Stacked machine learning models demo
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Updated
Jul 12, 2023 - Jupyter Notebook
Stacked machine learning models demo
A Stacking-Based Model for Non-Invasive Detection of Coronary Heart Disease
Implementation of two major ensemble learning methodologies, Bagging and Stacking, over the tasks of classification and regression. Also, compared the results of Random Forests with multiple Boosting Techniques.
Prediction of market premiums for property damage and business interruption insurance products. Added natural hazard data and stacked 3 best models as the final model.
Machine Learning Model to predict student graduation grade
This project aims to predict the Taxi-trip duration within NYC based on several factors as predictors. Various combinations of relevant features are explored as iterations. After analysing the dataset, important and necessary features are selected. Several regression models are implemented & evaluated based on R2 & RMSE, & predictions visualised
Kaggle competition on Walmart data
For this group project, I performed cluster analysis and classification using Python to predict one of three classes for water pumps; functional, functional but needs repair, and non-functions. I used clustering to find hidden data structures to exploit for fitting individual classification techniques with better results than using the entire da…
Stacking model for sign language images
Application of learnings in the Machine Learning course , this project mainly gives first hand idea of elaborative exploratory data analysis performed on data sets and various advanced regressions models are used for predicting House Prices.
A collection of machine learning models for predicting laptop prices
Algorithms used to confirm whether a celestial body is a planet or not.
A Machine Learning Project that aims to perform loan defaults prediction to help banks mitigate the risk of lending bad loans
Visa approval process by leveraging machine learning on OFLC's extensive dataset, aiming to recommend suitable candidate profiles for certification or denial based on crucial drivers.
Εxercises for Machine Learning course in Faculty of Informatics of Aristotle's University of Thessaloniki
Comparison of ensemble learning methods on diabetes disease classification with various datasets
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