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Badar-Islam / Fitness_improvement

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Analyzed customer characteristics using user counts, probabilities, and conditional probabilities to profile target audiences for AeroFit's treadmill portfolio. Delivered actionable insights to recommend the most suitable treadmill model—entry-level, mid-level, or advanced—to new customers, enhancing product alignment with user needs.

visualization python exploratory-data-analysis eda pandas bivariate-analysis conditional-probability-estimation univariate-analysis customer-profiling aerofit
  • Updated Nov 20, 2025
  • Jupyter Notebook

Biplabini-1992 / Fitness-Customer-Data-Analysis-using-Descriptive-Statistics-Probability

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This project analyzes AeroFit’s treadmill customer dataset to uncover patterns in demographics, fitness behaviour and income & to profile customers for each treadmill model (KP281, KP481, KP781). The analysis includes descriptive statistics, probability calculations and visualizations to provide insights for targeted marketing & product suggestions

python probability pandas data-visualization seaborn data-analysis matplotlib customer-profiling aerofit
  • Updated Dec 18, 2025
  • Jupyter Notebook

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