Found 19 Articles for Scikit-learn

In today's highly competitive business landscape, customer churn (the loss of customers) is a critical challenge that many companies face. Being able to predict which customers are at risk of churning can help businesses take proactive measures to retain those customers and maintain long-term profitability. In this article, we will explore how to build a machine learning model for customer churn prediction using Python and the scikit-learn library. The customer churn prediction model that we will develop aims to analyze customer data and predict whether a customer is likely to churn or not. By leveraging the power of machine learning ... Read More

Introduction Python could be a flexible programming dialect with an endless environment of libraries and systems. One prevalent library is scikit−learn, which gives a wealthy set of devices for machine learning and data investigation. In this article, we are going to dig into the concept of pipelines in Python and scikit−learn. Pipelines are an effective apparatus for organizing and streamlining machine learning workflows, permitting you to chain together numerous information preprocessing and modeling steps. We'll investigate three diverse approaches to building pipelines, giving a brief clarification of each approach and counting full code and yield. Understanding pipelines in ... Read More

Understanding various techniques for estimating covariance matrices is essential in the field of machine learning. The Scikit-Learn package has two popular covariance estimation methods, which will be compared in this article. Ledoit-Wolf Oracle Approximating Shrinkage (OAS) Estimation. Introduction to Covariance Estimation Before we begin comparing, let's establish covariance estimation. In statistics and data analysis, covariance estimation is a technique used to understand and quantify the relationship between multiple dimensions or features in your data collection. This becomes much more important when working with multidimensional data sets because understanding the relationships between various variables may improve the performance of your machine ... Read More

Data generalization, also known as data summarization or data compression, is the process of reducing the complexity of large datasets by identifying and representing patterns in the data in a more simplified form. This is typically done in order to make the data more manageable and easier to analyze and interpret. Introduction to Data Generalization Data generalization is a crucial step in the data analysis process, as it allows us to make sense of large and complex datasets by identifying patterns and trends that may not be immediately apparent. By simplifying the data, we can more easily identify relationships, classify ... Read More

Linear classification is one of the simplest machine learning problems. To implement linear classification, we will be using sklearn’s SGD (Stochastic Gradient Descent) classifier to predict the Iris flower species. Steps You can follow the below given steps to implement linear classification with Python Scikit-learn − Step 1 − First import the necessary packages scikit-learn, NumPy, and matplotlib Step 2 − Load the dataset and build a training and testing dataset out of it. Step 3 − Plot the training instances using matplotlib. Although this step is optional, it is good practice to plot the instances for more clarity. Step 4 − Create ... Read More

Iris, a multivariate flower dataset, is one of the most useful Pyhton scikit-learn datasets. It has 3 classes of 50 instances each and contains the measurements of the sepal and petal parts of three Iris species namely Iris setosa, Iris virginica, and Iris versicolor. Along with that Iris dataset contains 50 instances from each of these three species and consists of four features namely sepal_length (cm), sepal_width (cm), petal_length (cm), petal_width (cm). We can use Principal Component Analysis (PCA) to transform IRIS dataset into a new feature space with 2 features. Steps We can follow the below given steps to ... Read More

Sklearn DIGITS dataset has 64 features as each image of the digit is of size 8 by 8 pixels. We can use Principal Component Analysis (PCA) to transform Scikit-learn DIGITS dataset into new feature space with 2 features. Transforming 64 features dataset to 2-feature dataset will be a big reduction in the size of data and we’ll lose some useful information. It will also impact the classification accuracy of ML model. Steps to Transform DIGITS Dataset to 2-feature Dataset We can follow the below given steps to transform DIGITS dataset to 2-feature dataset using PCA − First, import the ... Read More

Dimensionality reduction, an unsupervised machine learning method is used to reduce the number of feature variables for each data sample selecting set of principal features. Principal Component Analysis (PCA) is one of the popular algorithms for dimensionality reduction available in Sklearn. In this tutorial, we perform dimensionality reduction using principal component analysis and incremental principal component analysis using Python Scikit-learn (Sklearn). Using Principal Component Analysis (PCA) PCA is a statistical method that linearly project the data into new feature space by analyzing the features of original dataset. The main concept behind PCA is to select the “principal” characteristics of the ... Read More

Random projection is a dimensionality reduction and data visualization method to simplify the complexity of highly dimensional data. It is basically applied to the data where other dimensionality reduction techniques such as Principal Component Analysis (PCA) can not do the justice to data. Python Scikit-learn provides a module named sklearn.random_projection that implements a computationally efficient way to reduce the data dimensionality. It implements the following two types of an unstructured random matrix − Gaussian Random Matrix Sparse Random Matrix Implementing Gaussian Random Projection For implementing Gaussian random matrix, random_projection module uses GaussianRandomProjection() function which reduces the dimensionality by ... Read More

Naïve Bayes classification, based on the Bayes theorem of probability, is the process of predicting the category from unknown data sets. Scikit-learn has three Naïve Bayes models namely, Gaussian Naïve Bayes Bernoulli Naïve Bayes Multinomial Naïve Bayes In this tutorial, we will learn Gaussian Naïve Bayes and Bernoulli Naïve Bayes classifiers using Python Scikit-learn (Sklearn). Gaussian Naïve Bayes Classifier Gaussian naïve bayes classifier is based on a continuous distribution characterized by mean and variance. With the help of an example, let’s see how we can use the Scikit-Learn Python ML library to build a Gaussian Naïve Bayes classifier. ... Read More