Better — Download Fetch_olivetti_faces

Lighting conditions, facial expressions (open/closed eyes, smiling/not smiling), and facial details (glasses/no glasses). 🚀 Downloading the Dataset via Scikit-Learn

Training linear classifiers (SVM, Logistic Regression) or Deep Learning blocks to predict the individual identity label (0–39). download fetch_olivetti_faces

import matplotlib.pyplot as plt def plot_faces(images, targets, num_rows=2, num_cols=5): fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, 5), subplot_kw={'xticks':[], 'yticks':[]}) for i, ax in enumerate(axes.flat): ax.imshow(images[i], cmap='gray') ax.set_title(label=f"ID: {targets[i]}", fontsize=9) plt.tight_layout() plt.show() # Plot the first 10 images from the dataset plot_faces(olivetti_data.images, olivetti_data.target, num_rows=2, num_cols=5) Use code with caution. ⚙️ Preprocessing for Machine Learning ⚙️ Preprocessing for Machine Learning : A 3D

: A 3D numpy array of shape (400, 64, 64) . This preserves the spatial pixel structure for easy rendering. facial expressions (open/closed eyes

To confirm successful download, use Matplotlib to visualize a grid of faces showing different subjects or variations of a single individual.