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Neural Network
Variable Selection Techniques
Categorical Variables
Beginners Take: How Logistic Regression is related to Linear Regression Read more on this free link
Introduction to Logistic Regression Read more on this free link
What Is P-Value? In statistics, the p-value is the probability of obtaining results at least as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct Read more on this free link
5 Real-world Examples of Logistic Regression Application Read more on this free link
Logistic Regressions Examples Read more on this free link
Logistic regression in modeling and assessment of transport services Read more on this free link
Logistic Regression Guide Read more on this free link
Logistic Regression in real-life: building a daily productivity classification model Read more on this free link
Logistic Regression — Idea and Application - Python guide Read more on this free link
Making Predictions With Logistic Regression Read more on this free link
Logistic Regression using R Read more on this free link
Logistic Regression using XLMiner Read more on this free link
Logistic Regression Using Excel
Regression Analysis represents a set of statistical methods and techniques, which we use to evaluate the relationship between variables. These are one dependent variable (our target) and one or more independent variables (predictors). In financial modeling, we can employ regression analysis to estimate the strength of the relationship between variables and subsequently forecast this relationship’s future behavior Read more on this free link
What is machine learning? Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes Read more on this free link
This article will give a practical look at modeling a Multiple Regression model for the Gross Domestic Product (GDP) of a country Read more on this free link
Classification Vs. Clustering - A Practical Explanation Read more on this free link
Supervised Learning vs. Unsupervised Learning – A Quick Guide for Beginners Read more on this free link
Evaluating a model is a core part of building an effective machine learning model. There are several evaluation metrics, like confusion matrix, cross-validation, AUC-ROC curve, R squared and adjusted R squared etc. which are used for different kinds of problems Read more on this free link
Machine Learning | Classification vs Clustering Read more on this free link
The above content addresses the application of Machine Learning in Modern Business viz., Drone Management, Identification of Spam and Phishing messages etc. Read more on this free link
R-Squared is a relative term related to the mean model. R-squared value ranges from 0–1 and the more closer it is to 1 the more it explains about the variability of response data around it’s mean Read more on this free link
Machine Learning From Scratch: Classification, Regression, Clustering and Gradient Descent Read more on this free link
Supervised vs. Unsupervised Machine Learning
Linear Regression in Excel is explained in the video
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- Linear Regression for Machine Learning
- Creating a Linear Regression Model in Excel
- Application of Regression Analysis in Business
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- Using the Logistic Regression model in supporting decisions of establishing marketing strategies
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