Turning complexity into clarity through data and models by @Felix_Ochieng
This projects focuses on cleaning, standardizing, and preparing raw layoffs dataset for analysis
by removing duplicates, fixing inconsistencies, handling missing values,
and optimizing the dataset structure using SQL.
This project focuses on performing Exploratory Data Analysis (EDA) using SQL on a cleaned layoffs dataset. The goal is to uncover key trends and patterns in layoffs across companies, industries, countries, funding stages, and time.
This project aimed to develop a predictive model for average depression scores (Av_PHQ) among adolescents using the Shamiri dataset. The model sought to identify key factors influencing mental health, especially depressive symptoms, to inform data-driven interventions within school settings.
Nairobi, Kenya 00100