KDnuggets

AUGUST 7, 2019

In this tutorial, you’ll use Python and Pandas to explore a dataset and create visual distributions, identify and eliminate outliers, and uncover correlations between two datasets.

Exploratory Data Analysis 286

Exploratory Data Analysis Data Analysis Data Analysis Python 286

Towards AI

AUGUST 7, 2024

My story (The Shift from Jupyter Notebooks to VS Code) Throughout early to mid-2019, when I started my data science career, Jupyter Notebooks were my constant companions. Because of its interactive features, it’s ideal for learning and teaching, prototypes, exploratory data analysis projects, and visualizations.

Data Science 116

Data Science ML ML Data Scientist 116

Towards AI

OCTOBER 31, 2023

Exploratory Data Analysis Next, we will create visualizations to uncover some of the most important information in our data. The graph also shows that the transaction data exhibits seasonality, where around December and January, the monthly transactions usually drop.

Exploratory Data Analysis 116

Exploratory Data Analysis Tableau Data Visualization Data Analysis 116

Mlearning.ai

FEBRUARY 28, 2023

In 2018–2019, while new car sales were recorded at 3.6 As a data scientist at Cars4U, I had to come up with a pricing model that can effectively predict the price of used cars and can help the business in devising profitable strategies using differential pricing. million units, around 4 million second-hand cars were bought and sold.

Python 52

Python EDA Exploratory Data Analysis Data Analysis 52

Mlearning.ai

JUNE 28, 2023

But I have to say that this data is of great quality because we already converted it from messy data into the Python dictionary format that matches our type of work. Exploratory Data Analysis This is one of the fun parts because we get to look into and analyze what’s inside the data that we have collected and cleaned.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing AI 52

DrivenData Labs

DECEMBER 7, 2023

In this challenge, solvers submitted an analysis notebook (in R or Python) and a 1-3 page executive summary that highlighted their key findings, summarized their approach, and included selected visualizations from their analyses. Solution format. Guiding questions. There was no one common methodological pattern among the top solutions.

Natural Language Processing 130

Natural Language Processing Clustering Data Science Data Analysis 130