A data transformation pipeline library based on Potter's Wheel.
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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. 💰📉
Data Wrangling: From Messy Data to Meaningful Insight - delivered to my Space Science students
Exploratory data analysis challenge.
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.
MasterSchool_Advanced Data Wrangling & EDA: Automotive Market Analysis
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.
A repository containing mentoring materials for a Ph.D. student in Neuroscience
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.
Created interactive dashboard to track and analyze online sales data. Used complex parameters to drill down in worksheet and customization using filters and slicers.
Analyzing Capstone Project (Cyclistic)
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.
Analyzing 1.24M U.S. congressional tweets (2008–2017) to build a lobbying targeting system — TF-IDF, OLS regression, sentiment analysis, and two custom metrics (LLS + BWS) built in Python, and SQLite.
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