The document outlines a project by the government of Gujarat aimed at analyzing student dropout rates in schools through a web portal where schools can upload student data. The system will employ a machine learning model to generate insights based on various categories such as school, age, and socio-economic factors, helping the government to devise targeted interventions. Key stakeholders include schools and the government, with a focus on ensuring structured data upload and necessary technical support for implementation.

1. Basic Details of the Team and
Problem Statement
Ministry/Organization Name/Student Innovation:
Government of Gujrat (Smart Education).
PS Code: SIH1362
Problem Statement Title: Student dropout analysis for
school education
Team Name: Solutioneers
Team Leader Name: Sajal Kesharwani
Institute Code (AISHE): 0301
Institute Name: Rewa Engineering College, Rewa (M.P.)
Theme Name: Software

2. Idea/Approach Details
 A web portal through which schools could upload
the student data and an ML model to display the
output as the student dropout analysis based on
different categories.
⮚ The Government and the common citizens must
register on the portal first through proper
authentication system whereas schools will be
registered by the application developer
⮚ The schools will be provided with necessary
credentials required to log in to the portal during the
registration
⮚ Major stakeholders : Schools & Government
⮚ Unique feature : Help Desk to register the issues by
the students or schools on a personal level
1. Frontend development:
HTML, CSS, JS
2. Backend development:
Node.JS, Database, RestAPI
3. Machine Learning Model
Scikit-Learn, Tensorflow, Matplotlib, Numpy
Technology stack:
Process Flowchart:

3. Idea/Approach Details
Use Cases:
Schools:
⮚ To discern the school position by
uploading the student data
⮚ To improve the school facilities wherever
required
Government:
 To view the current situation of different
schools, areas, caste, gender, age
regarding the drop out rate
 To frame necessary policies based on the
dropout analysis
Dependencies / Show stopper:
⮚ The unavailability of the necessary
requirements could be the potential show
stopper
⮚ We will be requiring the schools to upload the
structured student data without which the
analysis could not be done
⮚ The development of the application is not
possible without some technical knowledge
such as website development, database
management, application integration, data
analysis and machine learning concepts
⮚ Government support is much needed during
the deployment of the application and to ask
the schools to upload their data

4. Team Member Details
Team Leader Name: Sajal Kesharwani
Branch : BTech Stream : ECE Year : IV
Team Member 1 Name: Ritik Trivedi
Branch : BTech Stream : ECE Year : IV
Team Member 2 Name: Sachi Dhaturaha
Branch : BTech Stream : ECE Year : IV
Team Member 3 Name: Shiksha Dwivedi
Branch : BTech Stream : ECE Year : IV
Team Member 4 Name: Harsh Mishra
Branch : BTech Stream : ECE Year : IV
Team Member 5 Name: Shriya Chaturvedi
Branch : BTech Stream : ECE Year : IV
Team Mentor 1 Name: Samiksha Singh
Category (Academic/Industry): Expertise (VlSI , Microelectonics): Domain Experience (in years):
Team Mentor 2 Name: Type Your Name Here
Category (Academic/Industry): Expertise (AI/ML/Blockchain etc): Domain Experience (in years):

5. Important Pointers
Problem Statement
Right to education is key concern for government and at school level; drop out ratio is high due to poverty and
social, economic reasons. If government have drop out student analysis on following different categories, it will be
very useful in framing different policies. 1. School wise 2. Area wise 3. Gender wise 4. Caste wise 5. Age/standard
wise Expected Output: Focused interventions on the high dropout rates
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