GitHub topics: catboost-classifier

AdiRosenstock/Airbnb-Classification-Problem-Kaggle

Final project for STAT 303-3 | Airbnb Superhost Classification using CatBoost (2nd place out of 124)

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jarif87/voicepersona-recognition-engine

VoicePersona Recognition Engine: A FastAPI app for gender prediction from voice features (meanfun, IQR, Q25, sd, sp.ent) using a Random Forest model. Features a modern, rounded form with a purple-teal gradient background, displaying only the predicted gender ("Male" or "Female").

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intelligent-life-paradox/ETF-Forecasting-and-Clustering

Trabalho final da disciplina de Aprendizagem de Máquina

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tatv047/Credit-Card-Fraud-Detection

A comprehensive machine learning(binary classification) project for detecting credit fraud on a highly imbalanced dataset.

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ConradKleykamp/Predicting-Introverts-from-Extroverts

Leveraging and tuning a LGBM model to predict whether an individual is an introvert or an extrovert based upon their social behavior and personality traits

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jarif87/employee-turnover-forecasting

FastAPI app predicting employee retention ("Stay" or "Left") using a CatBoost model. Features a clean UI with dropdowns for categorical inputs and numeric fields, displaying only predictions

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DIMFLIX/NewsGuard

categorizing news: fake or not

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EgorSolovei/ML-in-nuclear-physics2

Дипломная работа физического факультета СПбГУ

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jarif87/tune-popularity-app

Flask web app to predict song popularity using CatBoost. Enter five song features for instant predictions. Modern, responsive UI, no CSRF for development.

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patrueduard03/data-mining-mushroom-classification

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jarif87/neuro-voice-predictor

A FastAPI app for Parkinson’s disease prediction using a pre-trained model. Offers a sleek, animated UI for inputting five voice metrics (spread1, PPE, spread2, MDVP:Fo(Hz), MDVP:Flo(Hz)). Git-ready with venv excluded and GitHub screenshots.

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theMagusDev/client-churn-prediction

Customer Churn Prediction

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gharib-uk/Predictive-Maintenance-on-NASA-Turbofan-JetEngine

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Harsha2k3/predictive_maintenance_ML_Project

This project utilizes advanced data analysis and machine learning techniques to predict equipment failures before they occur. The goal is to detect anomalies and possible defects in equipment and processes to enable preemptive maintenance, thereby reducing downtime and costs.

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havva-nur-ezginci/ML-Applications

💡This project involves the implementation of machine learning (ML) algorithms 💻🔍on various datasets.

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edaaydinea/OP2-Prediction-of-the-Different-Progressive-Levels-of-Alzheimer-s-Disease-with-MRI-data

This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD) with MRI data.

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edaaydinea/OP1-Prediction-of-the-Different-Progressive-Levels-of-Alzheimer-s-Disease

This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD).

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alessioborgi/MLPipeline_OptimizationStudy

Exploration and optimization of a ML pipeline, delving into various techniques for enhancing different stages of ML workflows, including data preprocessing, feature engineering, model selection, and hyperparameter tuning.

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nguyenthiennhan444/Cosmic-Classifier

This notebook implements a structured machine learning pipeline to classify cosmic data using the CatBoost Classifier, known for its efficiency with categorical features and minimal preprocessing requirements.

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ohincu/insurance-cross-selling

Identify health insurance customers with interest in a vehicle insurance.

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ayushsaksena30/Cosmic-Classifier

This notebook implements a structured machine learning pipeline to classify cosmic data using the CatBoost Classifier, known for its efficiency with categorical features and minimal preprocessing requirements.

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alejimgon/Titanic-Kaggle-Competition-Model

This repository contains my current model for the Titanic Kaggle competition.

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SevilayMuni/e-commerce-fraud-detection

Detecting fraudulent transactions in e-commerce data provided by Vesta Corporation.

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tanishq-ctrl/House-price-prediction-and-visualization

This repository contains code and data for analyzing real estate trends, predicting house prices, estimating time on the market, and building an interactive dashboard for visualization. It is structured to cater to data scientists, real estate analysts, and developers looking to understand property market dynamics.

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alel2003/Spaceship-Titanic

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franmateus/Risco-de-Credito-Modelos-Ensemble

Modelos de classificação de risco de crédito usando algoritmos de Métodos Ensemble

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oktaviorezap/Customer-Churn-Prediction-using-Machine-Learning

Reduce the Number of Churn Customers and Identifying Business Impacts Based on Prediction Result by The Model

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Mugambi645/mental-health-prediction

Mental health prediction

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Jecoc907/Music-Industry-Analysis-using-Spotify-Data

This project investigates the dynamics of the global music market, analyzing trends and factors influencing popular songs.

