Data Science Dojo

FEBRUARY 14, 2024

Normalization A feature scaling technique is often applied as part of data preparation for machine learning. The goal of normalization is to change the value of numeric columns in the dataset to use a common scale, without distorting differences in the range of values or losing any information.

K-nearest Neighbors 243

K-nearest Neighbors Machine Learning Machine Learning Support Vector Machines 243

Dataconomy

MARCH 28, 2025

Decision trees: They segment data into branches based on sequential questioning. Unsupervised algorithms In contrast, unsupervised algorithms analyze data without pre-existing labels, identifying inherent structures and patterns. Random forest: Combines multiple decision trees to strengthen predictive capabilities.

Machine Learning 91

Machine Learning Machine Learning Algorithm K-nearest Neighbors 91

How to Learn Machine Learning

MARCH 25, 2025

It groups similar data points or identifies outliers without prior guidance. Type of Data Used in Each Approach Supervised learning depends on data that has been organized and labeled. This data preparation process ensures that every example in the dataset has an input and a known output.

Supervised Learning 59

Supervised Learning Machine Learning Machine Learning Algorithm 59

Pickl AI

JULY 1, 2024

Data Preparation for Demand Forecasting High-quality data is the cornerstone of effective demand forecasting. Just like building a house requires a strong foundation, building a reliable forecast requires clean and well-organized data. They are particularly effective when dealing with high-dimensional data.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

JULY 12, 2024

Data Preparation for AI Projects Data preparation is critical in any AI project, laying the foundation for accurate and reliable model outcomes. This section explores the essential steps in preparing data for AI applications, emphasising data quality’s active role in achieving successful AI models.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

OCTOBER 15, 2024

They identify patterns in existing data and use them to predict unknown events. Techniques like linear regression, time series analysis, and decision trees are examples of predictive models. Start by collecting data relevant to your problem, ensuring it’s diverse and representative.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52

phData

AUGUST 24, 2023

With a modeled estimation of the applicant’s credit risk, lenders can make more informed decisions and reduce the occurrence of bad loans, thereby protecting their bottom line. More recently, ensemble methods and deep learning models are being explored for their ability to handle high-dimensional data and capture complex patterns.

ML 52

ML ML Machine Learning Machine Learning 52