feat: added production association rule module

Author: mvanwyk

docs/analysis_modules.md

@@ -92,3 +92,60 @@ time_plot(
     move_legend_outside=True,
 )
 ```
+
+## Analysis Modules
+
+### Product Association Rules
+
+The product association module implements functionality for generating product association rules, a powerful technique
+in retail analytics and market basket analysis.
+
+Product association rules are used to uncover relationships between different products that customers tend to purchase
+together. These rules provide valuable insights into consumer behavior and purchasing patterns, which can be leveraged
+by retail businesses in various ways:
+
+1. Cross-selling and upselling: By identifying products frequently bought together, retailers can make targeted product
+   recommendations to increase sales and average order value.
+
+2. Store layout optimization: Understanding product associations helps in strategic product placement within stores,
+   potentially increasing impulse purchases and overall sales.
+
+3. Inventory management: Knowing which products are often bought together aids in maintaining appropriate stock levels
+   and predicting demand.
+
+4. Marketing and promotions: Association rules can guide the creation ofeffective bundle offers and promotional
+   campaigns.
+
+5. Customer segmentation: Patterns in product associations can reveal distinct customer segments with specific
+   preferences.
+
+6. New product development: Insights from association rules can inform decisions about new product lines or features.
+
+The module uses metrics such as support, confidence, and uplift to quantifythe strength and significance of product
+associations:
+
+- Support: The frequency of items appearing together in transactions.
+- Confidence: The likelihood of buying one product given the purchase of another.
+- Uplift: The increase in purchase probability of one product when another is bought.
+
+Example:
+
+```python
+from pyretailscience.product_association import ProductAssociation
+
+pa = ProductAssociation(
+    df,
+    value_col="product_name",
+    group_col="transaction_id",
+)
+pa.df.head()
+```
+<!-- markdownlint-disable MD013 -->
+| product_name_1   | product_name_2               |  occurrences_1 |  occurrences_2 |  cooccurrences |  support | confidence | uplift |
+|:-----------------|:-----------------------------|---------------:|---------------:|---------------:|---------:|-----------:|-------:|
+| 100 Animals Book | 100% Organic Cold-Pressed... |             78 |             78 |              1 | 0.000039 |  0.0128205 |   4.18 |
+| 100 Animals Book | 20K Sousaphone               |             78 |             81 |              3 | 0.000117 |  0.0384615 |  12.10 |
+| 100 Animals Book | 360 Sport 2.0 Boxer Briefs   |             78 |             79 |              1 | 0.000039 |  0.0128205 |   4.13 |
+| 100 Animals Book | 4-Series 4K UHD              |             78 |             82 |              1 | 0.000039 |  0.0128205 |   3.98 |
+| 100 Animals Book | 700S Eterna Trumpet          |             78 |             71 |              1 | 0.000039 |  0.0128205 |   4.60 |
+<!-- markdownlint-enable MD013 -->

docs/api/product_association.md

@@ -0,0 +1,3 @@
+# Product Associations
+
+::: pyretailscience.product_association