## Logistic Regression Customer Churn Prediction: Example

In today’s fast-paced and highly competitive business world, spanning across industries like telecommunications, finance, e-commerce, and more, the ability to predict and understand customer churn has emerged as a critical component of strategic business management. Whether it’s a telecom giant grappling with subscriber turnover, a fintech company aiming to retain its user base, or an e-commerce platform trying to reduce shopping cart abandonment, the implications of churn are vast and deeply impactful. This is where the role of logistic regression, a potent and versatile statistical method, comes into play. This blog delves into different aspects of training a logistic regression machine learning model for churn prediction, highlighting its universality and …

## GLM vs Linear Regression: Difference, Examples

Linear Regression and Generalized Linear Models (GLM) are both statistical methods used for understanding the relationship between variables. Understanding the difference between GLM and Linear Regression is essential for accurate model selection, tailored to data types and research questions. It’s crucial for predicting diverse outcomes, ensuring valid statistical inference, and is vital in interdisciplinary research. In this blog, we will learn about the differences between Linear Regression and GLM by delving into their distinct characteristics, suitable applications, and the importance of choosing the right model based on data type and research objective. What’s the difference between GLM & Linear Regression? Linear Regression and Generalized Linear Models (GLM) are two closely …

## MinMaxScaler vs StandardScaler – Python Examples

Last updated: 7th Dec, 2023 Feature scaling is an essential part of exploratory data analysis (EDA), when working with machine learning models. Feature scaling helps to standardize the range of features and ensure that each feature (continuous variable) contributes equally to the analysis. Two popular feature scaling techniques used in Python are MinMaxScaler and StandardScaler. In this blog, we will learn about the concepts and differences between these feature scaling techniques with the help of Python code examples, highlight their advantages and disadvantages, and provide guidance on when to use MinMaxScaler vs StandardScaler. Note that these are classes provided by sklearn.preprocessing module. As a data scientist, you will need to …

## Linear Regression in Machine Learning: Examples

Last updated: 6th Dec, 2023 In this post, the linear regression concepts in machine learning is explained with multiple real-life examples. Two types of regression models (simple/univariate and multiple/multivariate linear regression) are taken up for sighting examples. In case you are a machine learning or data science beginner, you may find this post helpful enough. You may also want to check a detailed post – What is Machine Learning? Concepts & Examples. What is Linear Regression? Linear regression is a machine learning concept that is used to build or train the models for solving supervised learning problems related to predicting continuous numerical value. Recall that the supervised learning problems represent …

## Lasso Regression in Machine Learning: Python Example

Last updated: 6th Dec, 2023 Lasso regression, sometimes referred to as L1 regularization, is a technique in linear regression that incorporates regularization to curb overfitting and enhance the performance of machine learning models. It works by adding a penalty term to the cost function that encourages the model to select only the most important features and set the coefficients of less important features to zero. This makes Lasso regression a popular method for feature selection and high-dimensional data analysis. In this post, you will learn concepts, formula, advantages and limitations of Lasso regression along with Python Sklearn examples. The other two similar forms of regularized linear regression are Ridge regression and …

## Logistic Regression in Machine Learning: Python Example

Last updated: 6th Dec, 2023 In this blog post, we will discuss the logistic regression machine learning algorithm with a python example. Logistic regression is a regression algorithm specifically designed to estimate the probability of an event occurring. For example, it can be used in the medical field to predict the likelihood of a patient developing a certain disease based on various health indicators, such as age, weight, and blood pressure. It is often used in machine learning applications. In this blog, we will learn about the logistic regression algorithm, use python to implement logistic regression model with IRIS dataset. What is Logistic Regression? The logistic regression algorithm is a …

## Using GridSearchCV with Logistic Regression Models: Examples

GridSearchCV method is a one of the popular technique for optimizing logistic regression models, automating the search for the best hyperparameters like regularization strength and type. It enhances model performance by incorporating cross-validation, ensuring robustness and generalizability to new data. This method saves time and ensures objective model selection, making it an essential technique in various domains where logistic regression is applied. Its integration with the scikit-learn library (sklearn.model_selection.GridSearchCV) simplifies its use in existing data pipelines, making it a valuable asset for both novice and experienced machine learning practitioners. How is GridSearchCV used with Logistic Regression? GridSearchCV is a technique used in machine learning for hyperparameter tuning. It is a …

## Handling Class Imbalance in Machine Learning: Python Example

Techniques for Handling Class Imbalance Class imbalance may not always impact performance, and using imbalance-specific methods can sometimes worsen results. Xu-Ying Liu, Jianxin Wu, and Zhi-Hua Zhou, Exploratory Undersampling for Class-Imbalance Learning Above said, there are different techniques such as the following for handling class imbalance when training machine learning models with datasets having imbalanced classes. Python packages such as Imbalanced Learn can be used to apply techniques related to under-sampling majority classes, upsampling minority classes, and SMOTE. In this post, techniques related to using class weight will be used for tackling class imbalance. How to create a Sample Dataset having Class Imbalance? In this section, you will learn about how to create an …

