Installation and Setup

1. Install Python

Steps:

  1. Download and install Python from python.org
  2. Verify installation by running python --version in your terminal

2. Create a Virtual Environment

Steps:

  1. Install virtualenv: pip install virtualenv
  2. Create a new environment: virtualenv sklearn_env
  3. Activate the environment:
    • Windows: sklearn_env\Scripts\activate
    • macOS/Linux: source sklearn_env/bin/activate

3. Install scikit-learn

Steps:

  1. Install scikit-learn: pip install scikit-learn
  2. Install required dependencies: pip install numpy pandas matplotlib
  3. Verify installation: 
    import sklearn
print(f"scikit-learn version: {sklearn.__version__}")

Data Preprocessing

Handling Data

scikit-learn provides powerful tools for data preprocessing. Here's how to handle common data tasks:

from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer

# Create preprocessing steps
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())
])

categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))
])

# Combine transformers
preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features),
        ('cat', categorical_transformer, categorical_features)
    ])

Key Features

  • Feature Scaling

    Standardize or normalize features for better model performance

  • Encoding

    Convert categorical variables to numerical representations

  • Imputation

    Handle missing values in your dataset

Model Training

Building Models

Train various machine learning models with scikit-learn:

from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVC

# Classification example
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)

# Regression example
reg = LinearRegression()
reg.fit(X_train, y_train)

# Support Vector Machine example
svm = SVC(kernel='rbf')
svm.fit(X_train, y_train)

Model Types

  • Supervised Learning

    Classification and regression models

  • Unsupervised Learning

    Clustering and dimensionality reduction

  • Model Selection

    Cross-validation and hyperparameter tuning

Model Evaluation

Assessing Performance

Evaluate your models using various metrics:

from sklearn.metrics import accuracy_score, precision_score, recall_score
from sklearn.metrics import mean_squared_error, r2_score

# Classification metrics
y_pred = clf.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred)}")
print(f"Precision: {precision_score(y_test, y_pred)}")
print(f"Recall: {recall_score(y_test, y_pred)}")

# Regression metrics
y_pred = reg.predict(X_test)
print(f"MSE: {mean_squared_error(y_test, y_pred)}")
print(f"R2 Score: {r2_score(y_test, y_pred)}")

Evaluation Metrics

  • Classification Metrics

    Accuracy, precision, recall, F1-score

  • Regression Metrics

    MSE, RMSE, R2 score

  • Cross-Validation

    K-fold, stratified, time series splits

Model Deployment

Saving and Loading Models

Deploy your trained models:

import joblib
from sklearn.pipeline import Pipeline

# Create a complete pipeline
pipeline = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('classifier', clf)
])

# Save the model
joblib.dump(pipeline, 'model.joblib')

# Load the model
loaded_model = joblib.load('model.joblib')

# Make predictions
predictions = loaded_model.predict(new_data)

Deployment Options

  • Model Persistence

    Save and load models using joblib

  • API Deployment

    Deploy models as REST APIs

  • Cloud Deployment

    Deploy models on cloud platforms

Best Practices

Development Guidelines

Follow these best practices for efficient scikit-learn development:

  • Always preprocess your data before training
  • Use pipelines for reproducible workflows
  • Perform cross-validation for reliable results
  • Regularize models to prevent overfitting
  • Document your preprocessing steps

Next Steps

Now that you understand the basics of scikit-learn, you can:

  • Explore advanced model architectures
  • Learn about feature engineering
  • Implement custom transformers
  • Deploy models in production
Get Started with scikit-learn