@JA_Olaoye This make more sense, and it will expose one into how logistics, terminal, port data look like, this will make one to see a close real life data is and how important the end product of the analysis matters to decision making.
Good boss
@starboy_abefe Any average person going into tech will say I want to learn "data analysis", I was training a student in where I work, she told her dad, we are not doing anything, when I am busy teaching her the maths and stats in depth, she says na dashboard be koko.
The thing off me.
π¨ $2.83M lost to fraud β and the real story is in the data.
I built a full fraud transaction dashboard for a bank project, and the numbers are stark.
Here's what the data revealed:
π 8.01% fraud rate β up month-over-month, with fraud losses climbing 9.74%
The dashboard surfaces these patterns across credit tiers, channels, regions, and transaction types enabling the fraud team to move from reactive investigation to proactive targeting. What fraud metric do you find most underused in financial services dashboards? Drop it below. π
Working on the Fraud analysis dataset, I hit a stumbling block, noticed that the churn rate is 70.2% and the loyal rate is 98.6%. so I check my query to view a single customer details and I saw only him is "At risk" and "Loyal".
@Rita_tyna@JA_Olaoye@ezekiel_aleke