All.This is a modular framework for managing and standardizing data structures, enabling seamless interaction across the neurons.me ecosystem. It transforms objects like images, text, and audio into structured formats optimized for machine learning and deep learning applications.

🩺 Machine Learning diabetes prediction model using Support Vector Machine (SVM) classifier. Analyzes 8 medical features (glucose, BMI, age, etc.) from Pima Indian dataset to predict diabetes risk with 75-80% accuracy. Built with Python, scikit-learn, pandas. Includes data preprocessing, model training, and prediction system for diabetes..

Example code accompanying the sternberg concept cell data release for Kyzar et al. (2024)

A digital transformation of cyber assessment and authorization data with a relational schema

Unifying Biotic Interactions Data: Terminology, Data Analysis, Standardization, and Proposal of a Data Schema for Plant-Pollinator Interactions

Feature Engineering with Python

A Python-based data cleaning project to streamline Quickbooks invoice data for analysis, paving the way for improved insights into sales, pricing, and inventory management.

Highlighting expertise in data migration, data normalization and standardization, this project demonstrates successful data transfer from Snowflake to Databricks. It emphasizes optimized data flow and enhanced accessibility through standardization, showcasing a commitment to ethical data practices.

Hi folk, During my internship at KultureHire, I completed an end to end Data Analytics project. I created an executive and functional dashboard using pivot tables, conducted a thorough analysis, and provided actionable recommendations. I'm excited to share my work and the insights I discovered.

This project is about cleaning and preparing a global layoffs dataset for analysis, focusing on handling null values, correcting data types, and ensuring data integrity for more accurate insights.

Prepare and check data to comply with Darwin Core Standard in R

Building a modern data warehouse with SQL Server, including ETL Processes, Data Modeling and Analytics

The call center provided a messy dataset of customers. The objective was to clean, standardize, and remove duplicates to create an accurate, organized contact list. I used Pandas to load, explore, clean, and export the data, delivering a refined list ready for effective customer outreach.

Tutorial code for performing PCA (with mathematical explanation) on breast cancer features computed from digitized images of fine needle aspirate (FNA) of a breast mass. Center the data, calculate correlation matrix, compute principal components, visualize and interpret results.

☺️Hi folk, During my internship at KultureHire, I completed a real-world Data Analyst project. I created an interactive dashboard using pivot tables, conducted a thorough analysis, and provided actionable recommendations. I'm excited to share my work and the insights I discovered.

This repository contains SQL scripts and documentation for cleaning and standardizing data in the NashvilleHousing table within the sqlproject2 database. The project aims to prepare the dataset for analysis by addressing inconsistencies, filling missing values, standardizing formats, and removing duplicates.

Risk Analytics using Python

Build a modern Data Warehouse from scratch with SQL Server, including ETL processes, data modeling and analytics.

Este proyecto incluye un proceso detallado de limpieza de datos de registros judiciales para la generación de estadísticas relevantes, utilizando Excel y Power BI. También se incluye la visualización interactiva de los datos procesados.