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

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

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

Nonlinear Regression Models

12 clinical features for predicting death events.

Codes and slides of my Machine Learning lectures

Algorithms and Data Structures for Data Science and Machine Learning

Implementing Decision Trees, Bagging Trees and Random Forest

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

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

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

Machine Learning Jupyter Notebooks

Goal Using the data collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio

Classification problem using Ensemble Techniques