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Thrilled to share the results of my recent data analysis project on 1,000 London residential properties! This was a fantastic exercise in applying classroom knowledge to real-world data complexity.

What I Did:

Data Wrangling: Used Python (Pandas) to clean, inspect, and prepare 17 features for analysis.

Visualization & EDA: Created a dashboard (including scatter plots, box plots, and bar charts) using Matplotlib and Seaborn to tell the data story.

Key Insight: Found a massive price premium linked to condition over age. New and Renovated properties consistently outperformed older homes in average price, highlighting the market's demand for modern features like Underfloor Heating.