Affinity Analysis, also known as Market Basket Analysis, is a modelling technique based on the premise that if you buy one group of things, you're more likely to buy another. Someone who buys peanut butter and bread, for example, is far more likely to want to buy jelly as well.

The Apriori Algorithm is a well-known Association Rule algorithm that is frequently used in market basket analysis. It is also thought to be more accurate than the AIS and SETM algorithms. It aids in the discovery of frequent itemsets in transactions as well as the identification of association rules between these items.

The database is represented by the FP growth method as a tree termed a frequent pattern tree or FP tree. The relationship between the itemsets will be maintained by this tree structure. Using one common object, the database is fractured. The fragmented section is referred to as a "pattern fragment."

The first strategy is Mine Merge, which does not require a specific mining algorithm and can be applied to FP-growth without any changes. The second is an FP-growth implementation of the general concept of Common Counting.

Internally, FP-growth is a method that does not necessitate the development of candidates. It employs an FP-tree data structure that eliminates the need for explicit candidate set construction, making the approach more suitable for big databases. The algorithm of frequent sets is discovered using the FP-tree.

Course Content

Getting Started with Machine Learning

Introduction to Machine Learning

Environment Setup Part 1

Environment Setup Part 2

Environment Setup Part 3

Quiz : Introduction to Machine Learning

Data Wrangling in Machine Learning

Importing Libraries and Dataset in Machine Learning

Handling Missing Data in Machine Learning

Handling Missing Data in Machine Learning - Practical

Encoding Categorical Data in Machine Learning

Encoding Categorical Data in Machine Learning - Practical

Splitting Dataset in Machine Learning

Splitting Dataset in Machine Learning - Practical

Normalizing the Data in Machine Learning - Part 1

Normalizing the Data in Machine Learning - Part 2

Finding Machine Learning Datasets

Exploratory Data Analysis in Machine Learning

Plotting Graphs in Machine Learning - Part 1

Plotting Graphs in Machine Learning - Part 2

Distribution Models in Machine Learning - Part 1

Distribution Models in Machine Learning - Part 2

Assignment : Data Preprocessing for Machine Learning

Quiz : Data Preprocessing for Machine Learning

Machine Learning Paradigms

Assignment : Machine Learning Paradigms

Quiz : Machine Learning Paradigms

Overview of Classification

Logistic Regression in Machine Learning

Logistic Regression in Machine Learning - Practical - 1

Logistic Regression in Machine Learning - Practical - 2

KNN in Machine Learning

KNN in Machine Learning Practical - 1

KNN in Machine Learning - Practical 2

Decision Trees for Classification in Machine Learning

Decision Trees for Classification in Machine Learning - Practical - 1

Decision Trees for Classification in Machine Learning - Practical - 2

Decision Tree Iterative Dichotomiser in Machine Learning - 3

Random Forest in Machine Learning

Support Vector Machine Classifier

Support Vector Machine Classifier - Practical - 1

Support Vector Machine Classifier - Practical - 2

Naive Bayes Classifier in Machine Learning

Naive Bayes Classifier in Machine Learning Practical - 1

Naive Bayes Classifier in Machine Learning Practical - 2

Evaluating Classification Models Performance in Machine Learning

Evaluating Classification Models Performance in Machine Learning Practical

Assignment : Supervised Learning Algorithms

Quiz : Supervised Learning Algorithms

Simple Linear Regression in Machine Learning

Simple Linear Regression in Machine Learning - Practical

Salary Prediction using Linear Regression in Machine Learning

Multi-Linear Regression in Machine Learning

Startup Prediction using Multiple Regression in Machine Learning

Support Vector Regressor in Machine Learning

Support Vector Regressor in Machine Learning - Practical - 1

Support Vector Regressor in Machine Learning Practical - 2

Decision Tree Regressor in Machine Learning

Decision Tree Regressor in Machine Learning Practical - 1

Decision Tree Regressor in Machine Learning Practical - 2

Regressor Model Selection in Machine Learning

Evaluating Regression Model Performance in Machine Learning

Evaluating Regression Model Performance in Machine Learning - Practical

Assignment : Regression Algorithms

Quiz : Regression Algorithms

Distance Metrics in Machine Learning

K-Means Clustering in Machine Learning

K-Means Clustering in Machine Learning - Practical

Mall Customers Prediction using K Means Clustering in Machine Learning

Hierarchical Clustering in Machine Learning - Agglomerative , Divisive

Agglomerative Clustering in Machine Learning - Practical

Divisive Clustering in Machine Learning - Practical

DBscan Spatial Clustering in Machine Learning

Mall Customers Prediction using Hierarchical Clustering in Machine Learning

Assignment : Unsupervised Learning Algorithms

Quiz : Unsupervised Learning Algorithms

Association Rule in Machine Learning - Apriori, FP Growth

Association Rule in Machine Learning - Apriori Practical

Market Basket Analysis using Apriori in Machine Learning