Using scikit-learn, train a baseline classification model (e.g., Logistic Regression or Random Forest) on a sample dataset. Split the data into training and testing sets, fit the model, and print accuracy results.

• By Mohsin shakoor
• August 25, 2025August 25, 2025
• Education

Import Libraries

• Import scikit-learn libraries (datasets, model, train_test_split, accuracy_score).

Load Dataset

• Use a built-in dataset (e.g., Iris, Breast Cancer, Digits) from scikit-learn or your own dataset.

Split Data

• Divide the dataset into features (X) and target (y).
• Use train_test_split to split into training and testing sets (e.g., 80% train, 20% test).

Choose a Model

• Select a baseline classifier (e.g., Logistic Regression, Random Forest, Decision Tree).

Train the Model

• Fit the model using the training data (X_train, y_train).

Make Predictions

• Use the trained model to predict outcomes on the test set (X_test).

Evaluate Performance

• Compare predictions with actual labels (y_test).
• Print metrics like accuracy score (and optionally precision, recall, F1).

(Optional) Improve Model

• Try different classifiers, tune hyperparameters, or use feature scaling for better performance

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