Add docs with detailed write-ups and result visualizations

docs/MNIST-Classification.md

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+# MNIST Classification
+
+A full tour of classification problem types on the MNIST handwritten digit dataset — binary, multiclass, multilabel, and multioutput — with an emphasis on evaluating models honestly rather than trusting raw accuracy.
+
+## Why not just report accuracy?
+
+About 90% of MNIST digits are *not* an 8. A classifier that always guesses "not 8" scores ~90% accuracy while learning nothing. This project compares models with ROC AUC and F1 instead, and validates every "is it learning something real" claim against a `DummyClassifier` baseline.
+
+## Linear vs ensemble: SGD vs Random Forest
+
+Same binary task ("is it an 8?"), two very different models:
+
+![ROC curve comparison](images/clf-roc-comparison.png)
+
+| Model | F1 | ROC AUC |
+|---|---|---|
+| SGD (linear) | 0.58 | 0.915 |
+| Random Forest (ensemble) | 0.90 | 0.997 |
+
+The gap illustrates a core limitation of linear models: SGD can only draw one straight decision boundary through 784-dimensional pixel space, while Random Forest's ensemble of trees carves non-linear regions that fit the actual shape of the data.
+
+## Where the multiclass model actually gets confused
+
+Extending to all 10 digits, the normalized confusion matrix shows exactly which digits get mistaken for which — more useful than a single accuracy number:
+
+![Multiclass confusion matrix](images/clf-confusion-matrix.png)
+
+## Multioutput: predicting an entire image
+
+Pushed the idea further — instead of predicting a label, predicting all 784 pixel values at once, framed as denoising a noisy digit:
+
+<table>
+<tr>
+<td><img src="images/clf-denoise-original.png" width="200"/></td>
+<td><img src="images/clf-denoise-noisy.png" width="200"/></td>
+<td><img src="images/clf-denoise-predicted.png" width="200"/></td>
+</tr>
+<tr>
+<td align="center">Original</td>
+<td align="center">Noisy input</td>
+<td align="center">Model's denoised output</td>
+</tr>
+</table>
+
+## Notebooks
+
+- [`classification.ipynb`](../classification-project/classification.ipynb) — original work
+- [`classification_notebook_polished.ipynb`](../classification-project/classification_notebook_polished.ipynb) — AI-assisted documentation pass