All (almost) tree models. reference Repo with ready to use codes and functions
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Updated
Jul 3, 2019
All (almost) tree models. reference Repo with ready to use codes and functions
Linear Regression with L2 Regularization, Online, Average, and Polynomial Kernel Perceptron for Optical Character Recognition, Decision Tree Ensemble, Random Forest, AdaBoost
Employee Churn Analysis, Feature Importance and Prediction Using Ensembling Model
Predict credit risk with machine learning techniques.
Predicting the cab booking using ML algorithms by considering the various conditions of seasons, day timing, & environment
A seminary paper intended to give a brief introduction on the topic of "Boosting, Bagging and Ensemble learning".
This repository contains solution for the 2022 Women in Data Science Kaggle competition that I participated in, which obtained a top 10% leaderboard standing.
Implementation of ensemble method requires different models, to get different models it is better to have different pretrained model as initialising weight (seed weights ). In this repository a simple code has been implemented to generate such seed weights for ensembling.
Automation of model training and ensemble creation for making predictions in a Kaggle competition submission.
This codes are from a research project of mine that I conducted under the supervision of department of CSE , BRAC University
4h task for sber DS contest
This project focuses on predicting the likelihood of a person having diabetes based on various health-related attributes. It employs a Voting Classifier, which combines the predictions of multiple machine learning models, to improve prediction accuracy.
An ensemble model created to classify images of currencies of 211-different classes. Winning entry for the https://www.kaggle.com/competitions/currency-prediction-challenge with around 88% accuracy.
Repository for Computational Intelligence Laboratory exercises in the 7th semester of the UG CSE program at Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu.
REliable PIcking by Consensus (REPIC)
Technologies and tools for big data analysis
Using Random Forest to detect Malicious attacks on the ES6 Website
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