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pandas-profiling
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1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
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.
Jupyter Notebook Templates for quick prototyping of machine learning solutions
In this repository, we would see different available libraries for Exploratory Data Analysis
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
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.
VisuVerse is an innovative and user-friendly Data Analysis and Data Visualization WebApp developed using Streamlit.
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
An app that uses pandas profiling to create a quick glance of a dataset.
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
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).
This is an interactive web application built using Streamlit and Pandas Profiling that allows users to perform data analysis on large CSV files with just one click.
