Duration

3 Days

Audience

Employees of federal, state and local governments; and businesses working with the government.

Course Overview

Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages.

Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models.  Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python’s data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.

This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises.  Our engaging instructors and mentors are highly experienced practitioners who bring years of current “on-the-job” experience into every classroom.

Learning Objectives

Working in a hands-on learning environment, guided by our expert team, attendees will learn to:

  • Understand the main concepts and principles of predictive analytics
  • Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects
  • Explore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanations
  • Learn to deploy a predictive model’s results as an interactive application
  • Learn about the stages involved in producing complete predictive analytics solutions
  • Understand how to define a problem, propose a solution, and prepare a dataset
  • Use visualizations to explore relationships and gain insights into the dataset
  • Learn to build regression and classification models using scikit-learn
  • Use Keras to build powerful neural network models that produce accurate predictions
  • Learn to serve a model’s predictions as a web application

Course Outline

  1. The Predictive Analytics Process
  • Technical requirements
  • What is predictive analytics?
  • Reviewing important concepts of predictive analytics
  • The predictive analytics process
  • A quick tour of Python’s data science stack
  1. Problem Understanding and Data Preparation
  • Technical requirements
  • Understanding the business problem and proposing a solution
  • Practical project – diamond prices
  • Practical project – credit card default
  1. Dataset Understanding – Exploratory Data Analysis
  • Technical requirements
  • What is EDA?
  • Univariate EDA
  • Bivariate EDA
  • Introduction to graphical multivariate EDA
  1. Predicting Numerical Values with Machine Learning
  • Technical requirements
  • Introduction to ML
  • Practical considerations before modeling
  • MLR
  • Lasso regression
  • KNN
  • Training versus testing error
  1. Predicting Categories with Machine Learning
  • Technical requirements
  • Classification tasks
  • Credit card default dataset
  • Logistic regression
  • Classification trees
  • Random forests
  • Training versus testing error
  • Multiclass classification
  • Naive Bayes classifiers
  1. Introducing Neural Nets for Predictive Analytics
  • Technical requirements
  • Introducing neural network models
  • Introducing TensorFlow and Keras
  • Regressing with neural networks
  • Classification with neural networks
  • The dark art of training neural networks
  1. Model Evaluation
  • Technical requirements
  • Evaluation of regression models
  • Evaluation for classification models
  • The k-fold cross-validation
  1. Model Tuning and Improving Performance
  • Technical requirements
  • Hyperparameter tuning
  • Improving performance
  1. Implementing a Model with Dash
  • Technical requirements
  • Model communication and/or deployment phase
  • Introducing Dash
  • Implementing a predictive model as a web application