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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.
Weather Prediction With Gradient Boost
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.
Heart Attack Analysis & Prediction model created for DataTalks.Club mlzoomcamp course
Predict Health Insurance Owners who will be interested in Vehicle Insurance
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.
Segmenting customers of an audiobook platform and predicting their future purchase.
Using supervised learning on Lending Club loan data to predict default and / or bad loans
Clustering bank loan customers using KMeans clustering and predicting their loan statuses using XGBClassifier. The prediction model is explained with SHAP values.
Different classification algorithms to predict the species of Iris flowers
classifying a patient has a heart disease or not
Analyzing tweets from Twitter and classifying them into trolls using Natural Language Processing
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
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.
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.
Detecting Parkinson Using extreme gradient boosting(XGBOOSTING) Algorithm.
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.
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.
Detecting Parkinson's using the XGBClassifier
Different classification algorithms to determine whether or not an individual from the Pima group will have type 2 diabetes
Метод опорних векторів -Support Vector Machine, SVM. Дерева рішень - RandomForestClassifier, XGBClassifier
Spam Email Detection using Machine Learning Classifier Algorithms
Health-insurance-cross-sell-prediction
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.
Detecting Parkinson’s Disease – Python Machine Learning Project