## Handling Class Imbalance using Sklearn Resample

Last updated: 5th Dec, 2023 The class imbalance problem in machine learning occurs when the classes in a dataset are not represented equally, leading to a significant difference in the number of instances for different classes. This imbalance can cause a classification model to be biased towards the majority class, resulting in poor performance on the minority class. Thus, the class imbalance hinders data scientists by challenging the development of accurate and fair models, as the skewed distribution can lead to misleading training predictions / outcomes and reduced effectiveness in real-world applications where minority classes are critical. In this post, you will learn about how to tackle class imbalance issue …

## Ordinary Least Squares Method: Concepts & Examples

Last updated: 5th Dec, 2023 Regression analysis is a fundamental statistical technique used in many fields, from finance, econometrics to social sciences. It involves creating a regression model for modeling the relationship between a dependent variable and one or more independent variables. The Ordinary Least Squares (OLS) method helps estimate the parameters of this regression model. Ordinary least squares (OLS) is a technique used in linear regression model to find the best-fitting line for a set of data points by minimizing the residuals (the differences between the observed and predicted values). It does so by estimating the coefficients of the linear regression model by minimizing the sum of the squared …

## Linear Regression vs Correlation: Examples

Linear regression and correlation are fundamental concepts in statistics, often used in data analysis to understand the relationship between two variables. Linear regression and correlation, while related, are not the same. They serve different purposes and provide different types of information. In this blog, we will explore each concept with examples to clarify their differences and applications. Linear Regression vs Correlation: Definition Linear Regression is a statistical method used for modeling the relationship between a dependent variable and one or more independent variables. The core idea is to find a linear equation that best describes this relationship, enabling the prediction of the dependent variable based on the values of the …

## One Sample T-test: Calculations, Formula & Examples

Last updated: 5th Dec, 2023 In statistics, the t-test is often used in research when the researcher wants to know if there is a significant difference between the mean of sample and the population, or whether there is a significant difference between the means of two groups (unpaired / independent or paired). There are three types of t-tests: the one sample t-test, two samples or independent samples t-test, and paired samples t-test. In this blog post, we will focus on the one sample t-test and explain with formula and examples. As data scientists, it is important for us to understand the concepts of t-test and how to use it in …

## Bias-Variance Trade-off in Machine Learning: Examples

Last updated: 5th Dec, 2023 The bias-variance trade-off is a fundamental concept in machine learning that presents a challenging dilemma for data scientists. It relates to the problem of simultaneously minimizing two sources of error that prevent supervised learning algorithms from generalizing beyond their training data. These two sources of error are related to Bias and Variance. Bias related errors refer to the error due to overly simplistic assumptions in the learning algorithm. Variance related errors refer to the error due to too much complexity in the learning algorithm. In this post, you will learn about the concepts of bias & variance in relation to the machine learning (ML) models. …

## Different Types of CNN Architectures Explained: Examples

Last updated: 4th Dec, 2023. In the fast-paced world of computer vision and image processing, the problem of image classification consistently stands out: the ability to effectively recognize and classify images. As we continue to digitize and automate our world, the demand for systems that can understand and interpret visual data is growing at an unprecedented rate. The challenge is not just about recognizing images – it’s about doing so accurately and efficiently. Traditional machine learning methods often fall short, struggling to handle the complexity and high dimensionality of image data. This is where Convolutional Neural Networks (CNNs) comes to rescue. And, there are different types of CNN architectures based …

## MongoDB – Commands to Check the Status of MongoDB Database

This article represents different commands which can be used to check the status of MongoDB database on Linux/Ubuntu. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. MongoDB Status Check Commands The following represents some of the commands that can be used to check the status of MongoDB database. Note that mongod represents the daemon process of MongDB databass and, primarily, used to manage database access. It is recommended to check the log file (/var/log/mongo/mongo.log) to get details. Following are some of the commands which can be used to get the status of Mongodb: service mongod status: Displays the status …

## Logit vs Probit Models: Differences, Examples

Logit and Probit models are both types of regression models commonly used in statistical analysis, particularly in the field of binary classification. This means that the outcome of interest can only take on two possible values / classes. In most cases, these models are used to predict whether or not something will happen in form of binary outcome. For example, a bank might want to know if a particular borrower might default on loan or otherwise. In this blog post, we will explain what logit and probit models are, and we will provide examples of how they can be used. As data scientists, it is important to understand the concepts …

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