Enhanced Data Visualization and Regression Techniques

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Enhanced Data Visualization and Regression Techniques

  • Day: 2024-04-21
  • Time: 04:30 to 05:20
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Data Visualization, Regression, Python, Health, Quadratic Models

Description

Session Goal:

The session aimed to improve [[data visualization]] techniques and explore regression methods for predicting body measurements and weight.

Key Activities:

  • Color Customization for Line Charts: Modified plotting functions to use distinct colors for different years, enhancing visibility and accessibility.
  • Boxplot Improvements: Improved legibility by grouping weeks into months, rotating x-axis labels, and adjusting label frequency.
  • Unique Month-Year Boxplot Labels: Created boxplots with unique month-year labels by modifying DataFrames and plotting functions.
  • Regression Techniques for Weight Prediction: Explored multiple regression techniques, including linear, polynomial, and generalized additive models.
  • Quadratic Curve Fitting: Implemented a Python function to fit quadratic curves using numpy’s polyfit.
  • Centering Quadratic Models: Explained recentering quadratic models around specific x-values and fitting polynomials.
  • Fitted Quadratic Models for Weight Prediction: Discussed the use of quadratic models to predict weight from body measurements.
  • Inverting Quadratic Equations: Outlined the process for estimating body measurements from weight using inverted quadratic equations.
  • Imputing Missing Data: Developed a function to impute missing body measurements based on quadratic relationships with weight.

Achievements:

  • Enhanced understanding and implementation of [[data visualization]] techniques.
  • Developed and refined regression models for health-related predictions.

Pending Tasks:

  • Further validation and testing of the regression models in real-world datasets.
  • Exploration of additional [[data visualization]] techniques to improve clarity and insight.

Evidence

  • source_file=2024-04-21.sessions.jsonl, line_number=1, event_count=0, session_id=4d6f3ad509b847607c7beadbe1abd504bbbc3259918d659aeb9cfe605fc5f276
  • event_ids: []

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