Ensemble based approach compared to traditional machine learning models
-
Updated
Jul 16, 2024 - Jupyter Notebook
Ensemble based approach compared to traditional machine learning models
Explored various resampling techniques to learn from an imbalanced dataset for detecting Credit card frauds.
Time Series Ensemble Forecasting
A package that makes it trivial to create and evaluate machine learning pipeline architectures.
🏆1st solution in web ad CTR predict competition🏆
Fast and Accurate ML in 3 Lines of Code
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
iNeuron.ai internship project: Mushroom Classification using Ensemble Machine Learning Models and Streamlit Deployment
A Julia machine learning framework
This project aims to predict the performance of students based on various demographic and educational factors. The goal is to use regression analysis to predict student scores in mathematics.
MABEL: Malware Analysis Benchmark for Artificial Intelligence and Machine Learning
Machine Learning results in R
A collection of 8 Applied Data Science projects.
Credit Card use prediction using RandomForest Regressor.
An introduction to fundamental Machine Learning topics
Few-shot satellite image classification for bringing deep learning on board OPS-SAT
Build a Web App called AI-Powered Heart Disease Risk Assessment App
A fraud detection model built using random forests and rule based classifier
Add a description, image, and links to the ensemble-learning topic page so that developers can more easily learn about it.
To associate your repository with the ensemble-learning topic, visit your repo's landing page and select "manage topics."