This project analyses and correlates student performance with different attributes. Then at last, it determines most suitable algorithm from bunch of them.

The Exploratory Data Analysis and Machine Learning Model Training for the Student Performance Data

AI-powered EduPredict: A student performance and analytics system using Machine Learning and Streamlit. Predicts outcomes, identifies risk factors, and provides actionable insights for educators.

This is our Mini Project for 6th semester. In this Mini Project we are developing a new webapp in which we will be performing data visualisation, dashboard designing web development using HTML5,CSS, JavaScript for web development. We are also using tools like Power BI or Tabelue for visualisation purpose.

Utilizes Pandas, Matplotlib, and NumPy to analyze grades, subjects, and study habits. Gain insights into academic performance through data analysis and visualization.

A Machine Learning-powered platform to predict student performance and provide actionable insights for teachers. Built with Django, React, and Scikit-learn.

This dashboard represents an analysis on student performance in math, reading, and writing examinations where more than one factor has been taken into consideration. in accordance with #InternIntelligence.

Predicting student academic performance using a machine learning approach. This project leverages the Random Forest Classifier algorithm.

Thesis Paper & Documentation about Student Performance Prediction with Explainable AI & Fairness

The primary objective of this project is to develop a predictive model that can forecast the performance of students in their academic projects. The model aims to help educators and institutions identify students who may need additional support or intervention early in the project development process, ultimately enhancing overall student success.

Analyzes student behavior patterns to understand their impact on academic performance. Provides clear visual insights and correlations from real data. Supports early prediction and decision-making for improving student outcomes.

Taking part in Kaggle challenges or simply picking random datasets and working on them

To understand and predict how the student's performance (test scores) is affected by the other variables (Gender, Ethnicity, Parental level of education, Lunch, Test preparation course).

Dead Simple Result Analysis for VTU Engineering Students

Student performance dashboard using Python & Streamlit

This project performs Exploratory Data Analysis (EDA) and hypothesis testing on student performance data. It explores trends based on attributes like gender, race/ethnicity, parental education, lunch type, and test preparation course completion.

No description provided.

A Flask web application that processes student test data and generates AI-powered personalized PDF reports with performance analytics, visualizations, and actionable recommendations using Google Gemini API.

Project for VTU result analysis, extraction and visualisations.

Machine learning model to predict student grades using academic and personal features

SQL script to answer the past "365 Learning Data Challenge"

A data-driven analysis of student academic performance using Python. Includes data cleaning, feature engineering, and insightful visualizations to uncover factors affecting exam scores.

various machine learning challenges

Analyzed student exam performance using descriptive & inferential statistics. Explored effects of gender, parental education, lunch type & test prep on Math, Reading & Writing scores. Used Python, Pandas, Seaborn, SciPy. Found strong score correlations & significant prep course impact.

Estudo comparativo de classificação multiclasse (OvA vs OvO) utilizando Regressão Logística para prever o desempenho acadêmico de estudantes. Alcançou 94.23% de acurácia com a estratégia One-Vs-One.

Analysis of Students and Parents Datasets for Bias mitigation and Fairness

PCA-based clustering of student grades to explore academic performance patterns (R)

Analyzing and visualizing student performance using Python, Pandas, and Matplotlib.

This Master thesis presents a Learning Analytics (LA) study conducted on RETOMadrID. The goal is to improve the platform by understanding students’ behavior using modern machine learning (ML) and data analysis techniques.

Regression Analysis using dataset from different Industries