Update README.md

Author: krzjoa

README.md

@@ -41,11 +41,9 @@
 - [Graph Machine Learning](#graph-machine-learning)
 - [Probabilistic Graphical Models](#probabilistic-graphical-models)
 - [Probabilistic Methods](#probabilistic-methods)
-- [Data Manipulation](#data-manipulation)
-- [Data Frames](#data-frames)
-- [Pipelines](#pipelines)
-- [Data-centric AI](#data-centric-ai)
-- [Synthetic Data](#synthetic-data)
+- [Model Explanation](#model-explanation)
+- [Optimization](#optimization)
+- [Genetic Programming](#genetic-programming)
 - [Feature Engineering](#feature-engineering)
 - [General](#general)
 - [Feature Selection](#feature-selection)
@@ -55,10 +53,12 @@
 - [Map](#map)
 - [Automatic Plotting](#automatic-plotting)
 - [NLP](#nlp)
+- [Data Manipulation](#data-manipulation)
+- [Data Frames](#data-frames)
+- [Pipelines](#pipelines)
+- [Data-centric AI](#data-centric-ai)
+- [Synthetic Data](#synthetic-data)
 - [Deployment](#deployment)
-- [Model Explanation](#model-explanation)
-- [Genetic Programming](#genetic-programming)
-- [Optimization](#optimization)
 - [Statistics](#statistics)
 - [Distributed Computing](#distributed-computing)
 - [Experimentation](#experimentation)
@@ -280,64 +280,6 @@
 * [GPyTorch](https://github.com/cornellius-gp/gpytorch) - A highly efficient and modular implementation of Gaussian Processes in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [sklearn-crfsuite](https://github.com/TeamHG-Memex/sklearn-crfsuite) - A scikit-learn-inspired API for CRFsuite. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 
-## Feature Engineering
-
-### General
-* [Featuretools](https://github.com/Featuretools/featuretools) - Automated feature engineering.
-* [Feature Engine](https://github.com/feature-engine/feature_engine) - Feature engineering package with sklearn-like functionality. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [OpenFE](https://github.com/IIIS-Li-Group/OpenFE) - Automated feature generation with expert-level performance.
-* [skl-groups](https://github.com/dougalsutherland/skl-groups) - A scikit-learn addon to operate on set/"group"-based features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [Feature Forge](https://github.com/machinalis/featureforge) - A set of tools for creating and testing machine learning features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [few](https://github.com/lacava/few) - A feature engineering wrapper for sklearn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [scikit-mdr](https://github.com/EpistasisLab/scikit-mdr) - A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [tsfresh](https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [dirty_cat](https://github.com/dirty-cat/dirty_cat) - Machine learning on dirty tabular data (especially: string-based variables for classifcation and regression). <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [NitroFE](https://github.com/NITRO-AI/NitroFE) - Moving window features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [sk-transformer](https://github.com/chrislemke/sk-transformers) - A collection of various pandas & scikit-learn compatible transformers for all kinds of preprocessing and feature engineering steps <img height="20" src="img/pandas_big.png" alt="pandas compatible">
-
-
-### Feature Selection
-* [scikit-feature](https://github.com/jundongl/scikit-feature) - Feature selection repository in Python.
