Scholarship Eligibility Prediction

-20%

Scholarship Eligibility Prediction

0 Orders 0 Wish listed

₹4,999.00

Qty

Total price:

₹4,999.00

Overview
Reviews

Detail Description

Abstract

Scholarship Eligibility Prediction is a machine learning-based system designed to predict whether a student may meet the eligibility criteria for a scholarship based on academic performance, family income, attendance, category, achievements, extracurricular activities, and other relevant factors. The system analyzes historical scholarship application data and applies classification algorithms to provide an eligibility prediction. It can help students understand relevant eligibility factors and assist institutions in efficiently screening scholarship applications according to defined criteria.

Objectives

Develop a machine learning model for scholarship eligibility prediction.
Analyze academic, financial, and student-related factors.
Identify important factors associated with scholarship eligibility.
Preprocess scholarship application data.
Train and evaluate classification algorithms.
Predict eligibility based on applicant information.
Reduce manual effort in preliminary application screening.
Support institutions in scholarship application analysis.

Existing System

Traditional scholarship eligibility assessment is generally performed by manually checking student applications against predefined scholarship requirements.

Disadvantages:

Manual verification can be time-consuming.
Large numbers of applications are difficult to process efficiently.
Information from different factors may be difficult to analyze together.
Preliminary screening can require significant administrative effort.
Historical application data may not be fully utilized for analytics.

Proposed System

The proposed system uses machine learning to analyze scholarship application data and predict whether an applicant meets the eligibility criteria defined for the dataset. Features may include academic scores, family income, attendance, achievements, course details, application information, and other scholarship-specific criteria.

Algorithms such as Logistic Regression, Decision Tree, Random Forest, KNN, SVM, and Gradient Boosting can be trained and evaluated. A Streamlit or Flask interface can allow authorized users to enter applicant information and obtain a prediction.

The prediction should be treated as a preliminary analytical result; actual scholarship eligibility should always be determined using the official scholarship rules and verification process.

Implementation Procedure

Collect historical scholarship application data.
Clean missing, duplicate, and inconsistent records.
Encode categorical variables and prepare numerical features.
Perform exploratory data analysis.
Select relevant features.
Split the dataset into training and testing sets.
Train multiple classification algorithms.
Evaluate models using accuracy, precision, recall, F1-score, and confusion matrix.
Select and save the appropriate model.
Develop a Streamlit/Flask prediction interface.
Test the system using sample applicant records.
Deploy the application for authorized educational use.

Software Requirements

Python 3.x
VS Code / PyCharm / Jupyter Notebook
Pandas
NumPy
Scikit-learn
Matplotlib / Seaborn / Plotly
Streamlit / Flask
Joblib / Pickle
SQLite / MySQL (optional)
Git / GitHub
Google Chrome / Microsoft Edge

Hardware Requirements

Intel Core i3 or equivalent
4 GB RAM minimum, 8 GB recommended
10 GB available storage
1366 × 768 or higher display
Keyboard and mouse
Internet connection
GPU not required for basic implementation

Advantages

Provides quick preliminary eligibility predictions.
Reduces manual effort in initial screening.
Analyzes multiple applicant factors together.
Supports scholarship application analytics.
Can process large datasets.
Helps identify factors associated with eligibility.
Can be integrated into educational portals.
Can be extended to support multiple scholarship programs.

No review given yet!

Fast Delivery all across the country

Safe Payment

7 Days Return Policy

100% Authentic Products

Shopping cart