A data transformation pipeline library based on Potter's Wheel.
💹📈Investigating the oils market prices in addition to the stock market prices between the start of 2001 to the end of 2023. 💰📉
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.
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.
Wrangling the WeRateDogs datasets to showcase data gathering, assessing, cleaning, and documentation skills.
This repository contains 3 projects that were carried out and submitted for my ALX Udacity Data Analyst Course
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 all the resources of the final Capstone Project which is a part of the IBM Data Science Professional Certification.
Exploratory data analysis challenge.
Wrangles an AI incident data set for easier visualization purposes
EDA Capstone Project(Almabetter)
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.
Power BI
Analyzing Capstone Project (Cyclistic)
Portfolio of reports created
Udacity Nano degree Project 4. (Wrangle and Analyze Data)
Data Wrangling of tweets information from @WeRateDogs Twitter page
This project uses GaussianNB, Random Forest, and AdaBoost Classification Models to predict the income category of individuals with US Census Data
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