Minimal setup for Machine Learning & Data Science Projects

Link to download miniconda ?
https://docs.conda.io/en/latest/miniconda.html

Numerical Computing data Preprocessing tools
Numpy
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

Pandas
pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.

Scipy
SciPy is a free and open-source Python library used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.


Data Visualisation tools
Matplotlib
Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.

Seaborn
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Plotly
Plotly offers the leading low-code platform for building, scaling, and deploying data apps. Deliver faster and more impactful business outcomes on advanced data analytics.


Machine Learning tools

Scikit-Learn
scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms.

PyTorch
PyTorch is a machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing.


LIME
LIME ( Local Interpretable Model-agnostic Explanations )is a novel explanation technique that explains the prediction of any classifier in an interpretable and faithful manner by learning an interpretable model locally around the prediction.

SHAP
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model.