A full exploratory data analysis of your dataset, not just summary statistics, but a genuine investigation that surfaces the insight worth acting on. Every deliverable includes clean code, professional visualisations, and a written findings summary your whole team can read.

You have a CSV or dataset and do not know what it is telling you

You need to understand customer behaviour, sales patterns, or operational bottlenecks

You want a clean, documented Python analysis you can build on

You need EDA done properly before building a model or dashboard

Complete data quality audit (missing values, outliers, type issues)

Full exploratory analysis with professional visualisations

Key findings written in plain business language

Clean, commented Python notebook (Jupyter)

Specific recommendations based on the findings

My Olist e-commerce analysis covered 113,000+ orders across 9 relational tables. I engineered business-specific features, ran a full EDA, and found that delivery timing — not pricing — was the primary driver of customer churn. This finding was invisible until the data was properly explored.

I reduced memory footprint by 60% through dtype optimisation and built a custom RFM segmentation model that surfaced a high-value customer segment standard methods missed entirely.

You share your dataset and describe what you are trying to understand

I confirm scope and deliver within the agreed timeline

You receive a Jupyter notebook with all code plus a written summary

One round of revisions included