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

Trabalho final da disciplina de Aprendizagem de Máquina

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dpb24/customer-churn

🌐 Predicting Customer Churn with Decision Tree, XGBoost & Neural Network Models on the Cell2Cell Dataset

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hantablack9/market-lead-scoring-propensity-modeling2

This ML project (being setup) is for building, testing and deploying models for qualifying sales leads. Current best model is XGBoost classifier which accurately predicts 82% of converting leads. Currently refactoring code for for modularity, applying SOLID principles for scalability, testing, and deployment.

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ANI717/XGBoost-MLOps-Pipeline

An end-to-end machine learning pipeline using XGBoost trained on the sklearn Breast Cancer dataset. This project demonstrates a full production workflow.

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Soumilgit/Datathon_Team-DataP1ac3X.c0m

A redeveloped hackathon bank customer churn prediction Core ML project.

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slxppin/Sketchify-A-Quick-Draw-drawing-classifier

Sketchify-A-Quick-Draw is a drawing classifier that uses machine learning to recognize hand-drawn sketches. It helps users identify and categorize their artwork quickly, making it a useful tool for artists and hobbyists alike.

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SYD-Taha/Smart-Loan-Approval-System

Smart Loan Approval System An end-to-end AI project that predicts loan approvals using XGBoost. Includes a FastAPI backend, Streamlit frontend, and SHAP-based explainability. Designed to demonstrate production-level ML deployment and transparency.

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sanadollu/AerialVehicleDetectionProject

This project addresses an image classification task using both traditional machine learning algorithms (HOG + ML) and a deep learning approach (CNN). The goal is to compare these methods in terms of accuracy, generalization, and practical performance.

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KhooodeSIN/heart_attack

Size: 454 KB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 0 - Forks: 0

ScrPzz/rongowai

Land/Water binary classifier based on Delay Doppler Map data from Rongowai

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AndersonIvanildo/docs-algorithms-apply-diabetes-dataset

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muhammadmutahir/CreditRiskModel_CRA_using_XGBoost_Neural_Network_Random_Forest_Regression_Sourav_Basu

This repository contains a credit risk analytics project that uses logistic regression, decision trees, and various data analysis techniques. Explore the code and resources in Jupyter Notebook format to understand the model's performance and insights. 🐱💻📊

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michplunkett/ucpd-incident-scraper

This repository scrapes the UCPD Daily Incident page at a pre-determined frequency and store the incidents on a generic JSON data-store.

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kushiviren25/MLCreditCard

ML Credit Card Fraud Detection aims on marking down fraudulent transactions using prominent ML models and handles class imbalance issues and provides visualization of the fraudulent transactions vs non fraud transactions and uses FastAPI to integrate with external systems .

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syariffaa/xgboost-for-mental-health

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Shakthirekak11/Political-Speech-Manipulation-Detection

🗳️ Political Speech Manipulation Detection uncovers misinformation, bias, and hostile rhetoric in political content using advanced language models and analytical pipelines. It processes speeches, tweets, and news articles to classify truthfulness, detect sentiment, and extract rhetorical and thematic patterns.

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Showmick119/Benchmarking-Signal-Processing-Packages

An evaluation of the performance and recency of various Python signal processing packages when applied to ECG and PPG data.

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Bhawesh-Agrawal/Numeric-Nomads

Anveshan Hackathon Project Submission Repo of Numeric Nomads

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chawthinn/campus-placement-prediction

This project uses the Kaggle competition dataset from the "ML with Python Course Project" to predict campus recruitment outcomes. It includes preprocessing, EDA, feature engineering, and model training using classification algorithms such as Logistic Regression, Decision Tree, and K-Nearest Neighbors.

