Data analyst who turns business questions into decisions.

Problem

The hotel had 83,590 customer records, and a lot of them needed work before they were useful. Some columns were hashed IDs, revenue was split across two channels, and a field called DaysSinceCreation was really measuring how long someone had been a customer.

Method

1. I dropped the ID, NameHash and DocIDHash columns. They say nothing about how a guest behaves, and keeping them would have grouped people by identity instead of behaviour.
2. I explored revenue, lead time, cancellations, no-shows and special requests before deciding which features to use.
3. I clustered the data three ways. K-Means gave me compact groups, and DBSCAN and HDBSCAN picked up the unusual stay lengths and big spenders that averages hide.
4. I used PCA and t-SNE to plot the groups in two dimensions, so I could actually see them and sanity-check them.
5. I wrote up each group as a customer profile, with a suggestion for how the hotel could look after it.

Problem

If a booking database has mistakes in it, every report built on top of it has the same mistakes. Ages go out of date, a changed passenger ID leaves other tables pointing at nothing, and one duplicated employee can double a headcount.

Method

1. For the train booking system I built three linked tables, with triggers that work out each passenger's age from their date of birth and update IDs everywhere when they change.
2. I added a five-character rule for Passenger_id and proper foreign keys, so bad rows can't get in at all.
3. I made backup tables and an EMPTY_DATA() procedure, so I could reset a test run with one call.
4. For the retail store data I ranked stores and employees, averaged sales by product, and matched up staff records using joins and DISTINCT.
5. I removed duplicate employees directly in the table instead of copying the clean rows into a second one.

Problem

A single churn number doesn't tell a manager much. They need to know which customers left, where they were and what they had in common. Without that, the report gets a nod in a meeting and is never opened again.

Method

1. I used a public churn dataset from Kaggle, so anyone can check my numbers against the source.
2. I showed churn over time rather than as one figure, so there's always something to compare against.
3. I broke the rate down by demographics, subscription plan and geography to see where it was highest.
4. I kept every chart cross-filterable and exportable, so people can dig into it themselves.

Problem

Most image classifiers only say whether something is there or not. With dementia that isn't very helpful, because catching it early matters most, and a plain yes or no can't show what stage someone is at.

Method

1. I trained the model to tell four stages apart: non-demented, very mild, mild and moderate.
2. I built a convolutional neural network and trained it on the Well-Documented Alzheimer's Dataset from Kaggle.
3. I published the model under an MIT licence and put it online, so anyone can try it on a real scan.