This course covers the working Principle of Association Mining and its various concepts like Support, Confidence, and Life in a very simplified manner. This course discusses about Naive Algorithm and Apriori Algorithm for finding Association Mining rules by taking lot of examples. All of these algorithms has been explained by taking working examples.

Who this course is for:

Students taking Machine Learning or Data Mining Course

Machine Learning Enthusiast

Students preparing for placement tests and interviews

When working with machine learning, it’s easy to try them all out without understanding what each model does, and when to use them. In this cheat sheet, you’ll find a handy guide describing the most widely used machine learning models, their advantages, disadvantages, and some key use-cases.

Supervised Learning

Supervised learning models are models that map inputs to outputs, and attempt to extrapolate patterns learned in past data on unseen data. Supervised learning models can be either regression models, where we try to predict a continuous variable, like stock prices—or classification models, where we try to predict a binary or multi-class variable, like whether a customer will churn or not. In the section below, we’ll explain two popular types of supervised learning models: linear models, and tree-based models.

Linear Models

In a nutshell, linear models create a best-fit line to predict unseen data. Linear models imply that outputs are a linear combination of features. In this section, we’ll specify commonly used linear models in machine learning, their advantages, and disadvantages.

Algorithm

Description

Applications

Advantages

Disadvantages

Linear Regression

A simple algorithm that models a linear relationship between inputs and a continuous numerical output variable

Stock Price PredictionPredicting housing pricesPredicting customer lifetime value

Explainable methodInterpretable results by its output coefficientFaster to train than other machine learning models

Assumes linearity between inputs and outputSensitive to outliersCan underfit with small, high-dimensional data

Logistic Regression

A simple algorithm that models a linear relationship between inputs and a categorical output (1 or 0)

Interpretable and explainableLess prone to overfitting when using regularizationApplicable for multi-class predictions

Assumes linearity between inputs and outputsCan overfit with small, high-dimensional data

Ridge Regression

Part of the regression family — it penalizes features that have low predictive outcomes by shrinking their coefficients closer to zero. Can be used for classification or regression

Predictive maintenance for automobilesSales revenue prediction

Less prone to overfittingBest suited where data suffer from multicollinearityExplainable & interpretable

All the predictors are kept in the final modelDoesn’t perform feature selection

Lasso Regression

Part of the regression family — it penalizes features that have low predictive outcomes by shrinking their coefficients to zero. Can be used for classification or regression

Predicting housing pricesPredicting clinical outcomes based on health data

Less prone to overfittingCan handle high-dimensional dataNo need for feature selection

Can lead to poor interpretability as it can keep highly correlated variables

Tree-based models

In a nutshell, tree-based models use a series of “if-then” rules to predict from decision trees. In this section, we’ll specify commonly used linear models in machine learning, their advantages, and disadvantages.

Algorithm

Description

Applications

Advantages

Disadvantages

Decision Tree

Decision Tree models make decision rules on the features to produce predictions. It can be used for classification or regression

Explainable and interpretableCan handle missing values

Prone to overfittingSensitive to outliers

Random Forests

An ensemble learning method that combines the output of multiple decision trees

Credit score modelingPredicting housing prices

Reduces overfittingHigher accuracy compared to other models

Training complexity can be highNot very interpretable

Gradient Boosting Regression

Gradient Boosting Regression employs boosting to make predictive models from an ensemble of weak predictive learners

Predicting car emissionsPredicting ride-hailing fare amount

Better accuracy compared to other regression modelsIt can handle multicollinearity It can handle non-linear relationships

Sensitive to outliers and can therefore cause overfittingComputationally expensive and has high complexity

XGBoost

Gradient Boosting algorithm that is efficient & flexible. Can be used for both classification and regression tasks

Churn predictionClaims processing in insurance

Provides accurate resultsCaptures non-linear relationships

Hyperparameter tuning can be complexDoes not perform well on sparse datasets

LightGBM Regressor

A gradient boosting framework that is designed to be more efficient than other implementations

Predicting flight time for airlinesPredicting cholesterol levels based on health data

Can handle large amounts of dataComputational efficient & fast training speedLow memory usage

Can overfit due to leaf-wise splitting and high sensitivityHyperparameter tuning can be complex

Unsupervised Learning

Unsupervised learning is about discovering general patterns in data. The most popular example is clustering or segmenting customers and users. This type of segmentation is generalizable and can be applied broadly, such as to documents, companies, and genes. Unsupervised learning consists of clustering models, that learn how to group similar data points together, or association algorithms, that group different data points based on pre-defined rules.

Clustering models

Algorithm

Description

Applications

Advantages

Disadvantages

K-Means

K-Means is the most widely used clustering approach—it determines K clusters based on euclidean distances

Customer segmentationRecommendation systems

Scales to large datasetsSimple to implement and interpretResults in tight clusters

Requires the expected number of clusters from the beginningHas troubles with varying cluster sizes and densities

Hierarchical Clustering

A “bottom-up” approach where each data point is treated as its own cluster—and then the closest two clusters are merged together iteratively

Fraud detectionDocument clustering based on similarity

There is no need to specify the number of clustersThe resulting dendrogram is informative

Doesn’t always result in the best clusteringNot suitable for large datasets due to high complexity

Gaussian Mixture Models

A probabilistic model for modeling normally distributed clusters within a dataset

Customer segmentationRecommendation systems

Computes a probability for an observation belonging to a clusterCan identify overlapping clustersMore accurate results compared to K-means

Requires complex tuningRequires setting the number of expected mixture components or clusters

Association

Algorithm

Description

Applications

Advantages

Disadvantages

Apriori Algorithm

Rule based approach that identifies the most frequent itemset in a given dataset where prior knowledge of frequent itemset properties is used