Top Machine Learning Algorithms Detailed in Python and Preprocessing for Machine Learning
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Updated
Jul 13, 2021 - Jupyter Notebook
Top Machine Learning Algorithms Detailed in Python and Preprocessing for Machine Learning
Supervised Machine Learning Analysis Using Classification Models
Implementation of bagging-based ensemble for solar irradiance prediction. Base learners used in ensemble learning is stacked-LSTM
This is a friend recommendation systems which are used on social media platforms (e.g. Facebook, Instagram, Twitter) to suggest friends/new connections based on common interests, workplace, common friends etc. using Graph Mining techniques. Here, we are given a social graph, i.e. a graph structure where nodes are individuals on social media plat…
The aim is to find an optimal ML model (Decision Tree, Random Forest, Bagging or Boosting Classifiers with Hyper-parameter Tuning) to predict visa statuses for work visa applicants to US. This will help decrease the time spent processing applications (currently increasing at a rate of >9% annually) while formulating suitable profile of candidate…
Understand and code some basic algorithms in machine learning from scratch
Welcome to the Machine Learning Repository - Part 4! This repository focuses on unsupervised machine learning algorithms, particularly clustering techniques, and explores the fascinating world of ensemble methods, including boosting and bagging.
Application of various text classification algorithms on multiple datasets.
machine learning ensemble learning types in easy steps with examples
EasyVisa Project
The project includes building seven different machine learning classifiers (including Linear Regression, Decision Tree, Bagging, Random Forest, Gradient Boost, AdaBoost, and XGBoost) using Original, OverSampled, and Undersampled data of ReneWind case study, tuning hyperparameters of the models, performance comparisons, and pipeline development f…
Banking-Dataset-Marketing-Targets
Machine Learning algorithms from-scratch implementation. It covers most Supervised and Unsupervised algorithms. Homework assignments and Projects for graduate level Machine Learning Course taught by Dr Manfred Huber at UTA during Spring 21
UArizona DataLab Workshops
Repository of explaination and python codes with Scikit-Learn for different ML algorithms.
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 Application In The Medical Field Used for predicting the occurrence of stroke for the patients depends on the patient information
Predicting developer's salary from Stack Overflow Annual Developer Survey (https://insights.stackoverflow.com/survey)
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