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

Towards AI

JULY 19, 2023

Check out the previous post to get a primer on the terms used) Outline Dealing with Class Imbalance Choosing a Machine Learning model Measures of Performance Data Preparation Stratified k-fold Cross-Validation Model Building Consolidating Results 1. Data Preparation Photo by Bonnie Kittle […]

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

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

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

phData

AUGUST 24, 2023

Various machine learning algorithms can be used for credit scoring and decisioning, including logistic regression, decision trees, random forests, support vector machines, and neural networks. Data Preparation The first step in the process is data collection and preparation.

ML 52

ML ML Machine Learning Machine Learning 52

PyImageSearch

SEPTEMBER 16, 2024

Supervised Learning These methods require labeled data to train the model. The model learns to distinguish between normal and abnormal data points. For example, in fraud detection, SVM (support vector machine) can classify transactions as fraudulent or non-fraudulent based on historically labeled data.

Clustering 56

Clustering Algorithm Machine Learning Machine Learning 56

Pickl AI

OCTOBER 15, 2024

Start by collecting data relevant to your problem, ensuring it’s diverse and representative. After collecting the data, focus on data cleaning, which includes handling missing values, correcting errors, and ensuring consistency. Data preparation also involves feature engineering.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52