Dataconomy

AUGUST 16, 2023

A model builder: Data scientists create models that simulate real-world processes. These models can predict future events, classify data into categories, or uncover relationships between variables, enabling better decision-making.

Data Scientist 172

Data Scientist Data Science Machine Learning Machine Learning 172

DrivenData Labs

APRIL 10, 2024

We take a gap year to participate in AI competitions and projects, and organize and attend events. At the time of selecting competitions, this was the most attractive in terms of sustainability, image segmentation being a new type of challenge for this team, and having a topic that would be easy to explain and visualize at events.

Deep Learning 130

Deep Learning Deep Learning Machine Learning Machine Learning 130

Ocean Protocol

DECEMBER 12, 2023

Abstract This research report encapsulates the findings from the Curve Finance Data Challenge , a competition that engaged 34 participants in a comprehensive analysis of the decentralized finance protocol. Part 1: Exploratory Data Analysis (EDA) MEV Over 25,000 MEV-related transactions have been executed through Curve.

Exploratory Data Analysis 52

Exploratory Data Analysis Predictive Analytics EDA Data Analysis 52

Heartbeat

JANUARY 30, 2023

In order to accomplish this, we will perform some EDA on the Disneyland dataset, and then we will view the visualization on the Comet experimentation website or platform. Another significant aspect of Comet is that it enables us to carry out exploratory data analysis. Let’s get started! You can learn more about Comet here.

EDA 52

EDA Machine Learning Machine Learning Exploratory Data Analysis 52

Ocean Protocol

FEBRUARY 1, 2024

This is a unique opportunity for data people to dive into real-world data and uncover insights that could shape the future of aviation safety, understanding, airline efficiency, and pilots driving planes. Their primary objective is to develop advanced models that accurately predict future weather conditions at KMIA (Miami Airport).

Exploratory Data Analysis 52

Exploratory Data Analysis Data Science Cross Validation Machine Learning 52

AWS Machine Learning Blog

JULY 31, 2023

We use the model preview functionality to perform an initial EDA. This provides us a baseline that we can use to perform data augmentation, generating a new baseline, and finally getting the best model with a model-centric approach using the standard build functionality.

ML 98

ML ML Data Preparation Machine Learning 98

Heartbeat

SEPTEMBER 28, 2023

In a typical MLOps project, similar scheduling is essential to handle new data and track model performance continuously. Load and Explore Data We load the Telco Customer Churn dataset and perform exploratory data analysis (EDA). Experiment Tracking in CometML (Image by the Author) 2.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines ML 52

Heartbeat

FEBRUARY 6, 2024

From the above EDA, it is clear that the room's temperature, light, and CO2 levels are good occupancy indicators. The exploratory data analysis found that the change in room temperature, CO levels, and light intensity can be used to predict the occupancy of the room in place of humidity and humidity ratio.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Analysis Data Analysis Machine Learning 52

Ocean Protocol

JANUARY 10, 2024

Some of the most serious effects of global warming include rising sea levels, more extreme weather events, changes in animal and plant life, and soil health & erosion. Here we use data science to diagnose the issues and propose better practices to treat our planet better than the last 30 years.

Data Science 52

Data Science Exploratory Data Analysis Support Vector Machines Data Analysis 52

Pickl AI

DECEMBER 16, 2024

Making Data Stationary: Many forecasting models assume stationarity. If the data is non-stationary, apply transformations like differencing or logarithmic scaling to stabilize its statistical properties. Exploratory Data Analysis (EDA): Conduct EDA to identify trends, seasonal patterns, and correlations within the dataset.

AI 52

AI AI Machine Learning Machine Learning 52

Iguazio

DECEMBER 14, 2023

There are 6 high-level steps in every MLOps project The 6 steps are: Initial data gathering (for exploration). Exploratory data analysis (EDA) and modeling. Data and model pipeline development (data preparation, training, evaluation, and so on).

ML 52

ML ML Data Science Data Preparation 52

Heartbeat

MAY 24, 2023

Create DataGrids with image data using Kangas, and load and visualize image data from hugging face Photo by Genny Dimitrakopoulou on Unsplash Visualizing data to carry out a detailed EDA, especially for image data, is critical.

Deep Learning 52

Deep Learning Deep Learning EDA ML 52

Pickl AI

JULY 20, 2023

Diagnostic Analytics Projects: Diagnostic analytics seeks to determine the reasons behind specific events or patterns observed in the data. It involves deeper analysis and investigation to identify the root causes of problems or successes. Root cause analysis is a typical diagnostic analytics task.

Analytics 40

Analytics Analytics Big Data Big Data 40

Pickl AI

SEPTEMBER 12, 2024

Data Cleaning: Raw data often contains errors, inconsistencies, and missing values. Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Visualisation: Effective communication of insights is crucial in Data Science.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Mlearning.ai

JULY 2, 2023

The Art of Forecasting in the Retail Industry Part I : Exploratory Data Analysis & Time Series Analysis In this article, I will conduct exploratory data analysis and time series analysis using a dataset consisting of product sales in different categories from a store in the US between 2015 and 2018.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Analysis Data Analysis Data Visualization 52

Heartbeat

MARCH 22, 2023

In this article, let’s dive deep into the Natural Language Toolkit (NLTK) data processing concepts for NLP data. Before building our model, we will also see how we can visualize this data with Kangas as part of exploratory data analysis (EDA).

Natural Language Processing 52

Natural Language Processing Deep Learning Deep Learning Machine Learning 52

Heartbeat

MAY 29, 2023

It is therefore important to carefully plan and execute data preparation tasks to ensure the best possible performance of the machine learning model. It is also essential to evaluate the quality of the dataset by conducting exploratory data analysis (EDA), which involves analyzing the dataset’s distribution, frequency, and diversity of text.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

DrivenData Labs

DECEMBER 7, 2023

Mid-point review and challenge Q&A event. This challenge included two optional milestones to help solvers understand the challenge goals and learn how well their solution aligned with those goals, an opportunity to get feedback via a mid-point review and a Q&A event. Overall and bonus prize structure.

Natural Language Processing 130

Natural Language Processing Clustering Data Science Data Analysis 130

Heartbeat

MARCH 13, 2023

We first get a snapshot of our data by visually inspecting it and also performing minimal Exploratory Data Analysis just to make this article easier to follow through. In a real-life scenario you can expect to do more EDA, but for the sake of simplicity we’ll do just enough to get a sense of the process.

ML 59

ML ML Exploratory Data Analysis Machine Learning 59

AWS Machine Learning Blog

SEPTEMBER 12, 2023

Email classification project diagram The workflow consists of the following components: Model experimentation – Data scientists use Amazon SageMaker Studio to carry out the first steps in the data science lifecycle: exploratory data analysis (EDA), data cleaning and preparation, and building prototype models.

Data Science 130

Data Science Data Scientist AWS ML 130