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siddheshwarkoli/Customer-Transaction-Prediction-Classification

BASED ON GIVEN FEATURE OF DATASET WE NEED TO PREDICT WHICH CUSTOMER MAKE TRANSACTION IN THE FEATURE IRRESPTIVE OF THE AMOUNT OF MONEY TRANSACTED

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SaketJha-323/Liver_Cirrhosis_Stage_Detection_System

Liver Cirrhosis Stage Detection System Using Random Forest and XGBoost with Stacking Classifier

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MaisSerhan/Haven_FBAI

🌟 Haven seeks to be the premier online resource for supporting and empowering parents in Palestine to nurture their children's healthy and happy development from pregnancy through early childhood. 👶❤️🏡

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Demetreous/Spotify-Track-Recommender-System-and-Popularity-Regression

ML project leveraging Spotify audio data for track popularity prediction and music recommendation using XGBoost, KNN, and SHAP

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saccofrancesco/deepshot

DeepShot is a machine learning model designed to predict NBA game outcomes using advanced team statistics and rolling averages. It combines historical performance trends with contextual game data to deliver highly accurate win predictions (71%)

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HangOn6/CreditRiskModel_CRA_using_XGBoost_Neural_Network_Random_Forest_Regression_Sourav_Basu

Improving credit risk model using Machine learning techniques. We use a host of ml models and neural network to solve the issue.

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iswaryalaxmis/Ensemble

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Deshan-Senanayake/Bird-Range-Prediction

This is a lightweight web application that allows users to predict bird presence, location, and the best time to observe birds based on machine learning models trained on real birdwatching data from the Hambantota District.

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Algi21/Supervised-Learning-Classification

Project Supervised Learning - Classification untuk memprediksi customer churn atau tidak berdasarkan dataset suatu perusahaan telekomunikasi.

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Shannu3766/ML-Powered-Network-analyzer

A real-time network traffic analysis system that leverages machine learning to predict and classify network flows. The system captures network packets, extracts flow features, and uses Random Forest model to identify potential network threats and anomalies.

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Rahul-2006/Heart-Attack-Risk-Prediction

This is a research project based on Heart Attack Risk Prediction Using Machine Learning and SHAP Explainability

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hybtli/malware-detection-genetic-optimization

Genetic Algorithm Optimization for Malware Detection and Classification

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srinivasangr/ETL_ML_Project

This project is designed to predict students' math scores based on demographic and academic features. It leverages machine learning models to analyze trends and provide actionable insights. The application is containerized using Docker and hosted on AWS for scalability and accessibility.

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Hari-1903/Loan-Default-Prediction

Predictive analytics project for loan default risk using deep learning. Compares ANN with Random Forest and XGBoost on LendingClub data, achieving high accuracy in identifying potential defaulters.

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sabin74/customer_churn_prediction

Predict whether a customer will churn (leave the service) based on their usage and demographic data using machine learning.

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Naddour98/my-1st-project

My 1st data analysis project - Predicting Employee Turnover using ML models

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sebastianperezv/Titanic-disaster-project

This is the legendary Titanic ML competition where I use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.

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jibbs1703/Classic-ML-Models

This repository contains scripts for developing, training and evaluating machine learning models using several python frameworks.

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dpb24/fake-news-detector

📰 NLP: Fake News Detection using Classical Machine Learning

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Awais-Asghar/Skin-Cancer-Binary-Classifier

A machine learning project for binary classification of skin cancer as malignant or benign, utilizing models like XGBoost, LGBM Classifier, Adaboost, SVM, and Logistic Regression. Features comprehensive data preprocessing, model training, and evaluation for accurate diagnosis.

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MohamedMostafa010/ExeRay

ExeShield AI detects malicious Windows executables using ML. Analyzes entropy, imports, and metadata for rapid classification, aiding incident response. Built with Python and scikit-learn.

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KKeshav1101/mini_project

A Django Application Interface for Hate Speech Detection Mini Project

Language: Python - Size: 334 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 1 - Forks: 1

Onome-Joseph/Customer-Churn-Prediction

This project predicts whether a customer is likely to stop patronizing a business by making use of historical customer data.

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SKT1803/bank-marketing-prediction

Supervised Machine Learning Classification – Bank Term Deposit Prediction

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davutbayik/socialmedia-ads-purchase-prediction

🎯 Social media ad purchase predictor using Streamlit and FastAPI — clean UI + real-time ML predictions!

