Predicting Students' Performance Using Machine Learning Models

Start Date: August 25, 2022 | 4 years ago

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

Data Collection (pre-week 1 even)

Data Pre-Processing

Exploratory Data Analysis

Modelling

Modelling (cont)

Possible deployment into API

Visualisation and publication

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

First Omdena Local Chapter Project?

Beginner-friendly, but also welcomes experts

Education-focused

Duration: 4 to 8 weeks

Open-source

Your Benefits

Address a significant real-world problem with your skills

Build your project portfolio

Access paid projects (as an Omdena Top Talent)

Get hired at top organizations

Requirements

Good English

Suitable for AI/ Data Science beginners but also more senior collaborators

Learning mindset

Application Form

Application Closed.