TECHNO-TRAVEL APP

This is a project that we did with my team mates. It was inspired by international visitors who come to Kenya but have trouble navigating the local transport there. We tried to solve the problem by adding all transport modes available and possible routes to a user's chosen destination.

Access to Drinking Water Part 1

The project focuses on analyzing global data related to water access. The aim was to investigate various factors affecting water accessibility, including population size, area type, and income groups. This project involved extensive data processing and visualization using Google Sheets.

Maji Ndogo PT.1: Beginning Our Data-Driven Journey in Maji Ndogo

The project involves analyzing a vast database of 60,000 records collected through extensive surveys. Through SQL I was able to explore foundational data tables, examines different water sources, uncovers visit patterns, and correct pollution data using advanced data operations.

Maji Ndogo PT.2: Clustering data to unveil Maji Ndogo's water crisis

This project continues with an in-depth examination of water accessibility and infrastructure issues following the initial data collection phase. This phase delves into the specifics of water source usage, queue times, and the condition of existing infrastructure.

Maji Ndogo PT.3: Weaving the data threads of Maji Ndogo's narrative

The Maji Ndogo project focuses on auditing and analyzing data for water sources in a country, as recorded in the water quality database. The goal is to compare the quality scores from the database to scores given by an auditor to identify discrepancies and potential patterns.

Maji Ndogo PT.4: Charting the course for Maji Ndogo's water future

The Maji Ndogo project involves analyzing data to improve water accessibility and quality in various regions. The project includes identifying corruption among field workers, linking data across multiple tables, and generating insights for decision-makers.

Access to Drinking Water Part 2

Involved analyzing WHO/UNICEF JMP estimates from 2000 to 2020. The project aimed to understand water access trends, calculate annual rates of change (ARC), and visualize data by region. Key steps included importing datasets, calculating ARCs for national, rural, and urban areas.

Python Data Structures Code Challenge

This code challenge involves manipulating data structures like lists and dictionaries to manage agricultural data. Tasks include adding vehicles with specific attributes and verifying the correctness of functions.