Towards AI

NOVEMBER 4, 2024

The points to cover in this article are as follows: Generating synthetic data to illustrate ML modelling for election outcomes. Model Fitting and Training: Various ML models trained on sub-patterns in data. Important Steps of EDA: Distribution analysis: Plot the distribution of continuous variables such as age and income.

Machine Learning 119

Machine Learning Machine Learning Exploratory Data Analysis EDA 119

Flipboard

JUNE 16, 2025

The world’s leading publication for data science, AI, and ML professionals. Getting Started: You Don’t Need Expensive Hardware Let me get this clear, you don’t necessarily need an expensive cloud computing setup to win ML competitions (unless the dataset is too big to fit locally).

Machine Learning 122

Machine Learning Machine Learning Data Science Artificial Intelligence 122

Data Science Dojo

MARCH 27, 2025

Another interesting read: Master EDA Importance of Data Normalization So, we defined data normalization, and hopefully, youve got the idea. Order_ID Customer_ID Product Price Order Date 101 1 Laptop $800 01-03-2024 102 1 Mouse $20 01-03-2024 Now, if John updates his email, it only needs to be changed once in the Customers Table.

Database 195

Database Data Warehouse Machine Learning Machine Learning 195

Data Science Dojo

APRIL 29, 2025

Exploratory Data Analysis (EDA) With clean data in hand, the next step is Exploratory Data Analysis (EDA). Techniques for EDA Descriptive Statistics: Start by calculating average shot distance, conversion rates, and shot success inside vs. outside the penalty area. Do not be afraid to dive deep and explore other techniques.

Power BI 195

Power BI Analytics Analytics EDA 195

Towards AI

JUNE 25, 2024

Last Updated on June 25, 2024 by Editorial Team Author(s): Mena Wang, PhD Originally published on Towards AI. This is the first one, where we look at some functions for data quality checks, which are the initial steps I take in EDA. Let’s get started. 🤠 🔗 All code and config are available on GitHub.

ML 80

ML ML EDA Data Wrangling 80

Towards AI

MARCH 22, 2024

Last Updated on March 25, 2024 by Editorial Team Author(s): Cornellius Yudha Wijaya Originally published on Towards AI. Learn how to develop an ML project from development to production. Spam Classifier Development – EDA and Model Development – Model Development and Experiment Tracking with MLFlow3.

Machine Learning 116

Machine Learning Machine Learning EDA Data Science 116

AWS Machine Learning Blog

JUNE 5, 2025

In this post, we share how Radial optimized the cost and performance of their fraud detection machine learning (ML) applications by modernizing their ML workflow using Amazon SageMaker. Businesses need for fraud detection models ML has proven to be an effective approach in fraud detection compared to traditional approaches.

Machine Learning 67

Machine Learning Machine Learning AWS ML 67