An introduction to Object-Oriented Programming for Data Scientists

First of all, what is Object-Oriented Programming? This is what is known as a Programming paradigm, which essentially means that it is a specific way of doing something or structuring… 55 minutes ago

Best String Super Skills you must have: REGEX

When working with data, there is always the possibility of having to deal with text. Be prepared for when the time arrives, you will be finding, processing and dealing pretty well with alphanumeric… 55 minutes ago

A Beginners Guide to Logistic Regression in Python

In statistics, a logistic model is applied to predict a binary dependent variable. When we are working with a data set where we need to predict 1s and 0s we usually rely on… 55 minutes ago

Improve Model Performance using Feature Importance

Machine Learning model performance is the most factor in selecting a particular model. In order to select a machine learning model, we can look at certain metrics that can help us select the best… 55 minutes ago

Employees’ Attrition — How Catboost and Shap can help you understand it!

Employees’ Attrition — How Catboost and Shap can help you understand it!. Discover how the use of Scikit and Catboost models can help you deal with an unbalanced dataset and why SHAP is a great tool to explain…. 55 minutes ago

Facebook Sets a New Milestone in Language Translation

Language translation is a challenging NLP task which requires an enormous amount of data to train the model. Nevertheless, a lot of progress has been made in the past few years. Recently, Facebook… 55 minutes ago

Measures of variability and z-scores. Why, when and how to use them?

Variability refers to how “spread out” or dispersed data is, and how different each score is from the other. For example, let’s think of the customers of a particular store and their age. In the case… 55 minutes ago

From Hello world to Quantum computing of dashboard in Python (Part 1 of 3)

The New York Times on an average Sunday contains more information than a Renaissance-era person had access to in his entire lifetime.We’re getting better and better at collecting data, but we lag in…

Understanding Transfer Learning & Image Augmentation

Have you ever taken part in an image classification competition and felt that your model isn’t as good as the person who is on top? Then I think this blog is for you. Step 3: We will create…

Elucidating Bias, Variance, Under-fitting, and Over-fitting.

Overfitting, underfitting, and bias-variance tradeoff are foundational concepts in machine learning. They are important because they explain the state of a model based on their performance. The best…