DSS: Missing Value Imputation Using Multiple Imputation by Chained Equations

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
Are You an Alumni?
Alumni FREE Access

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.

LEARNING OUTCOMES

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

Syllabus

Python Programming Basics

Working with Conda Environment
Introduction to Python for data science
Data manipulation and processing with Python Pandas

Multiple Imputation by Chained Equations (MICE)

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

Case Study: Missing Value Imputation on Obesity Level Dataset

Import Libraries
Read Data
Exploratory Data Analysis
Data Preparation
Implement Statistical imputation and MICE Imputation
Evaluation and Check Performance

STUDENT TESTIMONIALS

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

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LEARN FROM ANYWHERE

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.

LEARN AT YOUR OWN PACE
Zoom recording, course Books (PDF & HTML files), the dataset for practice, reference notes, and working files are accessible through our Learning Management System account.
PROOF YOUR MASTERY
Show current and prospective employers of your mastery in computer vision with a signed certificate of completion.
CONNECT WITH LIKE MINDED PEOPLE
Be a part of our data-passionate community with 5000+ members and 1000+ alumni.

FOR ABSOLUTE BEGINNERS

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.

If I don’t have any IT or programming skills, can I still attend this workshop?

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

How to join the interactive-online learning class after I’ve done the payment & registration?

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

What platform will be utilized for this online-interactive learning workshop?

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

How will the participants receive the learning materials?

Learning materials can be obtain via Google Classroom

Would I receive a certificate after participating in the Workshop?

Yes, you will receive a certificate of completion.

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YOUR INSTRUCTOR

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.

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Missing Value Imputation Using MICE

Course Summary

LEARNING OUTCOMES

Syllabus

STUDENT TESTIMONIALS

LEARN FROM ANYWHERE

FOR ABSOLUTE BEGINNERS

YOUR INSTRUCTOR

Cut Amalia Saffiera

Accessible Education

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