Machine Learning with scikit-learn
Build and evaluate machine learning models using Python's scikit-learn library
Getting Started with scikit-learn
Learn how to implement various machine learning algorithms using scikit-learn, from data preprocessing to model evaluation and hyperparameter tuning.
Prerequisites
- Basic Python programming knowledge
- Understanding of NumPy and Pandas
- Basic statistics and mathematics
- Jupyter Notebook environment
1. Data Preprocessing
Learn essential preprocessing techniques for preparing your data.
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import train_test_split
# Load and preprocess data
X = df.drop('target', axis=1)
y = df['target']
# Split the data
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
# Scale features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)2. Model Training
Implement and train various machine learning models.
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
# Train multiple models
models = {
'logistic': LogisticRegression(),
'random_forest': RandomForestClassifier(),
'svm': SVC()
}
# Train each model
for name, model in models.items():
model.fit(X_train_scaled, y_train)
print(f"{name} score: {model.score(X_test_scaled, y_test):.4f}")3. Model Evaluation
Evaluate model performance using various metrics.
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Make predictions
y_pred = model.predict(X_test_scaled)
# Print classification report
print(classification_report(y_test, y_pred))
# Plot confusion matrix
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True, fmt='d')
plt.show()4. Hyperparameter Tuning
Optimize model performance through hyperparameter tuning.
from sklearn.model_selection import GridSearchCV
# Define parameter grid
param_grid = {
'n_estimators': [100, 200, 300],
'max_depth': [10, 20, 30, None],
'min_samples_split': [2, 5, 10]
}
# Perform grid search
grid_search = GridSearchCV(
RandomForestClassifier(),
param_grid,
cv=5,
scoring='accuracy',
n_jobs=-1
)
grid_search.fit(X_train_scaled, y_train)
print("Best parameters:", grid_search.best_params_)
print("Best score:", grid_search.best_score_)