XYZ Bank — Digital Customer Journey Analysis | FY 2024

This was an end-to-end project aimed at finding out why XYZ-bank customers were going despite the digital banking platform recorded a strong registration growth in FY 2024. The analysis revealed that 26.4% of registered customers (528 individuals) never made a single digital transaction.

The Rise of Digital Payments in Kenya

End-to-end analysis of Kenya’s digital payments ecosystem (2007–2026) using Central Bank data. Mobile Money grew 500x, POS rose 4→42%, card usage fell post-COVID. Insights guide banks and fintech on trends, opportunities, and strategy.

Hotel Booking Analytics: Revenue Drivers & Customer Behaviour

Exploratory analysis of 66,541 hotel bookings (2010–2019). Key findings: Thailand, Malaysia & Singapore lead bookings; 46–60 age group spends most; discounts above 15% erode margins; bookings grew YoY with dips in Feb, Jun & Nov.

Nashville Housing Data — SQL Data Cleaning & Analysis

Built an end‑to‑end SQL data cleaning pipeline for 56,477 Nashville housing records using MySQL and Pandas. Techniques included self‑join imputation, substring parsing, conditional normalization, and date formatting to prepare the data for analysis and reliable insights.

Adventure Works MS-Excel Dashboard

Built interactive Excel dashboards with Power Pivot, DAX, macros, and pivot tables to analyse four years of Adventure Works sales. Revealed $307M revenue, 41% margin, Q2 peak performance, and 50 Years+ customers driving 39% profit to inform sales strategy and targeting.

Splendor Hotel Group: Cancellation & Revenue Loss Analysis

Analysed Splendor Hotel Group's booking data in Tableau, uncovering $13.1M revenue loss from 44,224 cancellations. Key findings: transient guests drove 88% of cancellations, OTAs accounted for 72%, and January peaked. Insights target deposit policy and OTA dependency.

Cyclistic Bike-share Data Analysis Project

Cyclistic Bike-share is an imaginary bike company whose data is used in the Google Data Analytics Capstone Course Project. This project aimed to help the marketing team make a better marketing decision by analyzing their past data using Python.