Feature Engineering for Machine Learning in Python: A Practical Guide to Creating Data Features That Drive Model Performance Using Python and Scikit-Learn: Techniques, Pipelines, and Case Studies - Tapa blanda

Sinopsis

• Master the Foundations of Feature Engineering: Understand what features are, why they're crucial for machine learning model performance and interpretability, and navigate the complete feature engineering lifecycle from brainstorming to deployment.

• Set Up Your Python Environment for Success: Become proficient with essential libraries like NumPy, Pandas, and Scikit-learn for data manipulation, analysis, and feature engineering implementation.

• Handle Missing Data Effectively: Learn to identify and apply various imputation strategies for both numerical and categorical data, ensuring your models receive clean and complete inputs.

• Transform Numerical Data for Optimal Performance: Discover techniques for scaling, normalizing, discretizing, and transforming skewed numerical features to meet model assumptions and improve accuracy.

• Encode Categorical Data for Machine Learning Models: Explore a wide array of encoding methods, from One-Hot and Label Encoding to advanced techniques like Target and WoE Encoding, and understand when to apply each for different data types and models.

• Engineer Powerful Features from Text Data: Master text preprocessing, apply Bag-of-Words and TF-IDF models, and leverage word embeddings (Word2Vec, GloVe) to extract meaningful insights from unstructured text.

• Extract Actionable Insights from Time Series Data: Learn to create date-time components, lag features, rolling window statistics, and incorporate seasonality and trend information for robust time series modeling.

• Gain an Overview of Feature Engineering for Specialized Data: Get introduced to key techniques for image, geospatial, and graph data, and understand how to leverage pre-trained models for feature extraction.

• Select the Most Relevant Features: Implement various feature selection methods, including filter, wrapper, and embedded techniques, to reduce dimensionality, combat overfitting, and enhance model interpretability.

• Apply Dimensionality Reduction Techniques: Understand and utilize methods like PCA, LDA, t-SNE, and UMAP to reduce the number of features while preserving essential information.

• Automate and Streamline Feature Engineering Workflows: Explore tools like Featuretools and tsfresh to automate feature creation, saving time and improving efficiency.

• Build Robust and Reproducible Feature Engineering Pipelines: Learn to construct and manage end-to-end pipelines using Scikit-learn, ensuring consistency and preventing data leakage.

• Prevent Data Leakage and Build Trustworthy Models: Identify common sources of data leakage in feature engineering and implement strategies to avoid it, leading to more reliable model evaluations.

• Understand Feature Stores and Their Role in MLOps: Grasp the concepts of feature stores for consistent feature management, reusability, and deployment in production environments.

• Apply Feature Engineering to Real-World Problems: Work through practical case studies in customer churn prediction, sentiment analysis, and sales forecasting, consolidating your knowledge across different data types.

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