Python ML example

Wed 07 Feb 2024, 10:36

Sure, let's go through a simple machine learning example using Python. We'll use the popular Scikit-learn library to train a model on the Iris dataset, which is a classic dataset in machine learning. The Iris dataset contains 150 instances of iris plants, each described by 4 features (sepal length, sepal width, petal length, and petal width) and associated with one of three species (setosa, versicolor, or virginica).

Our goal will be to train a model to predict the species of an iris plant based on these features.

First, ensure you have Scikit-learn installed. You can install it via pip if you haven't already:

pip install scikit-learn

Here's a step-by-step Python code example:

from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import confusion_matrix, accuracy_score # Load the Iris dataset iris = load_iris() X = iris.data # Features y = iris.target # Target variable (species) # Split dataset into training set and test set X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # 80% training and 20% testing # Feature scaling scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Initialize the model (using K-Nearest Neighbors classifier) knn = KNeighborsClassifier(n_neighbors=5) # Train the model knn.fit(X_train, y_train) # Predict on the test set y_pred = knn.predict(X_test) # Evaluate the model print("Confusion Matrix: ", confusion_matrix(y_test, y_pred)) print("Accuracy:", accuracy_score(y_test, y_pred))

This example follows these steps:

1. Load the Iris dataset: Scikit-learn provides helper functions to load several popular datasets.
2. Split the data: We divide the dataset into a training set and a testing set, using 80% of the data for training and 20% for testing.
3. Feature Scaling: Standardize features by removing the mean and scaling to unit variance. This step is crucial for many machine learning algorithms.
4. Initialize the Model: We use the K-Nearest Neighbors (KNN) classifier, a simple yet powerful classification algorithm.
5. Train the Model: Fit the model to the training data.
6. Predict: Use the trained model to make predictions on the test set.
7. Evaluate the Model: Assess the model's performance by comparing the predicted species with the actual species from the test set, using metrics such as the confusion matrix and accuracy.

This is a basic example to get you started with machine learning in Python. From here, you can explore more complex algorithms, hyperparameter tuning, cross-validation, and more advanced preprocessing techniques.

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