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362heavy/Liver_Cirrhosis_Stage_Detection_System

This repository contains a system for detecting the stage of liver cirrhosis using historical patient data. It employs machine learning to analyze key medical indicators and classify patients into three distinct stages. 🦠📈

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AshishKumar302/financial-data-analytics-projects

Financial Data Analytics Project at IBA Karachi

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aancyl/Early-Sepsis-Prediction

A Machine learning approach to predict whether a patient is likely to develop sepsis within the next six hours.

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helen-oy/Customer_Lifetime_Value_ML_Model

This is a trained machine learning model deployed on Google Cloud Platform (GCP) for prediction of Customer Lifetime Value and Churn. This model is available for users to predict lifetime value of customers based on data and features such as recency, frequency and monetary/revenue, interact with the application and make informed business decision.

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Romtaug/EtherScam

Détection automatisée des arnaques et comportements frauduleux dans les transactions en crypto-monnaies grâce à l’analyse des données blockchain.

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munas-git/EmailCTR-EDA-Counterfactuals

Predicting email ad click-through using interpretable ML and counterfactual simulations to uncover behavioral drivers and optimise targeting strategies.

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Arorms/TrafficClassification

2025 ISCC比赛 恶意流量分类

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ladesai123/Alzheimer-s-Disease-Prediction-App-using-ML-models

You can access the App through the link.

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Yashvj22/microsoft-malware-classification-using-hex-unigrams

Classifying malware families using unigram hex patterns and XGBoost from the Microsoft 2015 dataset

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MainakVerse/Adamas-AI

Adamas AI is your smart companion for diamond valuation and knowledge. Using advanced machine learning, we provide accurate price predictions and expert advice.

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aadhamashraf/Fraud-Detection-Interpretability-and-Explainability

Developing fraud detection systems using a variety of machine learning and deep learning models. Emphasis is placed on model explainability to ensure transparency in predictions, an essential aspect in financial applications.

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mahnoorsheikh16/Sketchify-A-Quick-Draw-drawing-classifier

Implementation of a sketch‐recognition pipeline inspired by Google’s Quick, Draw!—from raw stroke data to prediction. Includes data preprocessing and feature‐engineering scripts, three Bayesian classifiers alongside Logistic Regression, SVM, K-NN and XGBoost baselines, and an RNN model.

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mahnoorsheikh16/Credit-Card-Default-Prediction

This project focuses on predicting whether a customer will default on their credit card payment in the upcoming month. Utilizing historical transaction data and customer demographics, the project employs various machine learning algorithms to distinguish between risky and non-risky customers for better credit risk management.

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Shanmukhi1920/Text-Classification

Developed an NLP system using Gradio and Hugging Face to classify disaster tweets with both machine learning (ML) and deep learning (DL) models.

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UsmanShaikh24/Machine-Learning-Model-For-Anomaly-Detection-and-Predictive-Maintenance

This project addresses the growing need for intelligent industrial maintenance systems. By applying machine learning techniques, we aim to detect anomalies in machine behavior, predict machine failures, estimate Remaining Useful Life (RUL), and schedule maintenance tasks based on priority—enhancing reliability and minimizing downtime.

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DarkMattrMaestro/stats-tmnf-quarto

Un rapport statistique à but d'analyser la relation entre l’étiquette et le cheminement de circuits dans TMNF utilisant la classification

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santiagocanepa/Insta_Bot

AI-powered Instagram bot for precise gender targeting using XGBoost and OpenAI ADA, with 91% accuracy at just $0.001 per 1000 queries. Automates follows/unfollows from user lists or photo likes, and checks follow-backs with randomized human-like actions. Ideal for influencers and marketers aiming for targeted engagement.

Language: TypeScript - Size: 22.3 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 4 - Forks: 0

Ramtin-Karbaschi/Titanic_XGBOOSTmodel

XGBoost classification model predicting Titanic passenger survival with data preprocessing, feature engineering, and SMOTE for class balancing. Developed for the Kaggle Titanic competition.

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CelineBoutinon/credit-scoring

Source code for OpenClassrooms - Data Scientist Project 7 - Implement a Scoring Model (EDA & modelisation only)

Language: Python - Size: 27.9 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

rahul-9429/fake-midical-certificate-detector

Language: JavaScript - Size: 6.61 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

Jayadev445/crime-rate-pred-xgboost

Crime Rate Prediction Using XGBoost, comparing XGBoost based on accuracy with different models like KNN, decision trees and SVM models.

