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![Pipelines in Scikit-learn
from sklearn.pipeline import Pipelinefrom
sklearn.preprocessing import StandardScalerfrom
sklearn.linear_model import
LogisticRegressionpipeline = Pipeline([ ('scaler',
StandardScaler()), ('model',
LogisticRegression())])pipeline.fit(X_train, y_train)
Why Pipelines?
• Chain preprocessing and modeling steps.
• Reduce code redundancy.
• Better for production-ready workflows.](https://image.slidesharecdn.com/scikit-learn-with-python-a-comprehensive-overview-250801093126-1b75d82f/85/Scikit-learn-with-Python-A-Comprehensive-Overview-pptx-9-320.jpg)
Scikit-learn-with-Python-A-Comprehensive-Overview.pptx
- 1. Scikit-learn with Python: AComprehensive Overview Simplifying Machine Learning for Everyone
- 2. Introduction to Scikit- learn Whatis Scikit-learn? • Python-based open-source library for machine learning. • Built on NumPy, SciPy, and Matplotlib. Why use Scikit-learn? • Simple, efficient tools for predictive data analysis. • Consistent API and excellent documentation. • Ideal for beginners and professionals.
- 3. Core Features Supervised Learning Classification& Regression Unsupervised Learning Clustering & Dimensionality Reduction Model Selection & Evaluation Assess model performance Preprocessing & Transformation Prepare data for modeling Pipelines & Cross- validation Streamline workflows
- 4. Popular Algorithms Classification • LogisticRegression • k-Nearest Neighbors • Support Vector Machines • Decision Trees & Random Forest Regression • Linear Regression • Ridge, Lasso Clustering • k-Means • DBSCAN Dimensionality Reduction • PCA • t-SNE (via external libs)
- 5. Installation & Setup Toinstall Scikit-learn, use pip: pip install scikit-learn Requires Python (>=3.7), NumPy, SciPy. Often used with Jupyter Notebooks for interactive development.
- 6. Example: Classification withScikit-learn from sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifier# Load dataX, y = load_iris(return_X_y=True)X_train, X_test, y_train, y_test = train_test_split(X, y)# Train modelclf = RandomForestClassifier()clf.fit(X_train, y_train)# Predict and evaluatepredictions = clf.predict(X_test) This example demonstrates loading data, splitting it, training a RandomForestClassifier, and making predictions.
- 7. Preprocessing Techniques StandardScaler Scales featuresto a standard normal distribution. MinMaxScaler Normalizes values to a specific range (e.g., 0 to 1). LabelEncoder / OneHotEncoder Encodes categorical variables into numerical format. Imputer / SimpleImputer Handles missing values in datasets.
- 8. Model Evaluation Metrics ClassificationMetrics • Accuracy • Precision • Recall • F1 Score Regression Metrics • Mean Absolute Error (MAE) • Mean Squared Error (MSE) • R² Score Evaluation Tools • cross_val_score • train_test_split • GridSearchCV • RandomizedSearchCV
- 9. Pipelines in Scikit-learn fromsklearn.pipeline import Pipelinefrom sklearn.preprocessing import StandardScalerfrom sklearn.linear_model import LogisticRegressionpipeline = Pipeline([ ('scaler', StandardScaler()), ('model', LogisticRegression())])pipeline.fit(X_train, y_train) Why Pipelines? • Chain preprocessing and modeling steps. • Reduce code redundancy. • Better for production-ready workflows.
- 10. Summary & NextSteps • Scikit-learn is a robust library for ML in Python. • Great for beginners and scalable for advanced workflows. • Covers preprocessing, modeling, evaluation, and deployment. Learn Scikit-learn with ERPVITS Enroll in “Scikit-learn with Python” at https://erpvits.com/ Real-world projects, hands-on sessions, and expert mentorship. Available in Hyderabad, Pune, Bangalore, and Online.