Mastering Linear Regression in Python: A Step-by-Step Tutorial | Scipy|polyfit|lmfit|scikit|Astropy

6 DIFFERENT METHODS ARE FOLLOWINGS
1. SCIPY CURV FIT
2. NUMPY POLY FIT
3. SCIPY STATS
4. ASTROPY MODELLING
5. IM FIT
6. SCIKIT LEARN

Chapters TimeStamps:
00:00:00 Introduction To Linear Regression
00:01:05 Method-1 Scipy Curve Fit Linear Regression
00:20:22 Method-2 Numpy poly fit Linear Regression
00:30:00 Scipy Stats Linear Regression
00:36:41 Astropy Linear Regression
00:44:05 Lm Fit Linear Regression
00:50:54 SciKit Learn Linear Regression

1. SciPy Curve Fitting:
SciPy: A library used for scientific and technical computing in Python.
Usage: It can be employed for nonlinear regression and fitting user-defined functions to data.
2. NumPy Polyfit:
NumPy: Fundamental package for numerical computing in Python.
Usage: Useful for fitting a polynomial function to the data.
3. SciPy Stats:
SciPy: Includes statistical functions and tools.
Usage: Helpful for basic linear regression analysis and obtaining statistical information about the relationship between variables.
4. Astropy Modelling:
Astropy: A library for astronomy-related computations and analysis.
Usage: Useful for astronomical regression tasks, such as fitting models to astronomical spectra or light curves.
5. IMFIT:
IMFIT: An astronomical software package used for fitting and modeling astronomical images.
Usage: Specifically designed for modeling celestial objects and phenomena from image data, applying various fitting algorithms and models.
6. Scikit-Learn:
Scikit-Learn (sklearn): A popular machine learning library for Python.