (e.g., only 2 rules at 0.10 support and 1.5 lift)