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dal3006's Projects

dtw_measure icon dtw_measure

Code used to recreate the experiments in the paper 'Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine Learning', published in Frontiers Astronomy and Space Sciences.

dutch_gic icon dutch_gic

Calculation of geomagnetically induced currents in the Dutch powergrid

dynamics-in-power-systems icon dynamics-in-power-systems

The aim of this experiment is to understand the concept of transient stability of a power system when temporary faults occur in a power system.

dynpssimpy icon dynpssimpy

Repository for dynamic power system simulation in Python.

earthing icon earthing

A python library for design of earthing networks in electrical substations.

ecfs icon ecfs

A Python implementation of the Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality paper.

eda icon eda

Exploratory Data Analysis which includes visualisation

eda- icon eda-

Here I have performed Exploratory Data Analysis on various data sets , using python libraries and visualisation tools such as seaborn and matplotlib.

eda-1 icon eda-1

Exploratory Data Analysis :Its all about feature extraction and data visualisation.

eda-dashboard icon eda-dashboard

This repository will help in doing Exploratory Data Analysis of data sets with proper visualisations to help.

eda_in_python icon eda_in_python

This repository explores steps to visualise and to understand data initially.

eda_titanicsurvivors icon eda_titanicsurvivors

In this repository we have performed Exploratory Data analysis to visualise and clean the data.

edat icon edat

EDAT - statistical dataset visualisation tool

eeg_clustering icon eeg_clustering

High Performance Clustering of EEG Data for Prediction of Seizure Events

electric-fault-analysis icon electric-fault-analysis

Contains the working of decision tree and the methods of hyperparameter tuning. The data set is of electrical fault prediction.

electrical-fault-detection-and-classification icon electrical-fault-detection-and-classification

A sample power system was modeled using MATLAB Simulink and all six types of faults were introduced into the transmission line of the power system. An ML classifier using 8 types model was implemented using sklearn and an appropriate model was selected as the end model for each problem.

energymaster icon energymaster

Energy monitoring for logging, detection of faults, and preventative maintenance monitoring.

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