IBM Data Analyst Capstone Project - Analyzing survey dataset

In this notebook, I have done Data Cleaning, Data Wrangling, EDA and Feature Engineering. After that I trained the dataset using Machine Learning Algorithm Random Forest Regressor.

A terminal spreadsheet multitool for discovering and arranging data

A set of handy functions for digital data analysis purposes

I aquired a full scholarship from Google Launchpad. Advanced data wrangling skills to work with messy, complex real-world datasets. Highly customized visualizations using the Matplotlib Python library

Winning the Space race with Data Science

Data analysis projects include data cleaning, data wrangling, exploratory data analysis, data visualization, and statistical modeling using various tools and programming languages such as Python, R, SQL, and Excel.

This include all my data analysis study works and assignments.

Designed a dashboard to track employee data for the HR team including attendence, working hours and leaves. This dashboard can streamline the HR processes and also can save the HR team 3-4 hours of work daily.

Power BI Project

Aquí compartiré y documentaré mi aprendizaje en el análisis predictivo utilizando ML y prácticas de Python. Variando desde simples ejercicios hasta proyectos prácticos. Cada proyecto incluye archivos de soporte y notebooks con código y análisis y las prácticas sus enunciados comentado al inicio.

The objective of this study is to map all the relevant features for the properties along with the information related to the geography around it, to estimate the value of a particular property/house.

The aim of this project is to improve of taking personal loans by using EDA and statistical measures

IBM Data Science Capstone Project from Coursera

data wrangling examples

Diabetic Prediction

Data Wrangling and Visualizing Stock Data from Alpha Vantage API - Apple Stocks

Data Wrangling com Python para e-Commerce

Fundamentals of Quantitative Analysis

The primary objective was to produce a detailed report to objectively understand the behaviour of corporations by using mainly the previous nine editions of Forbes 2000. This involved merging datasets, analyzing discrepancies, and providing insights through data visualization techniques.