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
Week 1
Data Collection (pre-week 1 even)
Data Pre-Processing
- 2
Week 2
Data Pre-Processing
- 3
Week 3
Exploratory Data Analysis
Modelling
- 4
Week 4
Modelling (cont)
- 5
Week 5
Possible deployment into API
- 6
Week 6
Visualisation and publication
- 7
Week 7
Visualization and publication (cont.)
- 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