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

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.

Weather Prediction With Gradient Boost

Predict Health Insurance Owners who will be interested in Vehicle Insurance

Predicting transaction fraud using classification problems such as Guardian Boosting as well as user interfaces using Streamlite

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.

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

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

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

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

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

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

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.

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.