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Global Gaming Trends & Rating Insights
This project explores historical videogame sales, ratings and genre data to understand what makes a game successful across global markets. The analysis highlights regional preferences, platform differences, and key factors shaping sales performance. It also includes hypothesis testing to compare user ratings between platforms and genres in a statistically sound way.
Project tags: Data Cleaning, Data Visualization, Descriptive statistics, Exploratory Data Analysis, Feature Engineering, Matplotlib, NumPy, Outlier Detection, Pandas, SciPy, Seaborn, Statistical hypothesis testingRead more: Global Gaming Trends & Rating Insights
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Revenue Analysis of Two Competing Telco Tariffs
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.
Project tags: A/B-style testing, Data Cleaning, Descriptive statistics, Exploratory Data Analysis (EDA), Matplotlib, NumPy, Pandas, Python, Revenue Modeling, SciPy, Seaborn, Statistical hypothesis testing, User behavior analysisRead more: Revenue Analysis of Two Competing Telco Tariffs
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Real Estate Listing Anomalies & Market Insights
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…
Project tags: Anomaly Detection, Data Analysis, Data Cleaning, Fraud Prevention, Jupyter Notebook, Matplotlib, NumPy, Pandas, Python, SeabornRead more: Real Estate Listing Anomalies & Market Insights