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Predictive Analytics 1 with Python – Machine Learning Tools

Predictive Analytics 1 with Python – Machine Learning Tools

This course introduces the basic paradigm for predictive modeling: classification and prediction.


In this course you will be introduced to basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining. You will cover two core paradigms that account for most business applications of predictive modeling: classification and prediction. You will also study commonly used machine learning techniques and learn how to combine models to obtain optimal results. This course includes hands-on work with Python, a free software environment with statistical computing capabilities.

  • Introductory, Intermediate
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

At the conclusion of this course you will be able to visualize and explore data, provide an assessment basis for predictive models, and choose appropriate performance measures. You will become familiar with common algorithms including k-nearest-neighbor, Naive Bayes, Classification and Regression Trees, as well as ensemble models.

  • Visualize and explore data to better understand relationships among variables
  • Organize the predictive modeling task and data flow
  • Develop machine learning models with the KNN, Naive Bayes and CART algorithms using Python’s scikit-learn
  • Assess the performance of these models with holdout data
  • Apply predictive models to generate predictions for new data
  • Use Python’s sci-kit learn package to implement the models in the course

Who Should Take This Course

Marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters.  This course is especially useful if you want to understand what predictive modeling might do for your organization, undertake pilots with minimum setup costs, manage predictive modeling projects, or work with consultants or technical experts involved with ongoing predictive modeling deployments.

Our Instructors

Mr. Kuber Deokar

Mr. Kuber Deokar

Mr. Kuber Deokar holds a Masters degree in Statistics from University of Pune, India, where he also taught undergraduate statistics. Mr. Deokar holds the position of Instructional Operations Supervisor at He is responsible for coordination of online courses, and ensures seamless interactions between the management team, course instructors, teaching assistants, and students. He also serves as the senior teaching assistant and shares instructional responsibilities for several courses, and handles consultancy assignments, working from our office in Pune, India.

Course Syllabus

Week 1


  • What is supervised learning
  • Data partitioning and holdout samples
  • Choosing variables (features)
  • Handling missing data
  • Visualization and exploration

Week 2

Classification and Prediction

  • Assessing classification models
    • Confusion matrix
    • Misclassification costs
    • Lift
  • Assessing prediction models
    • Common metrics
  • K-Nearest-Neighbors (KNN)
    • Measuring distance
    • Choosing k
    • Generating classifications and predictions

Week 3

Bayesian Classifiers; CART

  • Full Bayes classifier
  • Naive Bayes classifier
  • Classification and Regression Trees (CART)
    • Growing the tree
    • Avoiding overfit – pruning
    • Using trees for classifications and predictions

Week 4


  • Combine multiple algorithms
  • Improve results

Class Dates


09/08/2023 to 10/06/2023
Instructors: Mr. Kuber Deokar


01/12/2024 to 02/09/2024
Instructors: Mr. Kuber Deokar
05/10/2024 to 06/07/2024
Instructors: Mr. Kuber Deokar
09/13/2024 to 10/11/2024
Instructors: Mr. Kuber Deokar


01/10/2025 to 02/07/2025
Instructors: Mr. Kuber Deokar
05/09/2025 to 06/06/2025
Instructors: Mr. Kuber Deokar
09/12/2025 to 10/10/2025
Instructors: Mr. Kuber Deokar


You should be familiar with Python, as covered in our Python for Analytics course.

Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

Frequently Asked Questions

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Predictive Analytics 1 with Python – Machine Learning Tools

Additional Information


Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and end of course data modeling project. Note: There will be a mid-week discussion exercise in the first week of the course.

In addition to assigned readings, this course also has supplemental video lectures, and an end of course data modeling project.

Course Text

The required text for this course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python, by Shmueli, Bruce, Gedeck, and Patel. This same text is also used in the follow on courses: “Predictive Analytics 2 – Neural Nets and Regression – with Python” and “Predictive Analytics 3 – Dimension Reduction, Clustering and Association Rules – with Python”


The course includes hands-on work with Python, a free software environment with statistical computing capabilities.

Supplemental Information

Take a 10-question quiz on analytics: Test Yourself

Whatch our preview of this course:


Watch this video by Dr. Shmueli on “Data Mining in a Nutshell”.

Register For This Course

Predictive Analytics 1 with Python – Machine Learning Tools