Nilearn

Nilearn is the machine learning library developed especially for the neuroimaging data processing.It has vast trained models on the neuro imaging data gathered from various MRI machines and other neuro imaging machines.It can be used to apply supervised learning on neuroimaging data as well it can be used to suggest the treatment in accordance with the input data to predict the treatment.It can also be used for Decoding and MVPA.So it is the best library for applying Machine Learning on neuroimaging data and predict proper results.

Nilearn is the library for python which is used for neuro image processing.It makes easy for us to use many advanced machine learning,pattern recognition and multivariate statistical techniques on neuroimaging data.It can easily be used on fMRI data,resting data and VB data so it is the best api for neuro images.It is being used in the health sector for predicting clinical score or treatment response with supervised learning algorithms.It can also be used for many other functionalities for neuro imaging data.It is the best library for predicting and performing supervised learning on neuro imaging data.

Nilearn makes it easy to use many advanced machine learning, pattern recognition and multivariate statistical techniques on neuroimaging data for applications such as MVPA (Mutli-Voxel Pattern Analysis), decoding, predictive modelling, functional connectivity, brain parcellations, connectomes. Nilearn can readily be used on task fMRI, resting-state, or VBM data. For a machine-learning expert, the value of nilearn can be seen as domain-specific feature engineering construction, that is, shaping neuroimaging data into a feature matrix well suited to statistical learning, or vice versa.