This document outlines the use of graph databases and Neo4j to model student data. It discusses how graph databases store connected data through nodes and relationships. It then demonstrates how to import student assessment data into Neo4j and create nodes and relationships to link assessments to students, courses to students based on their enrollments, and classify students' skills levels. The document provides an example of different queries that can be run on the graph database to analyze the student data.

1. Graph
databases
Neo4j
Presented by :Habiba Abderrahim

2. OUTLINE
Data
Imputation
Queries
Conclusion
Mean/Mode/Median
Regression imputation
KNN Imputation
INTRODUCTION
01 02
04
Example
Neo4j
03
2

3. Graph
databases
01
3

4. Graph Databases
Type of NoSQL database
Model their data using nodes and
edges that form a “graph”
Handling connected data
HOW?
Where?
what?
4
Graph databases are focused on
relationships and store already
connected data !

5. 5
Main Focus

6. 6
Relationships
● Relational data stores can’t describe
relationships other than standard one-to-
one, one-to-many, and many-to-many.
● Associate data with each other through
references (just like foreign keys in the
relational model)

7. Social Network exemple
7
Label
Relationships

8. Use Case
“Student and
E-Learning “
02
8
On
NEO4J

9. Data
imputation
01
9

10. Data Imputation using python
10
Original Data
#df.isnull().sum()

11. 11
Inject Missing values For test purpose
test.loc[test['sk4_classroom'] == '10','sk4_classroom'] = np.nan
Before After
test.loc[test['sk5_classroom'] == '5','sk5_classroom'] = np.nan

12. 12
test['sk5_classroom']= [float(str(i).replace(",", "")) for i in test['sk5_classroom']]
From To
Convert Data Type

13. Data Imputation technique
13
Mean/Mode/Median
Linear imputation
Knn imputation

14. Regression imputation
14
1- Separate Missing Values from Data and
consider as testdata
2- Drop nan values from data and consider it
as Train
3-create xtain , ytain from data and build LR
Model
4-create x_test of the test data
5-apply the Model on X_test and make
predictions
6-Replace Missing values with Predicted
value
A B C
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0.5
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NaN
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NaN
A B C
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0.5
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77
20
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A B C
12
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0.5
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NaN
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NaN
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NaN
X_Train y_Train
Data without
null values
fit predict
STEPS

15. Model imputation : Linear imputation
15
● Train data that do not contain null values
Linear Regression
● Fit the model and predict Missing values
separated data that contains rows
where sk4 (y) is NaN
● Replace Missing values with the predicted ones

16. Mean/Mode/Median
16
test['sk4_classroom'].fillna(test['sk4_classroom'].mean(),inplace=True)

17. 17
Model Imputation: KNN Imputation

18. Neo4j
02
18

19. Import csv DataSet
19
1- Put csv data in import directory in order to use them on Neo4j :
C:Usersheubu.Neo4jDesktoprelate-
datadbmssdbms-322d6c94-76fe-474a-9a5c-
5726497cec55
Settings :

20. Student Assessments
20
LOAD CSV WITH HEADERS FROM "file:///studentAssessment.csv"
as a CREATE (StudentAssessments:StudentAssessment{id_assessment:toInteger(a.id_assessment)
,id_student:toInteger(a.id_student) , date_submitted:toInteger(a.date_submitted) ,
is_banked :toInteger(a.is_banked) , score:toFloat(a.score) })
verify node number using python :
creating a node with the Student Assessment label and properties for each row in
the studentInfo.csv file.

21. 21
MATCH (n:StudentAssessment) RETURN n LIMIT 25

22. submitted relationship
22

23. ENRolled IN
23
MATCH (s:Student), (c:Course)
WHERE s.code_module = c.code_module AND s.code_presentation =
c.code_presentation
CREATE (s)-[:ENROLLED_IN]->(c)
Here the code_module and code_presentation properties are being used to link
:Student nodes and :Course nodes together.
These properties are being used as common keys that are shared by both
node types.

24. REGISTRED
24
MATCH (s:Registration), (c:Course)
WHERE s.code_module = c.code_module AND
s.code_presentation = c.code_presentation
CREATE (s)-[:REGISTRED]->(c)

25. Distinct Reactions
25

26. Highest Education :
26
Different diplomas of the students who are
enrolled in the Course node.

27. Classify the skills of the
online_classroom
27
MATCH (oc:online_classroom)
SET oc.sk1_level =
CASE
WHEN oc.sk1_classroom <= 3
THEN 'low'
WHEN oc.sk1_classroom <= 7
THEN 'medium'
ELSE 'high'
END

28. Classify the students according to their level of
competence in different skills
28
MATCH (s:Student), (oc:online_classroom)
WHERE s.id_student IN oc.students
CREATE (s)-[r:PARTICIPATED_IN
{skill_level:
CASE oc.sk2
WHEN oc.sk2 <= 3 THEN 'low'
WHEN oc.sk2 <= 7 THEN 'medium'
ELSE 'high'
END
}]->(oc)
1. match all pairs of Student and
online_classroom nodes in the
database.
1. Filters the pairs of nodes by checking
if the id_student property of the
Student node is in the students list
of the online_classroom node.
1. The CASE statement tests the value
of the sk1 property and assigns one
of the labels low, medium, or high to
the skill_level property based on the
range

29. All the Queries here :
29
https://docs.google.com/document/d/1LRb50MtVFPfHllB2WIevb1aOECuM74ZCiPRM
LK5sjys/edit?usp=sharing

30. Conclusion
05
30

31. Thanks!
Do you have any questions?
31