A deep convolutional network made of stacked feature extractors

Ensemble Learning Methods using Python

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.

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

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

Kaggle competition on Walmart data

Project work related to various hackathons

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

Insurance Company Data Analysis. Box-cox transformation, Target Transformation, One hot encoding, Random Forest, Xgboost, Stacking.

💰 Multi-step ahead Bitcoin price forecasting based on VMD and ensemble learning methods | ☀️ Solar power forecasting based on ensemble learning methods

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

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

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.

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.

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%

JOB-A-THON|MAY(2021)

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