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pavankethavath/Microsoft-Classifying-Cybersecurity-Incidents-with-ML

A machine learning pipeline for classifying cybersecurity incidents as True Positive(TP), Benign Positive(BP), or False Positive(FP) using the Microsoft GUIDE dataset. Features advanced preprocessing, XGBoost optimization, SMOTE, SHAP analysis, and deployment-ready models. Tools: Python, scikit-learn, XGBoost, LightGBM, SHAP and imbalanced-learn

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NYXMatik/Hotel-Booking-Cancellation-Analysis

Data Mining project for predicting hotel booking cancellations using real-world data and ensemble models.

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OmarrAymann/Machine-learning-projects

A collection of machine learning projects covering supervised learning and unsupervised learning. Each project includes: Clean and reproducible code End-to-end pipeline: data preprocessing, modeling, evaluation, visualization Well-documented notebooks and scripts Use of popular ML libraries like Scikit-learn

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Akhil-peram/Smart-Crop-Recommendation-System

Crop Recommendation System using Blended XGBoost and SVM Machine Learning Model

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Lakshani09DL/Weather_Prediction_System

A machine learning-powered weather prediction system (rain possibility)

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reputed-artist/Liver-Disease-Prediction-using-Ensemble-Machine-Learning-Method

Master degree project for liver disease prediction. i have improved the code reaching its accuracy to 98-99% from 70%.

Language: Jupyter Notebook - Size: 354 KB - Last synced at: 11 days ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

czarekmilek/Music-Genre-Classifier Fork of thammpiotr/Music-Genre-Classifier

Machine Learning project of a music genre classifier.

Language: Python - Size: 2.65 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

razamehar/Predicting-Bank-Customer-Churn

This project aims to predict bank customer churn using a dataset derived from the Bank Customer Churn Prediction dataset available on Kaggle. The dataset for this competition has been generated from a deep learning model trained on the original dataset, with feature distributions being similar but not identical to the original data.

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ArunabhaPani/kaggle_titanic_death_prediction_ml_model

The above model has been created by using the titanic dataset present in the kaggle website and has been constructed by performing Eda and constructing various ml models inorder to reach to the best possible outcome

Language: Jupyter Notebook - Size: 210 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

Romilagarwal/heart-failure-prediction

🫀 Machine learning system for early heart disease risk prediction using XGBoost. Features interactive Streamlit dashboard, Flask API, and Docker support. Try the live demo!

Language: Jupyter Notebook - Size: 5.63 MB - Last synced at: 24 days ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 16.9 MB - Last synced at: 3 months ago - Pushed at: over 2 years ago - Stars: 9 - Forks: 1

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).

Language: Jupyter Notebook - Size: 47.5 MB - Last synced at: 3 months ago - Pushed at: over 2 years ago - Stars: 10 - Forks: 4

TheDataTenno/Churn-Prediction

A machine learning project to predict customer churn using classification models with SMOTE and hyperparameter tuning."

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amzn/confident-sinkhorn-allocation

Pseudo-labeling for tabular data

Language: Jupyter Notebook - Size: 51.7 MB - Last synced at: 3 months ago - Pushed at: about 1 year ago - Stars: 23 - Forks: 7

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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CaritoRamos/predictive-classification-model-in-r

Predicting hotel booking cancellations using Machine Learning in R, with data preprocessing and model training. Random Forest achieved 85.23% accuracy, highlighting lead time and previous cancellations as key factors.

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Snigdho8869/Natural-Language-Processing-Projects

Exploring a collection of Jupyter notebooks showcasing a variety of Natural Language Processing (NLP) projects.

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rubenahrens/multi-gas-tropomi-ship

The public GitHub repository of Ruben Ahrens' Master's Thesis, where the importance of SO2 and HCHO data in ship plume detection was studied

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gorkbravo/Credit-Risk-Analysis

Credit Risk Classification, using XGBoost primarily

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mohammadreza-mohammadi94/Alzheimer-Risk-Assessment-WebApp

This repository is a web app that predicts Alzheimer's Disease risk using patient data. Powered by an ANN, it provides an easy-to-use interface for quick assessments.

