Problem-solving guide

Core Python and files

• Use variables to store values, then combine them with expressions.
• Use a loop and accumulator when a result must be built step by step.
• Use with open(...) to write text files safely.
• Know which file commands are Colab-specific: drive.mount, files.download, and files.upload.

Images

• Load with Pillow using Image.open(path), or with OpenCV using cv2.imread(path).
• Inspect .size, .mode, and .shape.
• Crop with array slicing: image[y1:y2, x1:x2].
• Convert OpenCV BGR images to RGB before displaying with Matplotlib.

Datasets and arrays

• For scikit-learn datasets, use dataset.data for features and dataset.target for labels.
• Use dataset.target_names and dataset.feature_names to interpret the numbers.
• Use NumPy slicing: array[start:stop:step, columns].
• Use np.unique(y, return_counts=True) to count classes.

Train/test splitting

• Manual slicing is possible, but train_test_split is the standard tool.
• Always split X and y together so rows and labels remain aligned.
• Use random_state when you want reproducible splits.

Plotting

• Use plt.plot for line plots and plt.scatter for point clouds.
• Use labels and legends to distinguish multiple series.
• Use plt.subplot(rows, columns, position) to build grids of many images.

Clustering

• Clustering is unsupervised: it groups data without true class labels.
• Create KMeans(n_clusters=...), then use fit and predict.
• Use centroids to understand cluster centers.
• Use elbow and silhouette methods to evaluate cluster choices.

Regression

• Regression predicts continuous numbers.
• Use LinearRegression().fit(X_train, y_train).
• For one feature, reshape X with reshape(-1, 1).
• Interpret intercept_ and coef_ as the regression equation.

Classification

• Classification predicts discrete class labels.
• Train KNN with KNeighborsClassifier(n_neighbors=...).
• Measure performance with accuracy, confusion matrix, precision, recall, F1-score, and classification_report.
• Compare models by using the same train/test split.