1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

Pandas profiling component for Streamlit.

Data Science Feature Engineering and Selection Tutorials

A New Interactive Approach to Learning Data Analysis

Demo from Data Community Bydgoszcz i Toruń, 27.02.2019

Numpy and Pandas are one of the most important building blocks of knowledge to get started in the field of Data Science, Analytics, Machine Learning, Business Intelligence, and Business Analytics. This Tutorial Focuses to help the Beginners to learn the core Concepts of Numpy and Pandas and get started with Machine Learning and Data Science.

In this repository, we would see different available libraries for Exploratory Data Analysis

Jupyter Notebook Templates for quick prototyping of machine learning solutions

Using PyCaret to Predict Apple Stock Prices

Predicting whether or not a person deposits money after a marketing campaign. Gain insights to develop the best strategy in the next marketing campaign

A Python library for day to day data analysis and machine learning. This aims to make data building, cleaning and machine learning much much faster. A library of extension and helper modules for Python's data analysis and machine learning libraries.

Analysis on crime data using pandas

EDA (Exploratory Data Analysis) -1: Loading the Datasets, Data type conversions,Removing duplicate entries, Dropping the column, Renaming the column, Outlier Detection, Missing Values and Imputation (Numerical and Categorical), Scatter plot and Correlation analysis, Transformations, Automatic EDA Methods (Pandas Profiling and Sweetviz).

The Automated ML web app project leverages Python along with Pandas Profiling, PyCaret, and Streamlit to provide a seamless and user-friendly experience for automating machine learning workflows. It enables users to effortlessly explore, preprocess, model, and download the trained model

In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task

An app that uses pandas profiling to create a quick glance of a dataset.

The Data set is picked from Kaggle which describes the Situation of the Multidimensional Measures around the globe. In this Analysis, I have tried to used Pandas, seaborn, and Ipywidgets for the End to End Analysis of the Subject.

Pandas

pandas is a powerful Python library for data analysis and manipulation. It’s like a Swiss Army knife for handling structured data!

Creating Profile Report with Pandas Profiling