maskey71098 / association-rule-learning

Association-Rule-Learning

Implementation of Association rule learning using Apriori and Eclat. Association rule learning is a type of unsupervised learning technique that checks for the dependency of one data item on another data item and maps accordingly so that it can be more profitable. It tries to find some interesting relations or associations among the variables of the dataset. It is based on different rules to discover the interesting relations between variables in the database.

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Installations

pip install numpy 
pip install pandas
pip install apyori

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Table of Contents

S.N	Association Learning Algorithm	Dataset Used
1.	Apriori	Market_Basket_Optimisation.csv
2.	Eclat	Market_Basket_Optimisation.csv

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Apriori Algortihm

The Apriori algorithm uses frequent itemsets to generate association rules, and it is designed to work on the databases that contain transactions. With the help of these association rule, it determines how strongly or how weakly two objects are connected. This algorithm uses a breadth-first search and Hash Tree to calculate the itemset associations efficiently. It is the iterative process for finding the frequent itemsets from the large dataset.

Process
Step 1: Set a minimum support and confidence.
Step 2: Take all the subsets in transaction having higher support than the minimum support.
Step 3: Take all the rules of these subsets having higher confidence than minimum confidence.
Step 4: Sort the rules by decreasing lift.

Steps Involved:

1. Importing the libraries.
2. Data Preprocessing.
3. Training the Apriori model on dataset.
4. Visualising the result.
5. Putting the results well organized into a Pandas DataFrame.
6. Displaying the sorted result in the descending order.

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Observation:

The association rules generated and sorted in descending order according to theirs lifts:

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Eclat Algorithm

The ECLAT algorithm stands for Equivalence Class Clustering and bottom-up Lattice Traversal. It is one of the popular methods of Association Rule mining. It is a more efficient and scalable version of the Apriori algorithm. While the Apriori algorithm works in a horizontal sense imitating the Breadth-First Search of a graph, the ECLAT algorithm works in a vertical manner just like the Depth-First Search of a graph. This vertical approach of the ECLAT algorithm makes it a faster algorithm than the Apriori algorithm.

Process
Step 1: Set a minimum support.
Step 2: Take all these subsets in transactions having a higher support than minimum support.
Step 3:" Sort these supports by decreasing support.

Steps Involved:

1. Importing the libraries.
2. Data Preprocessing.
3. Training the Eclat model on dataset.
4. Visualising the result.
5. Putting the results well organized into a Pandas DataFrame.
6. Displaying the sorted result in the descending order.

Observation

The association rules generated and sorted in descending order.

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