GitHub topics: ensamble-methods
joaopinto15/WinAPI_ML_Model
This repository provides a complete infrastructure for the detection of malicious behavior in Windows environments through the monitoring and classification of WinAPI call sequences using supervised machine learning techniques. The goal is to identify patterns of execution typically associated with malware based on dynamic behavioral traces.
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yuchenlin/LLM-Blender
[ACL2023] We introduce LLM-Blender, an innovative ensembling framework to attain consistently superior performance by leveraging the diverse strengths of multiple open-source LLMs. LLM-Blender cut the weaknesses through ranking and integrate the strengths through fusing generation to enhance the capability of LLMs.
Language: Python - Size: 74.6 MB - Last synced at: 28 days ago - Pushed at: 8 months ago - Stars: 944 - Forks: 83
Jxgamessi/STOCK-PRICE-PREDICTION-USING-LSTM-AND-RNN
This project builds an interactive Streamlit app for stock price forecasting. It uses an ensemble of Stacked LSTM and Simple RNN models trained on user-uploaded Excel datasets. The app visualizes Bollinger Bands, model performance, and predicts the next day's stock price, offering clear insights with real-time charts and accuracy metrics.
Language: Python - Size: 848 KB - Last synced at: 10 days ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0
jdtumlinson/decisionTrees_kmeans
This repository contains the code for the Decision Trees and k-Means Clustering assignment for CS434 Machine Learning and Data Mining at Oregon State University during Fall of 2024. This is being uploaded a few months afte the compleition of this assignment.
Language: Python - Size: 20.3 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0
Lukirby/Dota2-Team-Winner-Prediction
The dataset belongs to a competition hosted on Kaggle https://www.kaggle.com/competitions/mlcourse-dota2-win-prediction, the goal of which is to build a classifier model that predicts which of the team will win, given data extracted at one point during an ongoing match.
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Radhikareddy-chintareddy/Predictive-Churn-Modeling-Machine-Learning-for-Customer-Retention
This project employs machine learning algorithms to predict customer churn by analyzing historical customer data. It provides actionable insights to enhance customer retention. The models were fine-tuned using hyperparameter optimization and tackled data imbalance with SMOTE, achieving high F1-scores to drive targeted business strategies.
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S18-Niloy/Segmentation-of-Vegetation-Plot-from-Arial-Images
Segmentation of Vegetation Plots from Aerial Images: A Deep Learning Perspective
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Leonard2310/TrendAnalysisAgriTech
Project on Trend Analysis on Pest Occurrence Using Meteorological Data - Information Systems and Business Intelligence (MEng), supervised by Prof. F. Amato, PhD A. Moccardi and PhD M. Fonisto (2024)
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Abhinav330/Classification-problem-using-ensambling-on-titanic-dataset
a classification problem using ensemble methods on the Titanic dataset.
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altamashajaz/Student-Performance-Predictor
This project is an end-to-end machine learning solution to predict student performance using key features like study time and test scores. It includes exploratory data analysis, model training, and a Flask-based web app for real-time predictions, all built with modular programming for clean and maintainable code.
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waldekmaciejko/utils
Various scripts for machine learning
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UBC-MDS/525-group23
This repository is used for DSCI 525 - Web and Cloud Computing course project
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Matjaz12/Driver-Drowsiness-Prediction
Frame Level Driver Drowsiness Prediction
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akarshankapoor7/AdaBoost_tutorial
The AdaBoost (Adaptive Boosting) algorithm is a popular ensemble method used in machine learning to improve the performance of weak classifiers. It combines multiple weak classifiers to create a strong classifier, focusing more on the misclassified instances in each subsequent iteration.
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FaresAlbadrawi/alx-regression
Regression exercises and projects done at alx training
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veronikavinnichenko/Real-Estate-Agent
Price prediction and appartments recommendation
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Mashael2030/Diabetes-Health-Indicators-Classfication
Comparison of classifier Algorithms on Diabetes Health Indicators Dataset.
Language: Jupyter Notebook - Size: 19.6 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 4 - Forks: 5
SarangGami/TED-Talks-Views-Prediction-Supervised-learning
This project aims to build a regression model that predicts the number of views for TED Talks videos on the TED website.
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beatabb/Kernel_Based_Machine_Learning-course Fork of Maddi97/kmlmm_kdd
Project for Kernel-Based Machine Learning and Multivariate Modelling course at UPC Barcelona (FIB)
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kaledhoshme123/Predict-age-of-patient-based-on-the-X-Ray-images
create a model capable of predicting the patient's age group through chest X-rays.
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giulio-derasmo/Ship-Classification-Leonardo-Labs-Kaggle-Competition
Neural Networks ensemble via majority voting in order to classify ships given non-satellite images. All the models have been trained using PyTorch with pretrained weights.
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Marko19907/ML-assignments
Machine Learning assignments, Machine Learning (IE500618) course, fall 2022.
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Yahya-Ashraf-Mohamed/Finding-Donors-ML
Predicting potential donors using various machine learning models for Charity
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akthersr/Credit_Risk_Analysis
In this analysis we build and evaluate several machine learning algorithms by resampling models to predict credit risk.
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MOoTawaty/Finding-Donors-for-CharityML
finding_donors machine learning model
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t0re199/MADL_PROJECT
Application of Machine and Deep Learning techniques on images and texts.
Language: Python - Size: 42 KB - Last synced at: over 2 years ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0
StrawhatRA/Risky_Business
Credit Risk Analysis utilizing imbalanced classification machine learning models
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Kr1s1m/AI_Random_Forest
Random Forest library university project
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ebbeberge/stroke-prediction
We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors.
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sls-mdr/nf2-ml-default-loans Fork of jb-ds2020/2nd_Project
This is our second project at neuefische DS Bootcamp. Silas Mederer and me implied different ML models and documented the EDA and our business understanding of the Lending Club.
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