…lift)

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• Documentation change?

This pull request adds a Custom Apriori algorithm implementation that finds frequent itemsets and generates association rules using support, confidence, and lift.

The implementation is inspired by the existing Apriori algorithm in the repo but adds:

1 - Support, confidence, and lift calculations for association rules.

2 - Confidence and lift as separate class methods for clarity and reuse.

3 - A load_data() example for demonstration and doctest purposes.

4 - Proper filtering of rules using minimum support, confidence, and lift thresholds.

References:

• Wikipedia: Apriori algorithm

• Example dataset: included in load_data() function for doctest.

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".