Python package for stacking (machine learning technique)

Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.

Dataflow Programming for Machine Learning in R

I will update this repository to learn Machine learning with python with statistics content and materials

Analyzing the Drugs Descriptions, conditions, reviews and then recommending it using Deep Learning Models, for each Health Condition of a Patient.

Building Decision Trees From Scratch In Python

A repository of resources for understanding the concepts of machine learning/deep learning. 

Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

TextSentimentClassification, using tensorflow.

Paper: Towards Open-Set Face Recognition using Hashing Functions (IJCB'17)

machine learning trading algorithms: implement and compare decision tree learner, a random tree learner, and a bootstrap aggregating learner

Sklearn implement of multiple ensemble learning methods, including bagging, adaboost, iterative bagging and multiboosting

This repository not only contains experience about parameter finetune, but also other in-practice experience such as model ensemble (boosting, bagging and stacking) in Kaggle or other competitions.

FastML Framework is a python library that allows to build effective Machine Learning solutions using luigi pipelines.

Learn and Explore

It is from Kaggle Competitions where the training dataset is very small and the testing dataset is very large and we have to avoid or reduce overfiting by looking for best possible ways to overcome the most popular problem faced in field of predictive analytics.

Analysis and Prediction of Poker Hand Strength

An example repo for how PU Bagging and TSA works.

Machine Learning exercises for my subject of Machine Learning at University of Granada (UGR).

Used ensemble methods such as boosting, voting, Bagging

Implementation of the Rotation Forest Algorithm from paper from Kuncheva and Rodríguez, 2016.

Analysed syntax and Semantics of Corpus of Text Documents Retrieved from Web Scraping of News articles from Inshorts and followed the Standard NLP Workflow of the CRISP-DM model.

The objective of this project is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.

Implementation of Google Quick Draw doodle recognition game in PyTorch and comparing other classifiers and features.

Performance analysis of Decisions Trees, Boosting & Bagging, KNN, Neural Network and Linear Regression algorithms. Over two Data Sets (meant-to-be) very different in nature and volume.

OCaml wrapper to the R randomForest package