Build intuition with hands-on machine learning experiments.
A curated portfolio of ML lab work with code, visual outputs, and concise algorithm explanations. Explore regression, classification, clustering, and beyond.
Featured experiment
PCA: 4D to 2D Iris Projection
See how dimensionality reduction reveals separable species clusters.
Datasets
9+
Real and synthetic datasets
Algorithms
12
From Find-S to K-means
Outputs
30+
Charts, plots, and rule exports
Categories
Core ML themes
Track 1
Exploratory Analysis
Track 2
Dimensionality Reduction
Track 3
Concept Learning
Track 4
Semi-Supervised Learning
Track 5
Regression
Track 6
Classification
Track 7
Clustering
Featured experiments
Visual deep dives
Data Exploration + Outlier Scan
Histograms, box plots, and IQR-based outlier detection for California Housing features.
Correlation Mapping
Correlation heatmap and pair plot to reveal feature relationships.
PCA Dimensionality Reduction
Reduce Iris dataset from 4D to 2D and visualize class separation.
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