Complete Machine Learning Course > Association Rule Mining

Market Basket Analysis using Apriori in Machine Learning

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The Apriori technique is used to extract common itemsets from a transactional database and create association rules. The terms "confidence" and "support" are employed. The frequency of occurrence of items is referred to as support, while confidence is a conditional probability. An item set is made up of the items in a transaction.

The Apriori method was the first algorithm for frequent itemset mining to be proposed. To decrease the search space, this method employs two steps: "join" and "prune." It is an iterative method for identifying the most common itemsets.

Determines the importance of certain itemsets. The support function aids in the identification of various levels of importance in itemsets. With the aid of unneeded itemet reduction, storage space is lowered. The algorithm's accuracy and efficiency were improvised.

The following are some of the advantages of apriori: Among the association rule learning algorithms, this is the simplest and most straightforward. The resulting rules are simple to understand and express to end users.

Some related enhancements are made based on the inherent flaws of the Apriori algorithm:

  • Using a new database mapping method to prevent continually scanning the database
  • prune frequent itemsets and candidate items even more in order to increase joining efficiency
  • Counting support using the overlap technique

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Course Content

  1. Getting Started with Machine Learning
    4m 4s

  1. Introduction to Machine Learning
    13m 51s
  2. Environment Setup Part 1
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  3. Environment Setup Part 2
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  4. Environment Setup Part 3
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  1. Data Wrangling in Machine Learning
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  2. Importing Libraries and Dataset in Machine Learning
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  3. Handling Missing Data in Machine Learning
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  4. Handling Missing Data in Machine Learning - Practical
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  5. Encoding Categorical Data in Machine Learning
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  6. Encoding Categorical Data in Machine Learning - Practical
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  7. Splitting Dataset in Machine Learning
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  8. Splitting Dataset in Machine Learning - Practical
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  9. Normalizing the Data in Machine Learning - Part 1
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  10. Normalizing the Data in Machine Learning - Part 2
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  11. Finding Machine Learning Datasets
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  12. Exploratory Data Analysis in Machine Learning
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  13. Plotting Graphs in Machine Learning - Part 1
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  15. Distribution Models in Machine Learning - Part 1
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  17. Assignment : Data Preprocessing for Machine Learning
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  1. Machine Learning Paradigms
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  2. Logistic Regression in Machine Learning
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  13. Evaluating Regression Model Performance in Machine Learning
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  1. Association Rule in Machine Learning - Apriori, FP Growth
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  3. Market Basket Analysis using Apriori in Machine Learning
    17m 50s
  4. FP Growth in Machine Learning
    7m 43s
  5. Market Basket Analysis using FP Growth in Machine Learning
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  6. Assignment : Association Rule Mining
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  4. Q Learning in Machine Learning
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  1. Course Summary
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  1. Interview Questions Part 1
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  3. Interview Questions Part 3
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  1. Career Guideline
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