deepStats: a stastitical toolbox for deeptools and genomic signals

SVG time-series charting library

Exploring F1 through Python and Pandas

Visualizing oil data on country maps using OilMap

D3 violin, box and swarm plots

Homework results for job interviews

This page serve as the repository for the script file I used in my LiquidBrain Youtube Video

University course of algorithms and data structures in the MATLAB programming language.

Experimental export an Apache Jena RDF Grapg into an image

450h Data Scientist training - Collect and store large amounts of data - Build prediction models in Machine Learning and Deep Learning - Deploy your models in real conditions

A tool for extracting, processing and visualizing personal Google search data.

This repository is containing a portfolio of data analyst projects that I have completed and showcases my skills and experience

https://nevmenandr.github.io/rus-dict18-persons/

Work has been done on COVID-19 Bangladesh situation .Where Data Analysis, Data Visualization, Supervised Learning and Unsupervised Learning are used.

We run the dataset of Pima indians through different learnt Machine Learning techniques using R and then interpreting the results in terms of our research questions and purpose. From this, we were able to deduce the best algorithm as well as the most influential variables for the onset of diabetes with proper mathematical reasoning provided.

Quick script for analyzing and visualizing customer conversion rates through various stages in a game signup process using Python, SQL, and Plotly.

In this Project I wanted do study the factors to determine the natality rate in Spain, splitted in historic annual differences and monthly contrasts. In addition, my main idea was to use a regression model to know if the facts that affect the whole country are applied to each autonomous community, and if not, try to understand the outliers.

We examine the factors affecting the life expectancy in countries from the WHO through basic data analysis techniques. We do this by using machine learning algorithms and data visualization tools in R-programming.

Visualizations of diversification algorithms