This project performs market basket analysis using the Apriori algorithm implemented with the apyori library. The goal is to generate frequent itemsets and association rules from transactional data, helping to identify relationships between items that frequently co-occur in transactions.
- Frequent Itemset Generation: Identifies sets of items that frequently appear together in transactions.
- Association Rule Mining: Discovers association rules that describe how the occurrence of one item is related to the occurrence of another.
The Apriori algorithm is configured with the following parameters:
- Minimum Support (
min_sup): The minimum proportion of transactions that must contain an itemset for it to be considered frequent. - Minimum Confidence (
min_conf): The minimum confidence threshold for generating association rules. - Minimum Lift (
min_lift): The minimum lift value for association rules, indicating the strength of a rule. - Minimum Length (
min_length): The minimum number of items in an itemset to be considered for rule generation.
- Python: The primary programming language used for this analysis.
- Pandas: For data manipulation and preparation.
- Apyori: For implementing the Apriori algorithm.
- Jupyter Notebook: For interactive data analysis and visualization.
- Clone the repository:
git clone https://github.com/Aakashgone10/Market_basket_analysis.git