Data Science Dojo

JULY 5, 2023

Machine Learning (ML) is a powerful tool that can be used to solve a wide variety of problems. Getting your ML model ready for action: This stage involves building and training a machine learning model using efficient machine learning algorithms. Cleaning data: Once the data has been gathered, it needs to be cleaned.

Machine Learning 195

Machine Learning Machine Learning EDA ML 195

Data Science Dojo

APRIL 29, 2025

Correcting these issues ensures your analysis is based on clean, reliable data. Exploratory Data Analysis (EDA) With clean data in hand, the next step is Exploratory Data Analysis (EDA). Do not be afraid to dive deep and explore other techniques.

Power BI 195

Power BI Analytics Analytics EDA 195

Pickl AI

DECEMBER 3, 2024

Raw data often contains inconsistencies, missing values, and irrelevant features that can adversely affect the performance of Machine Learning models. Proper preprocessing helps in: Improving Model Accuracy: Clean data leads to better predictions. Loading the dataset allows you to begin exploring and manipulating the data.

Python 52

Python ML ML Exploratory Data Analysis 52

Mlearning.ai

APRIL 25, 2023

In this blog, we’ll be using Python to perform exploratory data analysis (EDA) on a Netflix dataset that we’ve found on Kaggle. We’ll be using various Python libraries, including Pandas, Matplotlib, Seaborn, and Plotly, to visualize and analyze the data. The type column tells us if it is a TV show or a movie. df.isnull().sum()

Data Analysis 52

Data Analysis Data Analysis Python Exploratory Data Analysis 52

Heartbeat

MAY 29, 2023

This step involves several tasks, including data cleaning, feature selection, feature engineering, and data normalization. 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

Heartbeat

MARCH 13, 2023

In this article, I intend to show how someone can keep track of changes with Comet ML’s dataset storage feature: Artifacts. They are: A Comet ML account. 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.

ML 59

ML ML Exploratory Data Analysis Machine Learning 59