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Data Science Association Rule Learning use case. Python programming language and its libraries combined together and R language in addition form the powerful tools for solving Association Rule Learning tasks. Association rule learning is a rule-based machine learning ML method for detecting possible relations between variables in large datasets. It's purpose is to identify powerful rules discovered in datasets using some measures of interestingness. https://python-code.pro/association-rule-learning-python-r-cheatsheets/

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Random rhetorical question: How did the person who first invented the clock, know what time is was? https://weird-jokes.com/jokes-directory/funny-rhetorical-questions/

Data Science Clustering use case. Python programming language and its libraries combined together and R language in addition form the powerful tools for solving Clustering analysis tasks. Cluster analysis or simply clustering is a branch of machine learning ML which mainly dealt with unsupervised task and usually involves automatically discovering natural grouping in data. Unlike supervised learning (like predictive modeling), clustering algorithms only interpret the input data and find natural groups or clusters using given features. In other words Clustering techniques apply when there is no class to be predicted but rather when the instances are to be divided into natural groups - clusters. https://python-code.pro/clustering-models-python-r-cheatsheets/

Random rhetorical question: Do you realize every time you put your glasses on you make a spectacle of yourself? https://weird-jokes.com/jokes-directory/funny-rhetorical-questions/

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Data Science Classification use case. Python programming language and its libraries combined together and R language in addition form the powerful tools for solving Classification analysis tasks. Classification is one of the most fundamental concepts in data science. Classification is a task that requires the use of machine learning algorithms that learn how to assign a class label to examples from the problem domain. From a modeling perspective, classification requires a training dataset with many examples of inputs and outputs from which to learn.A model will use the training dataset and will calculate how to best map examples of input data to specific class labels. As such, the training dataset must be sufficiently representative of the problem and have many examples of each class label. https://python-code.pro/classification-models-python-r-cheatsheets/

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Python for Regression. Python programming language and its libraries combined together form a powerful tool for solving Regression analysis tasks. Regression study is a predictive modelling method that analyzes the relation between the target or dependent variable and features or independent variables in a dataset. The different types of regression analysis methods are used when the target and independent features described by a linear or non-linear relationships between each other, and the target variable contains continuous values. The regression technique gets used mainly to determine the predictor strength, forecast trends, time series, and sometimes in case of cause & effect relation. Regression analysis is the basic technique to solve the regression problems in machine learning ML using data models. It consists of determining the best fit line, which is a line that passes through all the data points in such a way that distance of the line from each data point is optimal/minimized. https://python-code.pro/regression-models-python-cheatsheets/

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What is Scikit Learn ? If you’re dealing with Machine Learning in Python, Scikit Learn is considered as the gold standard. Scikit-learn is an open source Python library that provides a wide selection of supervised and unsupervised learning algorithms. It implements a range of machine learning tools performing preprocessing, cross-validation and visualization using a unified interface. https://python-code.pro/scikit-learn-for-data-science-cheat-sheet/

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Data preparation with SQL. After using SQL (structured query language) for data analysis let's dive deeper in data handling process and concentrate on dataset preparation task. We will see how describe input data, work with attributes, handle missing data, find and filter outliers, encode primary data, split and combine datasets. Although SQL is not considered as modern tool as Python for data handling, but it gives you nice to have additional instrumets in your data exploration journey. https://python-code.pro/dataset-preparation-with-sql/

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