Repository for the Brainhack School 2020 team working with fMRI and ABIDE data to train machine learning models.
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Updated
Jun 17, 2024 - Jupyter Notebook
Repository for the Brainhack School 2020 team working with fMRI and ABIDE data to train machine learning models.
This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.
This toolbox offers 7 machine learning methods for regression problems.
This toolbox offers 6 machine learning methods including KNN, SVM, LDA, DT, and etc., which are simpler and easy to implement.
In this project I have extarcted 30 time and frequancy features from EEG signals (of left hand and right hand moving) in an espicific time window. Then using PCA i have decreased the features dimension to 10. Then I have quarried different methdos of ML: KNN(1,3,5,6), SVM(Linear kernel, Gaussian kernel), LDA, Naive bayes on different time windows.
This project aims to understand and implement all the cross validation techniques used in Machine Learning.
The dataset contains information regarding residential properties which were collected by the US Census Service, the period 2006 to 2010.
1. train_test_split 2.K_fold 3.LeaveoneOut 4.Cross Validation Score 5.Logistic Regression
Model-Validation-Methods
Methodology used to classify breast cancer histopathological images as part of a datachallenge organised at Telecom Paris
Applied Regularisation techniques(Ridge+Lasso) and observed improvement in regression algorithm.It also contain two promising cross validation technique.
Learning Machine Learning Through Data
Churn prediction means detecting which customers are likely to leave a service or to cancel a subscription to a service.
The purpose of this project is to analyze some winning factors for a NBA team and predict their win rate using multiple linear regression. Different cross-validation methods were used to evaluate the model's prediction ability.
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