stacking-ensemble

State-of-the art Automated Machine Learning python library for Tabular Data

Time Series Ensemble Forecasting

This repository contains an example of each of the Ensemble Learning methods: Stacking, Blending, and Voting. The examples for Stacking and Blending were made from scratch, the example for Voting was using the scikit-learn utility.

Identify the type of disease present on a Cassava Leaf image

Εxercises for Machine Learning course in Faculty of Informatics of Aristotle's University of Thessaloniki

Data from a website that provides job reviews. The website wants to analyze texts and the corresponding rating that is provided by the user about startups. Based on the texts, try to verify if it corresponds to the score provided by the reviewer. the task helps the website to rank user's reviews or ratings

Ensemble Learning Methods using Python

This repository represents several projects completed in IE HST's MS in Business Analytics and Big Data's Machine Learning courses

Project work related to various hackathons

This project does the task of classifying whether a person is suffering from diabetes or not

Algorithms used to confirm whether a celestial body is a planet or not.

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.

SCOR Datathon in 2021. Predicted premiums for property damage and business interruption product. Added natural hazard data and stacked 3 best models as the final model.

A deep convolutional network made of stacked feature extractors

In this problem statement, a sequence of genetic mutations and clinical evidences, i.e. descriptive texts as recorded by domain experts are used to classify the mutations to conclusive categories, to be used for diagnosis of the patient.

Kaggle competition on Walmart data

Implementation of Ensemble Learning, Decision Tree, Random Forest, SVM, KNN, Logistic Regression, Bagging, Boosting and Stacking approach to analysis and predict the abnormal and normal behavior of Imbalanced Colon Dataset.

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 dataset. Unfortunately, k-means clustering, DBSCAN, hierarchical clustering, nor OPTICS produced well-defined clusters. The entire dataset was therefore used for fitting classification algorithms. The two classification techniques I was responsible for were k-nearest neighbors and stacked generalization ensemble. For the latter, I combined the best models each group member developed. All the models had a hard time predicting the functional but need repair class. My best model was only able to achieve an accuracy of 76%.

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.

Notebook files for classification and detection of anomalous trend in time series inverter data

Based on data such as general bio-data, payment history, and subscriptions, this stacking-ensemble model predicts whether a customer continues to use the service or not (attrition) with an accuracy of 83.14%

Full data science project workflow that placed me in the top 5% of all competitors (0.11931).

Kaggle Competition : Predicting house prices using a collection of advanced regression techniques and data visualization with plotly

JOB-A-THON|MAY(2021)

final_project of sun_ai_courses

Used different types of machine learning classifiers such as Passive Aggressive, Extra Trees, Dummy Classifier to detect the DDos attack and compared the accuracies of the classifiers to determine the best out of the three.

Logisitc Regression - A Binary Classification Method

Stacking bert model for movie sentiment analysis competition.