wrangling-data

A data transformation pipeline library based on Potter's Wheel.

This repository contains 3 projects that were carried out and submitted for my ALX Udacity Data Analyst Course

The project revolves around predicting the successful landing of the Falcon 9 first stage during SpaceX rocket launches. By leveraging the concepts and techniques learned in the specialization, we aim to develop a predictive model that can determine the likelihood of a successful landing.

💹📈Investigating the oils market prices in addition to the stock market prices between the start of 2001 to the end of 2023. 💰📉

MasterSchool_Advanced Data Wrangling & EDA: Automotive Market Analysis

Data Wrangling: From Messy Data to Meaningful Insight - delivered to my Space Science students

Exploratory data analysis challenge.

In this repository, you’ll learn in-demand skills used by data scientists, like Data Gathering, Analysis & Visualization, Statistical Analysis, Predictive Modeling, Machine Learning, and Data Mining. You’ll also work with the latest languages, tools, and libraries, including Python, SQL, Pandas, Numpy, ScikitLearn, Seaborn and Matplotlib.

In this project, I analyzed the prosper load data, studied the trends and concluded that monthly income, loan amount and borrower's rate significantly affect the prosper rating and a good predictors of delinquency.

Pipeline for curating intracranial EEG data (2022-2025)

Wrangling the WeRateDogs datasets to showcase data gathering, assessing, cleaning, and documentation skills.

This project analyzes customer purchasing behavior using descriptive statistics. It includes data preprocessing, exploratory data analysis, and statistical analysis to uncover patterns and trends. The goal is to optimize marketing strategies and improve offer acceptance rates.

This repository contains 3 projects that were carried out and submitted for my ALX Udacity Data Analyst Course

A repository containing mentoring materials for a Ph.D. student in Neuroscience

Machine Learning with Python: Halliburton Landmark Learning

EDA Capstone Project(Almabetter)

This repository contains all the resources of the final Capstone Project which is a part of the IBM Data Science Professional Certification.

Part of the Data Science course project dedicated to Digital Ethics concepts mapping

Power BI

Predicting hotel booking cancellations using Machine Learning in R, with data preprocessing and model training. Random Forest achieved 85.23% accuracy, highlighting lead time and previous cancellations as key factors.