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BeheshteSadeghi/ChurnModelling

This project aims to train the best model for predicting customer churn. In this project; first, data is studied and several diagrams are depicted for storytelling. Since we face with unbalanced data, categorical data, outliers, unscaled data and ecxess of features, related packages from python is utilized to prepare data for modelling.

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LohiyaH/AI-Based-Fraud-Detection

A comparative analysis of machine learning and deep learning algorithms for fraud detection, featuring XGBClassifier, CatBoostClassifier, and LGBMClassifier, as well as ANN, CNN, RNN, LSTM, and Autoencoders for performance benchmarking

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Farooqbasha008/Customer-Conversion-Prediction-for-a-new-age-Insurance-Company

A ML model to predict whether the clients will subscribe to insurance or not.

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mbappeenjoyer/CreditCardDefault_Prediction

Credit card defaulter prediction using data science techniques

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benitomartin/bank-churn-classification

Bank Churn Classification

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AnnaAnastasy/Mental-Health-EDA-ML

A data-driven project analyzing factors influencing mental health and building a predictive model for depression using advanced machine learning techniques.

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Arif-miad/Global-Plastic-Waste-Analysis

Global plastic waste is a pressing environmental issue, with massive production, limited recycling, and high risks to ecosystems and human health

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AnnaAnastasy/Classification-Project-Student-Grades

A machine learning project to predict students' academic performance using features like demographics, study habits, and parental involvement, achieving 74% accuracy with the CatBoost model.

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chaaalistaa/Catboost-Machine-Learning-for-Lifestyle-Prediction

Using machine learning techniques, namely supervised learning to predict the lifestyle of students by learning the features used and classifying based on the class, namely SES

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AnnaAnastasy/Dibetes-Prediction-Logistic-KNN-Cat

Predicting diabetes using machine learning techniques. Starting with Logistic Regression as a baseline, it progresses to advanced models like Gradient Boosting.

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dharmendradiwaker/Forecasting-House-Prices-Using-Machine-Learning

This project focuses on predicting house prices using machine learning techniques. The dataset consists of over 1,000,000+ rows and 12 columns containing information about various house attributes. The goal is to build predictive models to estimate house prices based on these attributes.

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Gourav052003/Predicting-Customer-Engagement-in-Financial-Products-Insights-from-Marketing-Campaigns

The purpose is to train a predictive model that can determine if a given customer will subscribe to a term deposit based on these various features. By analyzing historical data on successful and unsuccessful subscription outcomes, patterns can be identified which help predict future subscription behavior.

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sayande01/Thyroid_disease_Prediction_ML

This project develops an advanced predictive model to identify thyroid disease recurrence using machine learning algorithms. We used a detailed dataset with demographic, medical, and clinical features, and implemented Logistic Regression, Decision Tree, Random Forest, and CatBoost Classifier. Rigorous preprocessing and EDA were performed.

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ConradKleykamp/Loan-Approval-Prediction

Leveraging and tuning a LightGBM model to predict whether or not a loan will be approved

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kunalshelke90/Predict-Bank-Credit-Risk-using-South-German-Credit-Data

This is an end-to-end ML project, which aims at developing a classification model for the problem of classifying a given customer profile into either of the risk category (safe or not safe). The final classifier used for this project is CatBoost classifier. Deployed in AWS.

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alaeddinee21/RH-analystics

This project predicts employee promotions using a CatBoost classifier. It preprocesses data by filling missing values, scaling numerical features, and encoding categorical data. The model pipeline includes SMOTE for handling class imbalance, aiming to accurately identify employees likely to be promoted.

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Kunritty/Poisonous-Mushrooms-Classification

Data analysis and model training to predict whether mushrooms are poisonous or edible

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santicar1809/aprendizaje_automatico_textos

Clasification model of positive and negative reviews using NLP

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hperer02/Credit-risk-model

Discover a comprehensive approach to constructing credit risk models. We employ various machine learning algorithms like LightGBM and CatBoost, alongside ensemble techniques for robust predictions. Our pipeline emphasizes data integrity, feature relevance, and model stability, crucial elements in credit risk assessment.

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10mudassir007/Water-Potability

Water potability classifier using simple logistic regression to advanced gradient boosting algorithms

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harmanveer-2546/House-Price-Prediction-

We all have experienced a time when we have to look up for a new house to buy. But then the journey begins with a lot of frauds, negotiating deals, researching the local areas and so on. So to deal with this kind of issues Today, I prepared a MACHINE LEARNING Based model, trained on the House Price Prediction Dataset.

