Data Science
Top 20 Pandas Functions You Aren't Using, Which You Should Be Using Paid Members Public
This blog post will explore 20 powerful and unique Pandas functions that can significantly enhance your data analysis workflow. We will be using the famous Iris dataset as an example to demonstrate each function. The Iris dataset contains four features: Sepal Length, Sepal Width, Petal Length, and Petal Width, along
Entropy, information gain, and Gini impurity(Decision tree splitting criteria) Paid Members Public
Decision trees are supervised machine-learning models used to solve classification and regression problems. They help to make decisions by breaking down a problem with a bunch of if-else-then-like evaluations that result in a tree-like structure. For quality and viable decisions to be made, a decision tree builds itself by splitting
Decision Trees and Random Forests(Building and optimizing decision tree and random forest models) Paid Members Public
In the modern world, so much data is present on the internet. Organizations need efficient and rigorous algorithms to handle these huge chunks of data, make practical analyses, and provide appropriate decisions relevant to maximizing their profits and market presence. There are such algorithms commonly used today for decision-making processes.
Gradio tutorial (Build machine learning applications) Paid Members Public
You have built your optimally performing machine learning model. What next? This tutorial explores the use of Gradio in building machine learning applications. What is Gradio? Gradio is an open-source Python package that allows you to quickly create easy-to-use, customizable UI components for your ML model, any API, or even
Logistic regression in Python with Scikit-learn Paid Members Public
In linear regression, we tried to understand the relationship between one or more predictor variables and a continuous response variable. This article will explore logistic regression, where the response variable will be discrete or categorical. What is classification? Classification is a supervised machine learning problem of predicting which category or
Linear regression in Python with Scikit-learn (With examples, code, and notebook) Paid Members Public
Scikit-learn is a handy and robust library with efficient tools for machine learning. It provides a variety of supervised and unsupervised machine learning algorithms. The library is written in Python and is built on Numpy, Pandas, Matplotlib, and Scipy. In this tutorial, we will discuss linear regression with Scikit-learn. What
Seaborn tutorial Paid Members Public
Seaborn is a simple, easier-to-learn open-source data visualization Python library that provides fantastic default styles and color palettes to create attractive and informative statistical plots. Seaborn is built on top of Matplotlib. Matplotlib treats Figures and Axes as objects and focuses on how to draw them. Seaborn has a dataset-oriented,
Data visualization with Matplotlib Paid Members Public
💡"A good sketch is better than a long speech"(Napoleon Bonaparte). Organizations collect and analyze vast amounts of data from sales revenue, marketing performance, customer interactions, inventory levels, production metrics, staffing levels, costs, etc. This can be too much data that it is impossible to effectively understand and evaluate it
