Arunangshu Das

Trust me, I'm a software developer—debugging by day, chilling by night.

Z-Score

Z-Score

In the vast realm of statistics, there exists a powerful tool known as the Z-score. Whether you’re a seasoned data scientist or just starting to dip your toes into the world of statistics, understanding the Z-score can prove invaluable in

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Normal Distribution

Normal Distribution

Statistics is a powerful tool used to make sense of the world around us, helping us understand and interpret data in various fields ranging from science and engineering to finance and social sciences. At the heart of statistical analysis lies

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Five Number Summary

Five Number Summary

In the realm of statistics, summarizing data is essential for gaining insights and making informed decisions. One powerful technique for summarizing numerical data is the Five Number Summary. Whether you’re a data scientist, a business analyst, or a student learning

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Confusion Matrix

Confusion Matrix

In the realm of machine learning and data science, the confusion matrix is a fundamental tool used to evaluate the performance of classification models. It provides a detailed breakdown of the predictions made by a model, allowing us to assess

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Logistic Regression

Logistic Regression

Introduction:In the realm of statistics and machine learning, logistic regression stands as one of the fundamental techniques for classification tasks. Despite its name, logistic regression is primarily used for binary classification problems. However, it can also be extended to handle

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Elastic Net Regression

Elastic Net Regression

In predictive modeling and statistical analysis, Elastic Net Regression stands as a versatile technique that combines the strengths of both Lasso and Ridge Regression. This hybrid approach offers a robust solution to the challenges posed by multicollinearity, overfitting, and feature

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Lasso Regression

Lasso Regression

In the expansive realm of predictive modeling and statistical analysis, Lasso Regression stands tall as a powerful technique for feature selection and regularization. Its ability to shrink coefficients towards zero while simultaneously performing variable selection makes it a cornerstone in

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Ridge Regression

Ridge Regression

In the vast landscape of statistical modeling and machine learning, Ridge Regression stands as a prominent technique for tackling multicollinearity and overfitting issues often encountered in linear regression. Its robustness and effectiveness have made it a cornerstone in predictive modeling,

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