To analyse and implement brute force algorithm, apriori algorithm and FP-Growth algorithm, in order to find the most efficient way to find frequent items and association rules.
#Datasets: Are online sources from amazon,costco, shoprite, walmart and juicebar is made-up.
- Frequent Itemset Generation: Identifies itemsets that appear frequently in the dataset based on a specified minimum support threshold.
- Association Rule Generation: Generates rules from the frequent itemsets and evaluates them against a minimum confidence threshold.
- Brute Force Method: A straightforward implementation that exhaustively searches through all possible itemsets and rules.
- CSV File Input: The ability to load transaction data from a CSV file.
- Both .ipynb and .py is available.
- Just run individual cells to run .ipynb
- To run .py use command python dmsmidtermproject.py
- Run pip install -r requirements.txt