Python programming, Statistics,
Data Science and Machine Learning 💻
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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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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/

P value. Python programming, Statistics,
Data Science and Machine Learning 💻
WEB SITE 👉 python-code.pro
Twitter 🌠 twitter.com/abratus

Python programming, Statistics,
Data Science and Machine Learning 💻
WEB SITE 👉 python-code.pro
Twitter 🌠 twitter.com/abratus
Telegram ⚡ t.me/pythoncodepro

Coding with you 💻
WEB SITE 👉 python-code.pro
Twitter 🌠 twitter.com/abratus

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/

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:

How did the person who first invented the clock, know what time is was?

https://weird-jokes.com/jokes-directory/funny-rhetorical-questions/

WEB SITE 👌https://weird-jokes.com
Twitter 🌠 https://twitter.com/DuhovnayaS

Random rhetorical question:

Is it really wise to invest with somebody called a "broker"?

https://weird-jokes.com/jokes-directory/funny-rhetorical-questions/

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/

Python programming, Statistics,
Data Science and Machine Learning 💻
WEB SITE 👉 https://python-code.pro/
Twitter 🌠 https://twitter.com/abratus

Python programming, Statistics,
Data Science and Machine Learning 💻
WEB SITE 👉 https://python-code.pro/
Twitter 🌠 https://twitter.com/abratus
Telegram ⚡️ https://t.me/pythoncodepro

Random rhetorical question:

Do they have a Chinatown…in China?

https://weird-jokes.com/jokes-directory/funny-rhetorical-questions/

Python programming, Statistics,
Data Science and Machine Learning 💻
WEB SITE 👉 https://python-code.pro/
Twitter 🌠 https://twitter.com/abratus

Random rhetorical question:

Is a bathroom still a bathroom if there is no bath in the room?

https://weird-jokes.com/jokes-directory/funny-rhetorical-questions/

Python programming, Statistics,
Data Science and Machine Learning 💻
WEB SITE 👉 python-code.pro
Twitter 🌠 twitter.com/abratus

Data Science Reinforcement Learning use case.

Python programming language and its libraries combined together and R language in addition form the powerful tools for solving Reinforcement Learning tasks.

Reinforcement learning - RL - is an branch of machine learning ML which busy with how intelligent agents should take actions in an environment in order to maximize the notion of cumulative reward.

Reinforcement learning is one of three basic machine learning concepts next to supervised learning and unsupervised learning.

Reinforcement learning is distinguished from supervised learning in not needing labelled input/output pairs be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).

https://python-code.pro/reinforcement-learning-python-r-cheatsheets/