bagging-ensemble

Top Machine Learning Algorithms Detailed in Python and Preprocessing for Machine Learning

Implementation of bagging-based ensemble for solar irradiance prediction. Base learners used in ensemble learning is stacked-LSTM

Machine Learning Algorithms for Portfolio Analysis & Trading

Machine Learning prototype that assess the performance of 1 Decision Tree (no bagging) vs multiple Decision Trees (with bagging)

ML models for HR classification problem. For more information please visit the link: https://datahack.analyticsvidhya.com/contest/wns-analytics-hackathon-2018-1/

Application of various text classification algorithms on multiple datasets.

Projects on Classification and Regression

churn prediction for telecom company

Notebooks on ensemble techniques.

Various Machine learning algorithms

Implementation of Machine Learning Projects

Ensemble model which uses supervised machine learning algorithm to predict whether or not the patients in the dataset have diabetes

Python Notebook for Ensemble Learning on Wisconsin Breast Cancer Database

PGP-AIML UT Austin - ensemble techniques

In this project, we reduced an imbalanced dataset to a balanced dataset using Under-sampling approach by applying Consensus Clustering using 'Simple Majority Voting' consensus function and further saw the increase in the accuracy of disease prediction by running multiple classifiers with bagging and boosting technique.

Optimizing LinearSVC models trained on the MNIST Handwritten Digits dataset, includes ensemble methods and bayesian optimization.

Comparing the perfomance of different ensembling and feature engineering techniques to predict the outcome of a game of chess.

Given a string statement, the aim is to identify the B-Protein entity in the statement.

This project explains why and how are the Bagged Models better than the Complete Model. Bagged Model parameters have tighter confidence interval and a lower bias.

Reducing imbalanced dataset (Undersampling) by Consensus Clustering (Simple Majority Voting function) and validating the changes using different classifier model with bagging and boosting techniques.

This repository consists of folders which include some of the courseworks I have completed in my Data Science MSc at KCL.

The objective of this project is to build a “Risk Analytics model” to understand the renewal potential and claim propensity of Existing Customers under Personal Auto Insurance Lines. The final models for claims and renewals were able to accurately identifies over 70% of claims & 85% renewals.