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).

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.

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

Weather Prediction With Gradient Boost

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

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

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

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

Predict Health Insurance Owners who will be interested in Vehicle Insurance

Health-insurance-cross-sell-prediction

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

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

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.

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.

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

Diyabet Tespiti Projesi 💉