In this project, I have created a Machine Learning model using XGBClassifier to Detect Parkinsons Disease with eXtreme Gradient Boosting (XGBoost).

Detecting Parkinson’s Disease – Python Machine Learning Project

Develop a supervised model which predict whether or not participate in financial market in Python and using multivariate analysis ,determine key factors that lead to participation in financial market

Develop supervised model which predict the loan defaulter in python using XGBClassifer

Predict Health Insurance Owners' who will be interested in Vehicle Insurance

Malware Detection is a Kaggle Competition held privately which detects the probability of a machine being infected with malware or not given various features of each machine.

This is the first project to be completed in Upskill ISA Intelligent Machines. The project was done after the end of the competition. The XGBClassifier used in this model obtained 0.950844 public scores on Kaggle.

In this Python machine learning project, using the Python libraries scikit-learn, numpy, pandas, and xgboost, I have build a model using an XGBClassifier. We’ll load the data, get the features and labels, scale the features, then split the dataset, build an XGBClassifier, and then calculate the accuracy of our model.

classifying a patient has a heart disease or not

In this classification project, we will use different features like passenger class, sex, age, fare, etc to predict whether a person will survive in titanic or not.

Segmenting customers of an audiobook platform and predicting their future purchase.

In this problem i have tried to explain how XGB algorithm works in case of classification. I have also stated the accuracy score at the end for our XGBClassifier model. The confusion matrix has also been shown for the same. I have used the Kaggle Dataset - Titanic Survivors csv file.

Clustering bank loan customers using KMeans clustering and predicting their loan statuses using XGBClassifier. The prediction model is explained with SHAP values.

Detecting Parkinson Using extreme gradient boosting(XGBOOSTING) Algorithm.

Different classification algorithms to determine whether or not an individual from the Pima group will have type 2 diabetes

Different classification algorithms to predict the species of Iris flowers

Weather Prediction With Gradient Boost

Real case of classification with machine learning. Analysis of real data from telemarketing campaigns of a Portuguese bank.

Detecting Parkinson's using the XGBClassifier