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

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

Heart Attack Analysis & Prediction model created for DataTalks.Club mlzoomcamp course

Predicting transaction fraud using classification problems such as Guardian Boosting as well as user interfaces using Streamlite, Accuracy: 98% AUC-ROC

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.

Data fetched by wafers is to be passed through the machine learning pipeline and it is to be determined whether the wafer at hand is faulty or not apparently obliterating the need and thus cost of hiring manual labour.

Predict Health Insurance Owners who will be interested in Vehicle Insurance

Weather Prediction With Gradient Boost

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

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

ReneWind operates wind farms. Unexpected turbine failures are presenting operational and financial problems. This project uses machine learning to develop a model that accurately predict component failure, which will give the firm more control over maintenance scheduling, costs and power generation.

Bank Marketing Classifcation machine learning using 6 Models each of models given another accuracy

Gain a complete and accurate understanding of the disease you’re dealing with.

Задача от Яндекс.Практикум и Samokat.tech – реализовать векторный поиск и решить усечённую задачу матчинга

📘 This repository predicts OLA driver churn using ensemble methods—Bagging (Random Forest) and Boosting (XGBoost)—with KNN imputation and SMOTE. It reveals city-wise churn trends and key performance drivers, powering smarter, data-backed retention strategies for the ride-hailing industry.

Метод опорних векторів -Support Vector Machine, SVM. Дерева рішень - RandomForestClassifier, XGBClassifier

No description provided.

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

Using supervised learning on Lending Club loan data to predict default and / or bad loans

Diyabet Tespiti Projesi 💉

No description provided.

The online payment fraud analysis project follows several step approach from data preprocessing through model evaluation, result comparison and final model selection, using transaction patterns to identify fraud indicators including account draining, suspicious transfers, and balance inconsistencies.

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

churnxgb :chart_with_downwards_trend::rocket::grinning: : Customer Churn Predictions # BQML # XGBoost Classifier

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 project predicts weather conditions using historical weather data. Trained an XGBoost classifier and evaluated its performance using accuracy on unseen test data.

No description provided.

Detecting Parkinson's using the XGBClassifier

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.

Different classification algorithms to predict the species of Iris flowers