-* [boruta_py](https://github.com/scikit-learn-contrib/boruta_py) - Implementations of the Boruta all-relevant feature selection method. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [BoostARoota](https://github.com/chasedehan/BoostARoota) - A fast xgboost feature selection algorithm. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [scikit-rebate](https://github.com/EpistasisLab/scikit-rebate) - A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [zoofs](https://github.com/jaswinder9051998/zoofs) - A feature selection library based on evolutionary algorithms.
-
-## Visualization
-### General Purposes
-* [Matplotlib](https://github.com/matplotlib/matplotlib) - Plotting with Python.
-* [seaborn](https://github.com/mwaskom/seaborn) - Statistical data visualization using matplotlib.
-* [prettyplotlib](https://github.com/olgabot/prettyplotlib) - Painlessly create beautiful matplotlib plots.
-* [python-ternary](https://github.com/marcharper/python-ternary) - Ternary plotting library for Python with matplotlib.
-* [missingno](https://github.com/ResidentMario/missingno) - Missing data visualization module for Python.
-* [chartify](https://github.com/spotify/chartify/) - Python library that makes it easy for data scientists to create charts.
-* [physt](https://github.com/janpipek/physt) - Improved histograms.
-### Interactive plots
-* [animatplot](https://github.com/t-makaro/animatplot) - A python package for animating plots built on matplotlib.
-* [plotly](https://plot.ly/python/) - A Python library that makes interactive and publication-quality graphs.
-* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.
-* [Altair](https://altair-viz.github.io/) - Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph
-* [bqplot](https://github.com/bqplot/bqplot) - Plotting library for IPython/Jupyter notebooks
-* [pyecharts](https://github.com/pyecharts/pyecharts) - Migrated from [Echarts](https://github.com/apache/echarts), a charting and visualization library, to Python's interactive visual drawing library.<img height="20" src="img/pyecharts.png" alt="pyecharts"> <img height="20" src="img/echarts.png" alt="echarts">
-### Map
-* [folium](https://python-visualization.github.io/folium/quickstart.html#Getting-Started) - Makes it easy to visualize data on an interactive open street map
-* [geemap](https://github.com/giswqs/geemap) - Python package for interactive mapping with Google Earth Engine (GEE)
-### Automatic Plotting
-* [HoloViews](https://github.com/ioam/holoviews) - Stop plotting your data - annotate your data and let it visualize itself.
-* [AutoViz](https://github.com/AutoViML/AutoViz): Visualize data automatically with 1 line of code (ideal for machine learning)
-* [SweetViz](https://github.com/fbdesignpro/sweetviz): Visualize and compare datasets, target values and associations, with one line of code.
-
-### NLP
-* [pyLDAvis](https://github.com/bmabey/pyLDAvis): Visualize interactive topic model
-
-
-## Deployment
-* [fastapi](https://fastapi.tiangolo.com/) - Modern, fast (high-performance), a web framework for building APIs with Python
-* [streamlit](https://www.streamlit.io/) - Make it easy to deploy the machine learning model
-* [gradio](https://github.com/gradio-app/gradio) - Create UIs for your machine learning model in Python in 3 minutes.
-* [datapane](https://datapane.com/) - A collection of APIs to turn scripts and notebooks into interactive reports.
-* [binder](https://mybinder.org/) - Enable sharing and execute Jupyter Notebooks
-
 ## Model Explanation
 
