Applied Regularisation techniques(Ridge+Lasso) and observed improvement in regression algorithm.It also contain two promising cross validation technique.
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Updated
Mar 8, 2019 - Jupyter Notebook
Applied Regularisation techniques(Ridge+Lasso) and observed improvement in regression algorithm.It also contain two promising cross validation technique.
The dataset contains information regarding residential properties which were collected by the US Census Service, the period 2006 to 2010.
Model-Validation-Methods
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.
This project aims to understand and implement all the cross validation techniques used in Machine Learning.
1. train_test_split 2.K_fold 3.LeaveoneOut 4.Cross Validation Score 5.Logistic Regression
Learning Machine Learning Through Data
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.
Repository for the Brainhack School 2020 team working with fMRI and ABIDE data to train machine learning models.
Methodology used to classify breast cancer histopathological images as part of a datachallenge organised at Telecom Paris
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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