Analyzed real mobile usage data to compare two legacy tariff plans and determine which one brings higher monthly revenue. The project combines data cleaning, exploratory analysis, and statistical testing to identify the more profitable plan and support a data-driven marketing strategy for the operator.
In this project, an apartment-listings dataset was analysed to identify pricing outliers and anomalies that may point to fraudulent listings. Key attributes such as area, ceiling height, distance from city centre and listing date were explored using visualisations and engineered features. The findings deliver actionable insights for real-estate platforms seeking to flag irregular listings and understand pricing patterns.
