Python code for common Machine Learning Algorithms

A curated list of gradient boosting research papers with implementations.

A fast xgboost feature selection algorithm

Extension of the awesome XGBoost to linear models at the leaves

A lightweight gradient boosted decision tree package.

Tuning XGBoost hyper-parameters with Simulated Annealing

Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.

All codes, both created and optimized for best results from the SuperDataScience Course

XGBoost, LightGBM, LSTM, Linear Regression, Exploratory Data Analysis

Career Guidance System Using Machine Learning Techniques

Data Science Python Beginner Level Project

Modified XGBoost implementation from scratch with Numpy using Adam and RSMProp optimizers.

Determining the important factors that influences the customer or passenger satisfaction of an airlines using CRISP-DM methodology in Python and RapidMiner.

We have used our skill of machine learning along with our passion for cricket to predict the performance of players in the upcoming matches using ML Algorithms like random-forest and XG Boost

Machine Learning Project using Kaggle dataset

A binary classification model is developed to predict the probability of paying back a loan by an applicant. Customer previous loan journey was used to extract useful features using different strategies such as manual and automated feature engineering, and deep learning (CNN, RNN). Various machine learning algorithms such as Boosted algorithms (…

Binary Classification for detecting intrusion network attacks. In order, to emphasize how a network packet with certain features may have the potentials to become a serious threat to the network.

Introduction to XGBoost with an Implementation in an iOS Application

Machine-Learning: eXtreme Gradient-Boosting Algorithm Stress Testing

Designed web app employs the Streamlit Python library for frontend design and communicates with backend ML models to predict the probability of diseases. It's capable of predicting whether someone has Diabetes, Heart issues, Parkinson's, Liver conditions, Hepatitis, Jaundice, and more based on the provided symptoms, medical history, and results.