Missing Value Imputation Using MICE

Learn how to make a better future analysis and machine learning model for prediction. 

  • Schedule

    24 -26 January 2023

    18.30 – 21.30 (WIB)

  • Online-Interactive Learning

    Via Zoom

  • Investment

    Rp. 1.500.000

Course Summary

In data science workflows, a pre-processing task is required which includes data cleaning to ensure data quality before the analysis process. The problem of missing value is quite common in many real-life datasets. Missing value can bias the results of the machine learning models and/or reduce the accuracy of the model. Missing value imputation (MVI) is the solution method most commonly used to deal with the incomplete dataset problem.

In general, MVI is a process in which some statistical or machine learning techniques are used to replace the missing data with substituted values. However, the main limitation of using statistical imputation with measures of central tendency is that it leads to biased estimates of variance and covariance. Therefore, techniques with machine learning emerged as another alternative to overcome the weaknesses of statistical methods. One of the machine learning algorithms in the imputation technique is MICE.

This 3-day online workshop is a beginner-friendly introduction to Missing Value Imputation Using Multiple Imputation by Chained Equations (MICE). By performing a more accurate missing value imputation on your data, you will be able to make a better future analysis or even machine learning model for prediction. 

Throughout the online course, we will provide participants with a rich interactive experience. One Instructor and two Teaching Assistants will help participants to troubleshoot or help with any difficulties encountered by participants.


Upon completion of this workshop, you will be able to:

  • Work with Python and pandas for data cleansing and manipulation processes.
  • Understand a few techniques to handle missing value (Simple and Advance)
  • Learn and implement how MICE works on imputing missing value
  • Evaluate and compare how good the Missing Value Imputation with MICE compare to Statistical Imputation


  • Working with Conda Environment
  • Introduction to Python for data science
  • Data manipulation and processing with Python Pandas
  • Main types of Missing Data
  • Label Encoding for Categorical Data
  • Artificially Create Missing Value
  • MICE Algorithm
  • Evaluation Metrics (Accuracy, MSE, RMSE)
  • Compare MICE Performance to Statistical Imputation
  • Main types of Missing Data
  • Label Encoding for Categorical Data
  • Artificially Create Missing Value
  • MICE Algorithm
  • Evaluation Metrics (Accuracy, MSE, RMSE)
  • Compare MICE Performance to Statistical Imputation
  • Import Libraries
  • Read Data
  • Exploratory Data Analysis
  • Data Preparation
  • Implement Statistical imputation and MICE Imputation
  • Evaluation and Check Performance


This testimonial video is taken after our previous Online Data Science Series: Time Series Analysis for Business Forecasting.


Our learning format is online-interactive, you will feel the interactive experience as if you were present in a physical classroom. You can access the class using your Zoom account on pre-defined dates.


    Zoom recording, course Books (PDF & HTML files), the dataset for practice, reference notes, and working files are accessible through our Learning Management System account.


    Show current and prospective employers of your mastery in computer vision with a signed certificate of completion.


    Be a part of our data-passionate community with 5000+ members and 1000+ alumni.


Workshops in this series are tailored to casual programmers and non-programmers that are taking their first steps into data science. It assumes no prior knowledge or academic background, and attendees will be introduced to the beautiful art of writing R / Python code to produce data visualization and build machine learning models. The workshop has a gentle learning slope that is designed with non-technical professionals and academics in mind.

Yes, you can still attend the workshop as it is a beginner-friendly workshop.

Our system will send you an email containing a link and details to join a Google Classroom.

Online learning will be conducted via Zoom.us, Link to join the Zoom Class will be announced via Google Classroom.

Learning materials can be obtain via Google Classroom

Yes, you will receive a certificate of completion.


Cut Amalia Saffiera

Cut Amalia Saffiera graduated with a Master’s degree from International Islamic University Malaysia majoring in Computer Science. Previously she completed her studies with a Bachelor’s degree from the University of Sumatera Utara in Computer Science. Her study focuses on Data Science and Machine Learning.

As Data Science Instructor, she has been involved in numerous mentoring, data science projects, and consultative data science training for our clients, for example, People Analytics using Python: Python for Data Analysis for PLN (Perusahaan Listrik Negara), and many more.