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harmanveer-2546/Credit-Card-Fraud-Detection

The Credit Card Fraud Detection Problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be a fraud. This model is then used to identify whether a new transaction is fraudulent or not. Our aim here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications.

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DataRish/MBTI-Personality-Predictor

This project predicts MBTI personality types from users' recent 50 posts using NLP and ML techniques.

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yhunlu/datascience-end-to-end-heart-disease-project

Classification problem in Heart-Disease Data

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VikramBansall/Credit-Risk-Model-and-Analysis

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Prashant-Tiwari26/Multimodal-Sentiment-Analysis-using-Text-and-Images

Multimodal Sentiment Analysis using Text and Image Data on twitter dataset

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aajinr/bank_marketing

bank marketing prediction for a term deposit campaign, identify non-potential customers

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JohnyCoder238/ICR_competition

ML competition submission to classify anonymous age related condition

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GishB/HTTPRequestClassification

Решение задачи поиска аномальных HTTP запросов (их классификации) к сервису.

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yoraghav/Automated_Hangman

Uses letter frequency and catboost classifier model in synchronous for guessing letters in hangman game instance. The model performance is evaluated on both seen words in the dictionary and words out of the dictionary.

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RimTouny/Network-Intrusion-Detection-Kaggle-Competition-Predictive-Modeling-and-F1-Score-Optimization

Kaggle competition on network intrusion detection. Train model, predict test set, submit as CSV (ID, Class). F1-score metric. Part of my 2023 master's program at the University of Ottawa in AI for Cyber Security.

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boldirev-as/Digital_perm23

ML-solution of the case of the District hackathon Leaders of Digital 2023. The task was to predict accidents (accidents, pipe ruptures, fires) based on the weather forecast for each of the urban districts. Gradient boosting (macro f1), cross-validation, shap values.

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hikmatullah-mohammadi/multi-class-cirrhosis

Kaggle Playground Series - Season 3, Episode 26 - Multi-Class Cirrhosis | EDA | MI-Score | Feature Engineering

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Praveen76/Employee-Attrition-Mini-Project

Address employee attrition effectively with this mini project. Discover a comprehensive solution leveraging data analytics and machine learning techniques. Uncover insights, build predictive models, and implement strategies to mitigate attrition risks, fostering a resilient and productive workforce.

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salim-benhamadi/landslide-prevention-and-innovation-challenge

Classifying if a landslide occured or not

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k-loki/Mars-spectrometry-14th-place-solution

This is my final solution to the Mars-spectrometry challenge by NASA hosted on @drivendataorg

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DanniRodrJ/milling-machine_failure-prediction

Machine Learning aplicado al mantenimiento predictivo. Se realizaron 2 modelos: 1 por medio de clasificación binaria que predice si una máquina fresadora estará en riesgo de fallar o no, y el 2 modelo a través de clasificación multiclase que predecirá el modo de falla

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john-fante/flower-detection-meta-learning

Flower Detection w/Meta Learning(ViT, CatBoost, SHAP)

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john-fante/malware-classification

Malware Classification w/CatBoost and SHAP

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himarygr/disease-prediction-ml-model-app

A model on the streamlit framework predicts disease and makes a treatment recommendation

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bh-Abhishek-b/Web-Server-Log-Analysis

Web Server Log Analysis

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se-grey/DS-_Matching

Educational project

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issacchan26/CreditRiskPrediction

Data Analysis and prediction on Kaggle dataset: Credit Risk Dataset

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john-fante/water-quality-classification-with-CatBoost

Water Quality Classification w/CatBoost

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loopiiu/DSP2_Endterm

Expresso Churn Prediction Challenge - dealing with imbalanced dataset

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HendEmad/GraduationProject_Embedded-AI-Medical-Quadcopter

This repo includes Cardiac arrest prediction part, path planning, and landing system of the quadcopter.

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Shrinidhi1/New-User-Engagement-Challenge

Given the data of the user activity of a month, can predict user activity for the upcoming month.

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Priya-cse/Zindi-New-User-Engagement

New User Engagement

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Harmanveer2546/House-Price-Prediction-using-Machine-Learning

We all have experienced a time when we have to look up for a new house to buy. But then the journey begins with a lot of frauds, negotiating deals, researching the local areas and so on. So to deal with this kind of issues Today, I prepared a MACHINE LEARNING Based model, trained on the House Price Prediction Dataset.

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