Data Science Blog

FEBRUARY 23, 2023

And annotations would be an effective way for exploratory data analysis (EDA) , so I recommend you to immediately start annotating about 10 random samples at any rate. In this case, original data distribution have two clusters of circles and triangles and a clear border can be drawn between them. “Shut up and annotate!”

Supervised Learning 264

Supervised Learning Machine Learning Machine Learning Deep Learning 264

Towards AI

JULY 19, 2023

This is part 2, and you will learn how to do sales prediction using Time Series. Please refer to Part 1– to understand what is Sales Prediction/Forecasting, the Basic concepts of Time series modeling, and EDA I’m working on Part 3 where I will be implementing Deep Learning and Part 4 where I will be implementing a supervised ML model.

Cross Validation 98

Cross Validation Clustering EDA Data Preparation 98

Pickl AI

OCTOBER 14, 2024

Exploratory Data Analysis (EDA) Exploratory Data Analysis (EDA) is an approach to analyse datasets to uncover patterns, anomalies, or relationships. The primary purpose of EDA is to explore the data without any preconceived notions or hypotheses. Clustering: Grouping similar data points to identify segments within the data.

Data Analysis 52

Data Analysis Data Analysis EDA Data Mining 52

Pickl AI

JULY 12, 2024

Exploratory Data Analysis (EDA) EDA is a crucial preliminary step in understanding the characteristics of the dataset. EDA guides subsequent preprocessing steps and informs the selection of appropriate AI algorithms based on data insights. Feature Engineering : Creating or transforming new features to enhance model performance.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

JULY 20, 2023

Root cause analysis is a typical diagnostic analytics task. 3. Predictive Analytics Projects: Predictive analytics involves using historical data to predict future events or outcomes. It involves deeper analysis and investigation to identify the root causes of problems or successes.

Analytics 40

Analytics Analytics Big Data Big Data 40