 * [dalex](https://github.com/ModelOriented/DALEX) - moDel Agnostic Language for Exploration and explanation. <img height="20" src="img/sklearn_big.png" alt="sklearn"><img height="20" src="img/R_big.png" alt="R inspired/ported lib">
@@ -368,8 +310,6 @@
 * [tensorboard-pytorch](https://github.com/lanpa/tensorboard-pytorch) - Tensorboard for PyTorch (and chainer, mxnet, numpy, ...).
 * [mxboard](https://github.com/awslabs/mxboard) - Logging MXNet data for visualization in TensorBoard. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
 
-
-
 ## Genetic Programming
 * [gplearn](https://github.com/trevorstephens/gplearn) - Genetic Programming in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [DEAP](https://github.com/DEAP/deap) - Distributed Evolutionary Algorithms in Python.
@@ -402,6 +342,66 @@
 * [nlopt](https://github.com/stevengj/nlopt) - Library for nonlinear optimization (global and local, constrained or unconstrained).
 * [OR-Tools](https://developers.google.com/optimization) - An open-source software suite for optimization by Google; provides a unified programming interface to a half dozen solvers: SCIP, GLPK, GLOP, CP-SAT, CPLEX, and Gurobi.
 
+## Feature Engineering
+
+### General
+* [Featuretools](https://github.com/Featuretools/featuretools) - Automated feature engineering.
+* [Feature Engine](https://github.com/feature-engine/feature_engine) - Feature engineering package with sklearn-like functionality. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [OpenFE](https://github.com/IIIS-Li-Group/OpenFE) - Automated feature generation with expert-level performance.
+* [skl-groups](https://github.com/dougalsutherland/skl-groups) - A scikit-learn addon to operate on set/"group"-based features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [Feature Forge](https://github.com/machinalis/featureforge) - A set of tools for creating and testing machine learning features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [few](https://github.com/lacava/few) - A feature engineering wrapper for sklearn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [scikit-mdr](https://github.com/EpistasisLab/scikit-mdr) - A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [tsfresh](https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [dirty_cat](https://github.com/dirty-cat/dirty_cat) - Machine learning on dirty tabular data (especially: string-based variables for classifcation and regression). <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [NitroFE](https://github.com/NITRO-AI/NitroFE) - Moving window features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [sk-transformer](https://github.com/chrislemke/sk-transformers) - A collection of various pandas & scikit-learn compatible transformers for all kinds of preprocessing and feature engineering steps <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+
+
+### Feature Selection
+* [scikit-feature](https://github.com/jundongl/scikit-feature) - Feature selection repository in Python.
+* [boruta_py](https://github.com/scikit-learn-contrib/boruta_py) - Implementations of the Boruta all-relevant feature selection method. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [BoostARoota](https://github.com/chasedehan/BoostARoota) - A fast xgboost feature selection algorithm. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [scikit-rebate](https://github.com/EpistasisLab/scikit-rebate) - A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [zoofs](https://github.com/jaswinder9051998/zoofs) - A feature selection library based on evolutionary algorithms.
+
+## Visualization
+### General Purposes
+* [Matplotlib](https://github.com/matplotlib/matplotlib) - Plotting with Python.
+* [seaborn](https://github.com/mwaskom/seaborn) - Statistical data visualization using matplotlib.
+* [prettyplotlib](https://github.com/olgabot/prettyplotlib) - Painlessly create beautiful matplotlib plots.
+* [python-ternary](https://github.com/marcharper/python-ternary) - Ternary plotting library for Python with matplotlib.
+* [missingno](https://github.com/ResidentMario/missingno) - Missing data visualization module for Python.
+* [chartify](https://github.com/spotify/chartify/) - Python library that makes it easy for data scientists to create charts.
+* [physt](https://github.com/janpipek/physt) - Improved histograms.
+### Interactive plots
+* [animatplot](https://github.com/t-makaro/animatplot) - A python package for animating plots built on matplotlib.
+* [plotly](https://plot.ly/python/) - A Python library that makes interactive and publication-quality graphs.
+* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.
+* [Altair](https://altair-viz.github.io/) - Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph
+* [bqplot](https://github.com/bqplot/bqplot) - Plotting library for IPython/Jupyter notebooks
+* [pyecharts](https://github.com/pyecharts/pyecharts) - Migrated from [Echarts](https://github.com/apache/echarts), a charting and visualization library, to Python's interactive visual drawing library.<img height="20" src="img/pyecharts.png" alt="pyecharts"> <img height="20" src="img/echarts.png" alt="echarts">
+### Map
+* [folium](https://python-visualization.github.io/folium/quickstart.html#Getting-Started) - Makes it easy to visualize data on an interactive open street map
+* [geemap](https://github.com/giswqs/geemap) - Python package for interactive mapping with Google Earth Engine (GEE)
+### Automatic Plotting
+* [HoloViews](https://github.com/ioam/holoviews) - Stop plotting your data - annotate your data and let it visualize itself.
+* [AutoViz](https://github.com/AutoViML/AutoViz): Visualize data automatically with 1 line of code (ideal for machine learning)
+* [SweetViz](https://github.com/fbdesignpro/sweetviz): Visualize and compare datasets, target values and associations, with one line of code.
+
+### NLP
+* [pyLDAvis](https://github.com/bmabey/pyLDAvis): Visualize interactive topic model
+
+
+## Deployment
+* [fastapi](https://fastapi.tiangolo.com/) - Modern, fast (high-performance), a web framework for building APIs with Python
+* [streamlit](https://www.streamlit.io/) - Make it easy to deploy the machine learning model
+* [gradio](https://github.com/gradio-app/gradio) - Create UIs for your machine learning model in Python in 3 minutes.
+* [datapane](https://datapane.com/) - A collection of APIs to turn scripts and notebooks into interactive reports.
+* [binder](https://mybinder.org/) - Enable sharing and execute Jupyter Notebooks
+
+
+
 ## Statistics
 * [pandas_summary](https://github.com/mouradmourafiq/pandas-summary) - Extension to pandas dataframes describe function. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
 * [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects. <img height="20" src="img/pandas_big.png" alt="pandas compatible">