Students don?t Analyse their semester progress properly and don?t know the actual Percentage of their achievement in ongoing semester, by using this system student can see their actual progress and able to know how much score he get. Our system have two main functions Subject wise Predic
Acedamic profile AI based prediction system
Students don’t Analyse their semester progress properly and don’t know the actual
Percentage of their achievement in ongoing semester, by using this system student can see
their actual progress and able to know how much score he get. Our system have two main
functions Subject wise Prediction In subject wise prediction, system predicts students final
score by considering mid-term score, Project Score, and Sessional score and Semester Wise
Prediction, system predict CGPA. Perdition has three level in our system good, average and
bad. If student data is analysed timely then definitely it will help the student to improve
their performance in academics. By periodically evaluating the student progress, timely
help and support are provided to those students who are on the risk of failure or dropout, so
that they can improve their performance in future.
In this project, we use machine learning algorithms to develop a model for analysing and
predicting the academic result based on student academic results. Proposed model will
guide students to achieve high excellence in subject or completion of degree with good
CGPA.
Students are able to predict their progress in ongoing semester.
? Students are able to enhance efforts according to the Prediction result.
? Students are able to identify that he/she can pick next core courses based on the
result of the ongoing subject.
? Students are able to increase their CGPA by getting Indication message from the
system.
In first step the data will be collecting from students via online survey form.
In second step we applying data pre-processing will be performed using python language
on Jupiter notebook
In third process exploratory data analysis will be performed to increase the
Understand ability of the data .with this step we can identify correlations in the datasets,
specify the features and selecting the most important characteristics.
Forth step is about data partitioning data will be dividing into two parts one will be used
for training and other for testing purpose.
The fifth step is about applying the machine learning algorithm, in this step different
machine learning algorithms will be applied on the data and with the help of confusion matrix the most accurate growth will be identified.
At the last, the objective is to improve the system performance we can apply more
effective algorithms and extending the size of data set in which the performance will be
re-evaluated to achieve the above objective.
We will mainly be focusing on python language and we will use relevant tools such as
WEKA, R-Programming, and MATLAB etc.
For web application we use flutter as frontend and for backend we use python language
and for database we will be using Microsoft SQL database
Flutter web
SQL
Python
MATLAB
Week a
Students don’t Analyse their semester progress properly and don’t know the actual
Percentage of their achievement in ongoing semester, by using this system student can
see their actual progress and able to know how much score he/she needs to get a good
score in finals.
If the student get good score throughout semester he will get motivated and put more
efforts for the finals and take more interest.
This model can guide slow learner to achieve the high score in subject
we are dividing our project into two phase
first phase we can working on documentation and analysis of data and front end
second phase we are trying to building the model and deployment of ML model on SAAS and then showing our result on front of screen of user
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| SAAS service for deployment of ML model on cloud,License software, Cloud Server, hosting services | Equipment | 1 | 39000 | 39000 |
| Total in (Rs) | 39000 |
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