Day 12 - February 16, 2026
Date: February 16, 2026
Week: 23
Internship: AI/ML Intern at SynerSense Pvt. Ltd.
Mentor: Praveen Kulkarni Sir
1. Scatter Plot Rendering Stability Improvements
Problem
The scatter plot canvas was auto-correcting horizontally during browser zoom, but vertically it failed to adjust correctly. This caused:
- Partial graph visibility (top half visible, bottom clipped)
- Previous drifting issues between visual points and hover detection
- Inconsistent rendering across 80%, 100%, and 125% zoom
Root Cause
Canvas width and height were not symmetrically synchronized with container dimensions. Horizontal resizing logic was implemented correctly, but vertical scaling logic did not fully mirror it. This created coordinate mismatches between rendering space and interaction space.
Actions Taken
- Applied the same responsive resizing logic used for width to height.
- Ensured canvas height always matches container height.
- Prevented infinite vertical growth behavior.
- Stabilized coordinate mapping between mouse events and rendered points.
Outcome
- Horizontal and vertical rendering behavior now consistent.
- Points drifting issue resolved.
- Zoom-based misalignment eliminated.
- Canvas resizing behaves predictably across zoom levels.
Impact: Improved geometric accuracy and interaction reliability.
2. Legend Overlay Layout Issue Resolution
Problem
At higher zoom levels, the legend section overlapped the canvas area, reducing usable visualization space and interfering with data clarity.
Actions Taken
- Separated legend from the canvas render region.
- Adjusted layout structure so canvas occupies full available viewport.
- Moved legend below the visualization area.
- Ensured legend appears only when scrolling, not overlaying graph.
Outcome
- Full-screen canvas rendering restored.
- No visual obstruction during zoom.
- Cleaner UI hierarchy.
Impact: Improved visual clarity and user experience.
3. Error Gradient Visualization Enhancement
Problem
Linear normalization caused most error points to appear pale, making the dataset look artificially low-error even when meaningful deviations existed.
Initial Fix
- Implemented 95th percentile cap.
- Applied power scaling to boost mid-range errors.
- Adjusted color intensity mapping.
Refinement
The single-tone gradient approach reduced clarity. Updated to:
- Use green → yellow → red gradient.
- Preserve intuitive error semantics.
- Green = low error
- Red = high error
- Increase contrast for mid-range deviations.
Outcome
- Errors are now visually distinguishable.
- Moderate deviations are clearly visible.
- High-error clusters stand out without distorting distribution.
Impact: Improved analytical interpretability of model performance.
4. Axis Scaling Strategy Evaluation
Discussion Explored
- Fix both axes to [0,1]
- Fix actual axis only
- Allow predicted axis to float
- Hybrid scaling approach
Decision Direction
Moving toward:
- Fixing actual axis to [0,1]
- Allowing predicted axis to extend beyond bounds when necessary
- Preserving geometric meaning while still highlighting overshoot
This balances interpretability and diagnostic flexibility.
Impact: Better analytical consistency across datasets.
Current System Status
- Canvas resizing: Stable
- Hover detection: Accurate
- Legend layout: Corrected
- Error gradient: Enhanced and interpretable
- Axis scaling: Strategically evaluated
Engineering Value Delivered Today
- Removed structural UI instability
- Improved geometric correctness
- Increased error visibility fidelity
- Strengthened analytical reliability of the visualization module