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

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.

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

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

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.

Career Guidance System Using Machine Learning Techniques

Data Science Python Beginner Level Project

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

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.

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

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

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 (XGBoost, LightGBM, CatBoost) and Deep Neural Network are used to develop a binary classifier and their performances were compared.

Machine Learning Project using Kaggle dataset

AI Nexus 🌟 is a streamlined suite of AI-powered apps built with Streamlit. It features 👗 StyleScan for fashion classification, 🩺 GlycoTrack for diabetes prediction, 🔢 DigitSense for digit recognition, 🌸 IrisWise for iris species identification, 🎯 ObjexVision for object recognition, and 🎓 GradeCast for GPA prediction with detailed insights.

Chrome Extension Phishing Detection tool

The python notebook is on googles new collabatory tool. Its a churn model being run on 3 different algorithms to compare.

Machine learning Based Minor Project, which uses various classification Algorithms to classify the news into FAKE/REAL, on the basis of their Title and Body-Content. Data has been collected from 3 different sources and uses algorithms like Random Forest, SVM, Wordtovec and Logistic Regression. It gave 94% accuracy.

The Complete Journey Dataset: Churn Prediction

Predicting the supply chain shipment pricing based on the available factors in the dataset using the classical machine learning algorithms.

4 Boosting Algorithms You Should Know – GBM, XGBoost, LightGBM & CatBoost

This repository contains code and data for analyzing real estate trends, predicting house prices, estimating time on the market, and building an interactive dashboard for visualization. It is structured to cater to data scientists, real estate analysts, and developers looking to understand property market dynamics.

Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models and comparing to Ensembling Methods

Extreme Gradient Boost imputer for Machine Learning.

Introduction to XGBoost with an Implementation in an iOS Application

India is one of the countries with the highest air pollution country. Generally, air pollution is assessed by PM value or air quality index value. For my further analysis, I have selected PM-2.5 value to determine the air quality prediction and the India-Bangalore region. Also, the data was collected through web scraping with the help of Beautiful Soup.