"This repository contains implementations of Boosting method, aimed at improving predictive performance by combining multiple models. by using titanic database."
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Updated
Jun 4, 2024 - Jupyter Notebook
"This repository contains implementations of Boosting method, aimed at improving predictive performance by combining multiple models. by using titanic database."
This project employs ensemble learning methods to forecast cybercrime rates, utilizing datasets with population, internet subscriptions, and crime incidents. By analyzing trends and employing metrics like R2 Score and Mean Squared Error, it aims to enhance prediction accuracy and provide insights for effective prevention strategies.
This repository contains a comprehensive guide and implementation of ensemble modeling techniques, specifically focusing on Boosting, Bagging, and Voting. Ensemble methods are powerful techniques in machine learning that combine the predictions from multiple models to improve overall performance and robustness.
ID3 Decision Tree and Bagging Implementation with python
Dataflow Programming for Machine Learning in R
Ground water age predictor
Sentiment Classification with Bagging and SVM
This project employs ensemble learning methods to forecast cybercrime rates, utilizing datasets with population, internet subscriptions, and crime incidents. By analyzing trends and employing metrics like R2 Score and Mean Squared Error, it aims to enhance prediction accuracy and provide insights for effective prevention strategies.
Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/
This notebook explores fraud detection using various machine learning techniques.
Analyze the data and come up with a predictive model to determine if a customer will leave the credit card services or not and the reason behind it
Regression, Classification, Clustering, Dimension-reduction, Anomaly detection
A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.
Scikit learn oriented utils
Introduction to tree models with Python
The folliwing ML project involves EDA analysis of Election Dataset, Data preparation for modelling, and prediction using ML models. Also Text Analysis on the inaugral corpora from nltk to analyse the most frequently used words in Presidents' Speeches.
Bank Credit Card Customer churn prediction
The purpose of this project is to try to predict the occurrence of injuries based on player's in-game statistics.
These are some exercises and implementations to use multiple nodes of GPUs
Code to reproduce figures of Debeire, K., Runge, J., Gerhardus, A., Eyring, V. (2024). Bootstrap aggregation and confidence measures to improve time series causal discovery
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