Data Science


Top 8 Most Used Machine Learning Algorithms in Python

With an increased demand for modern and reliable technology, machine learning (ML) and artificial intelligence (AI) have gained much popularity recently. Here is the list of the top eight most used machine learning algorithms in python.

With an increase in the demand for modern and reliable technology, machine learning (ML) and artificial intelligence (AI) have gained a lot of popularity. Nearly 77% of devices use machine learning facilities. ML is the application of AI that employs statistics for finding patterns in vast amounts of data sets. The platform that uses machine learning includes:

  • Search assistants
  • Voice assistants
  • Social media feeds

The demand for machine learning online courses is increasing rapidly because of the wide array of career opportunities offered in this field.

Machine Learning Algorithms in Python

Here is the list of the top eight most used machine learning algorithms in python:

  1. Linear Regression

Linear regression is one of the most supervised ML algorithms that observes features and predicts the outcome simultaneously. It is used for estimating absolute values based on the continuous variables.

It is the most popular python ML algorithm and is often under-appreciated. The best fit line is called regression line and is represented by equation Y=a*X+b, where,

  • Y- Dependent Variable
  • a- Slope
  • X- Independent Variable
  • b- Intercept

Linear regression is classified into two types:

  • Simple linear regression
  • Multiple linear regression
  1. Logistic Regression

It is a supervised classification that uses estimated discrete values like 0/1, yes/no, and true/false. It is based on independent variables. The logistic regression is used to predict the probability of an event and gives the value of output between 0 and 1.

  1. Decision Tree

A decision tree is the type of supervised learning algorithm that is used to predict classification problems. It uses both classification and regression. This model compares essential features with the determined conditional statements. A decision tree works on both categorical and continuous dependent variables.

  1. Support Vector Mechanism (SVM)

SVM is one of the essential machine learning algorithms in python that plots lines that divide different categories of the data. Here, we calculate the vector for optimizing the line, which helps to ensure that the closest point in each group lies far from the other.

  1. Naive Bayes

This classification is based on the Bayes theorem. It assumes independence between predictors and features in the class is unrelated to any other. Naive Bayes is easy to build and is helpful for large data sets. Thus, it is known to outperform comprehensive tasks effectively.

  1. k- Nearest Neighbors

This is a python machine learning algorithm that is used for classification and regression. K-NN is a simple algorithm that stores all the information and considers different centroids. Things to consider before selecting kNN:

  • It is expensive.
  • Variables should be normalized else higher range variables can bias it
  • Works on pre-processing stage more before going for k-NN
  1. k- Means

It is a supervised model that is used to solve clustering problems. The data are classified using several clusters.

  1. Random Forest

Random decision forests are used for various purposes like classification, regression, and other tasks. Tree vote provides a classification based on every new objective.

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Author avatar
Krishna Kushwaha

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