What is Machine Learning?
Machine Learning (ML) is a branch of Artificial Intelligence that enables systems to learn patterns from data and make predictions or decisions without being explicitly programmed.
ML is widely used in applications like recommendation systems, spam detection, image recognition, and self-driving cars.
Scikit-learn Overview
Scikit-learn is a popular Python library for machine learning. It provides simple and efficient tools for data analysis and modeling, including:
- Classification (predicting categories)
- Regression (predicting continuous values)
- Clustering (grouping similar data)
- Dimensionality reduction
- Model evaluation and selection
Installation
You can install Scikit-learn using pip:
pip install scikit-learn
It also requires NumPy and SciPy for numerical computations.
Simple Example: Predicting Iris Species
Scikit-learn comes with many built-in datasets. Here's a simple example using the Iris dataset:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
# Load dataset
iris = load_iris()
X = iris.data
y = iris.target
# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create a Random Forest model
model = RandomForestClassifier()
# Train the model
model.fit(X_train, y_train)
# Predict on test data
y_pred = model.predict(X_test)
# Evaluate accuracy
print("Accuracy:", accuracy_score(y_test, y_pred))
This example trains a Random Forest Classifier to predict the species of iris flowers based on their features.
Typical ML Workflow with Scikit-learn
- Collect and load data
- Explore and preprocess data (cleaning, scaling, encoding)
- Split data into training and test sets
- Choose a suitable ML model (e.g., Linear Regression, Random Forest)
- Train the model on training data
- Make predictions on test data
- Evaluate model performance (accuracy, RMSE, etc.)
- Tune hyperparameters and improve the model