Tag: One-Hot Encoding
Running ML on raw, unprocessed data
Running machine learning (ML) on raw, unprocessed data is a critical yet intricate process that forms the backbone of any successful ML project. This comprehensive guide delves into each step….
Ignoring community modules’ security risks
Ignoring Community Modules’ Security Risks: Understanding the Importance of Secure IaC Practices Introduction Infrastructure as Code (IaC) is one of the cornerstones of modern DevOps practices, enabling teams to automate….
Running conflicting IaC deployments
Running Conflicting IaC Deployments: Understanding the Challenges and Best Practices Introduction Infrastructure as Code (IaC) has become the foundation for modern DevOps practices, allowing teams to define, provision, and manage….
Random Forests
Random Forests in Machine Learning 1. Introduction to Random Forests Random Forest is a Supervised Machine Learning algorithm that is used for both Classification and Regression tasks. It is an….
Decision Trees
Decision Trees in Machine Learning 1. Introduction to Decision Trees A Decision Tree is a Supervised Learning algorithm used for both classification and regression problems. It mimics human decision-making by….
Logistic Regression
Logistic Regression in Machine Learning 1. Introduction to Logistic Regression Logistic Regression is a Supervised Learning algorithm used for classification problems. Unlike Linear Regression, which predicts continuous values, Logistic Regression….
Handling Categorical Data
Handling Categorical Data in Machine Learning Using Pandas Introduction Categorical data represents discrete values that belong to a limited set of categories or labels. It is common in real-world datasets,….
Data Encoding Techniques (One-Hot Encoding, Label Encoding)
Data Encoding Techniques: One-Hot Encoding & Label Encoding Introduction to Data Encoding Data encoding is a crucial preprocessing step in machine learning, where categorical data is converted into a numerical….
Feature Engineering
Feature Engineering: A Comprehensive Guide Introduction Feature engineering is the process of transforming raw data into meaningful features that improve the performance of machine learning models. It involves selecting, creating,….