Machine learning library for classification tasks

Machine learning library for classification tasks

Machine learning library for classification tasks

Evaluating multiple classifiers after SVM-RFE (Support Vector Machine-Recursive Feature Elimination)

Thesis for my Diploma in ML and AI at UoH

Plain Python Implementation of popular machine learning algorithms from scratch. Algorithms includes: Linear Regression, Logistic Regression, Softmax, Kmeans, Decision Tree,Bagging, Random Forest, etc.

Breast Cancer Detection with Decision trees Algorithm And Bagging Normalizing

Machine Learning Framework for Estimating Efficiency of Organic Solar Cells using Extreme Random Forests

Use Random Forest to prepare a model on fraud data treating those who have taxable income <= 30000 as "Risky" and others are "Good"

Nonlinear Regression Models

12 clinical features for predicting death events.

Implementing Decision Trees, Bagging Trees and Random Forest

Codes and slides of my Machine Learning lectures

Algorithms and Data Structures for Data Science and Machine Learning

In this project I implemented decision tree, bagged tree, random forest and XGBoost for comparison of better MAE performance between Trees Algorithms.

Bagging is the term from "Bootstrap Aggregation Algorithm", That is for Low Bias & Low Variance

Machine Learning Jupyter Notebooks

Analyse the factors which lead to online shopping on a website and building predictive models for it.

Building classification models to predict if a loan application is approved. Using under-sampling, bagging and boosting to tackle the problem of with unbalanced dataset

Classification problem using Ensemble Techniques