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WealthWise
An app built with HTML, Node.js, and Python that provides users with several avenues to grow their money further. Next step: add AI tool for inquiries, forecasts, and projections.
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Chapter 1 Models
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Dtat Preparation for a baseball dtatset
This R Markdown document implements a comprehensive baseball data preparation pipeline that systematically cleans and enhances a dataset of 259 team observations with 16 original variables. The workflow begins by creating missing value indicator flags to preserve information about data patterns, then applies IQR-based outlier detection across all numeric variables. After dropping the highly incomplete TEAM_BATTING_HBP variable (92.7% missing), it imputes remaining missing values using median substitution for robustness against outliers. The feature engineering section creates meaningful baseball-specific metrics including offensive power ratios, base-running efficiency, pitching effectiveness (WHIP proxy), and disciplinary measures (walk-to-strikeout ratios). The pipeline applies log transformations to highly skewed variables, creates categorical performance tiers (High/Medium/Low offensive performance, Elite/Average/Poor pitching, and error rate buckets), and concludes with correlation analysis and data quality validation. This systematic approach transforms raw baseball statistics into a modeling-ready dataset with both original variables and engineered features that capture key aspects of team performance across batting, pitching, base-running, and defensive capabilities.
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2025 January- June Netflix Engagement with the K-Wave
A brief report describing and visualising the prevalence of Korean titles in Netflix's fifth engagement report focusing on the hours viewed metric.
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