Predictive Analytics with Python

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Predictive Analytics with Python | 141.03 MB

Title: Predictive Analytics with Python
Author: Rahul Kumar Thatikonda
Category: Data Processing, Programming Languages, Application Software
Language: English | 997 Pages | ISBN: 9781783983278

Description:

Build Models That Survive Beyond the Notebook.

Key Features
● Get a free one-month digital subscription to www.avaskillshelf.com.
● Production-first data pipeline engineering using Polars for high-performance ETL and Pandera for data contracts.
● Full MLOps lifecycle coverage with CRISP-DM, Scikit-Learn, XGBoost, MLflow experiment tracking, and hyperparameter tuning.
● Enterprise-grade capstone project building an Automated Valuation Model deployed with Docker and FastAPI.

Book Description
Data Science Finds the Signal. Engineering Turns It into Business Value.

Moving a model from a Jupyter Notebook to a production system requires engineering discipline, not just data science skills. Predictive Analytics with Python is the definitive guide for the engineering-first era of data science, helping you transition from fragile notebook workflows to resilient, production-ready predictive systems built for real-world infrastructure.

You begin by replacing slow legacy workflows with a modern technical stack, high-performance ETL with Polars, data contract enforcement with Pandera, and resilient Scikit-Learn and XGBoost pipelines with rigorous feature engineering, cross-validation, and experiment tracking using MLflow. The book then advances into time-series forecasting with Nixtla before covering model serialisation, REST API deployment with FastAPI, Docker containerisation, and production monitoring as well as governance.

The book culminates in an end-to-end capstone project building an enterprise-grade Automated Real Estate Valuation Model. By the end, you will engineer predictive systems that prioritize stability, auditability, and transformative business value.

What you will learn
● Transition fragile notebook workflows into robust production-grade software engineering practices.
● Execute high-performance ETL and data processing using the Polars library at scale.
● Enforce rigorous data contracts using Pandera to validate pipeline inputs automatically.
● Build resilient predictive pipelines using Scikit-Learn and XGBoost with production patterns.
● Automate hyperparameter tuning and track model experiments using MLflow effectively.

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