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Omdena
Local Chapter project

Predicting Students' Performance Using Machine Learning Models

  • Data Visualization
  • Machine Learning
  • Data
Start date
August 25, 2022
Finish date
October 25, 2022
Project status
completed
Predicting Students' Performance Using Machine Learning Models

Challenge background

Student performance has been a global concern since it is influenced by a variety of causes and environments that vary by place. Student performance in certain places might be influenced by regional difficulties for a variety of reasons. Machine learning can be used to determine whether a student’s performance is poor or high, and it can also provide solutions by comparing low-performing students to high-performing students and observing what each of them accomplishes differently. Different prediction models will be used to guarantee that each model’s accuracy is adequate.

Project timeline

  1. 1

    Week 1

    Data Collection (pre-week 1 even)

    Data Pre-Processing

  2. 2

    Week 2

    Data Pre-Processing

  3. 3

    Week 3

    Exploratory Data Analysis

    Modelling

  4. 4

    Week 4

    Modelling (cont)

  5. 5

    Week 5

    Possible deployment into API

  6. 6

    Week 6

    Visualisation and publication

  7. 7

    Week 7

    Visualization and publication (cont.)

  8. 8

    Week 8

    Visualization and publication (cont.)

What you'll learn

1. Collection of data 2. Pre-processing of Data 3. Exploratory Data Analysis 4. Modelling 5. Model deployment into a possible API 6. Visualization and Publication

Get involved

What to expect from a Local Chapter project

First project

  • Welcomes beginners and experienced practitioners.
  • Focuses on education and collaborative delivery.
  • Produces open-source project work.

Benefits

  • Address a significant real-world problem with your skills.
  • Build your project portfolio.
  • Demonstrate your work to organizations and project partners.

Requirements

  • Working English communication.
  • A learning mindset.
  • Commitment to collaborative project work.

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