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

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.

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.

Dead Simple Result Analysis for VTU Engineering Students

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).

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.

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.

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

Project for VTU result analysis, extraction and visualisations.

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.

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.

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.

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.

Predict student total scores based on activity scores using Linear Regression. The model improves as more activities are considered, achieving near-perfect accuracy with 12 activities. Includes data preprocessing, training, evaluation, and interactive visualizations

A machine learning project aimed at predicting student performance using various ML algorithms. Features data preprocessing, model training, and evaluation. Ideal for educational data analysis and academic research.

ECS8050 – Foundations of AI coursework project. Student performance prediction using statistical analysis, SVD, optimisation, and Markov Decision Processes on the UCI Student Performance dataset.