📅 2024-04-21 — Session: Enhanced Data Visualization and Regression Techniques
🕒 04:30–05:20
🏷️ Labels: Data Visualization, Regression, Python, Matplotlib, Health
📂 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: Adjusted line chart colors for better visibility and accessibility using Python’s matplotlib.
- Boxplot Improvements: Enhanced boxplot readability by grouping weeks into months and rotating x-axis labels.
- Unique Month-Year Labels for Boxplots: Created boxplots with unique month-year labels by modifying DataFrame and plotting functions.
- Regression Techniques for Weight Prediction: Explored various regression techniques, including multiple linear regression and polynomial regression, for modeling body measurements and weight relationships.
- Quadratic Curve Fitting: Implemented quadratic curve fitting using numpy’s polyfit function.
- Centering Quadratic Models: Centered quadratic models around specific x-values for better data transformation.
- Weight Prediction Models: Developed quadratic models to predict weight from body measurements.
- Inverting Quadratic Equations: Used quadratic equations to estimate body measurements from weight.
- Data Imputation: Created a Python function for imputing missing body measurements using a quadratic fit.
Achievements:
- Successfully customized visualization techniques for improved data readability and accessibility.
- Developed and implemented regression models for accurate weight prediction based on body measurements.
Pending Tasks:
- Further validation of regression models with additional datasets to ensure robustness and accuracy.
- Exploration of alternative visualization libraries for enhanced data representation.