Language: Python - Size: 7.69 MB - Last synced at: about 2 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 1

ankushmallick1100/Diabetes-Prediction-of-Females-using-Maching-Learning-Techniques

This is a machine learning work that uses various machine learning algorithms to predict whether a patient is diabetic or not. Here various machine learning algorithms like SVM, RF Classifier, DT Classifier, KNN, LR , LR with CV, NB Classifier, and XGB are used. For this work, a website is made with Python Streamlit library.

Language: Jupyter Notebook - Size: 105 KB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 1 - Forks: 0

Pranjalshivhare06/Medical-Ensurance-Charge-Predictor

The Insurance Price Predictor is a machine learning project designed to predict insurance costs based on various input features. The project leverages four different algorithms, with XGBoost emerging as the most accurate and efficient model.

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andreas-pattichis/ML-for-RNA-Seq-Disease-Classification-and-Biomarker-Discovery

This project develops a machine learning model to classify individuals as healthy, having rheumatoid arthritis (RA), or systemic lupus erythematosus (SLE) using RNA-Seq gene expression data. The project also identifies significant genes as potential biomarkers, leveraging SGDClassifier and XGBoost models.

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armahdavi/AI_ML_assisted_breast_cancer_tumor_detection

Consolidating tutorial codes for breast cancer tumor detection, covering ML fundamentals like classification, feature engineering, training, evaluation, and key performance metrics.

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IzaacCoding36/Projeto-ONIA

Esse repositório será utilizado para a publicação e desenvolvimento do meu projeto para a Olimpíada Nacional de Inteligência Artificial (ONIA) de 2025.

Language: Python - Size: 1.78 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 1 - Forks: 0

mlatinov/Bank-Marketing-Analysis

Bank Marketing Campaign Analysis This project analyzes a Portuguese bank’s telemarketing campaign to predict term deposit subscriptions. The goal is to identify key factors influencing customer decisions and improve marketing efficiency. 🚀

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gehad-Ahmed30/Loan-Approval-Classification

This project focuses on analyzing and classifying loan applications using the Credit Risk dataset. It applies data analysis and machine learning techniques and deep learning to predict loan approval based on applicants' financial factors.

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otuemre/FraudDetectAI

FraudDetectAI is an advanced credit card fraud detection system built with XGBoost and Hybrid SMOTE Sampling (Oversampling + Undersampling). This project tackles highly imbalanced datasets, ensuring strong fraud detection accuracy while minimizing overfitting risks.

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madhans476/Food-hazard-detection-SEMEVAL-2025

Solution for SemEval-2025 Task 9: The Food Hazard Detection Challenge. Utilized machine learning, fine-tuned LLMs (GPT-2, LLaMA, Flan-T5-XL), and ensemble learning to classify food hazards and detect specific products and hazards from unstructured text.

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Samia35-2973/Living-Type-Classification-from-Codon-Usage

Machine learning project to classify living types based on codon usage data using Random Forest and XGBoost classifiers.

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swarnava-96/Rainfall-Prediction

A Flask web app which predicts whether it will rain tomorrow or not.

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JenishaTailor/HEART-DISEASE-PREDICTION-ML

developed a machine learning model to predict the probability of a patient having heart disease or a heart attack using patient-specific medical data. A Logistic Regression model was chosen as the baseline in binary classification tasks, ensuring a clear interpretation of risk probabilities.

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Mahendra357/Analysis-of-Amazon-Cell-Phone-Reviews-Using-NLP-Technique

The Amazon Cell Phone Review Sentiment Analysis project is a Flask-based web application that classifies Amazon cell phone reviews as positive or negative using a machine learning model powered by NLP techniques. Users can enter a review, analyze its sentiment with a single click, and view the result in real-time.

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Shailesh-Padhariya/Human-Resources

This project analyzes employee attrition using machine learning models, including Logistic Regression, Random Forest, and XGBoost. The objective is to identify key factors influencing employee turnover and provide insights to improve retention strategies

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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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