📅 2024-04-21 — Session: Enhanced Data Visualization and Regression Techniques
🕒 04:30–05:20
🏷️ Labels: Data Visualization, Regression, Python, Health, Quadratic Models
📂 Project: Dev
⭐ Priority: MEDIUM
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.