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

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

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.

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.

Dead Simple Result Analysis for VTU Engineering Students

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

Regression Analysis using dataset from different Industries

Project for VTU result analysis, extraction and visualisations.

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.

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

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.

Developed an end-to-end machine learning project using Docker and AWS, and implemented an industrial-grade code with modular architecture. The project focused on student performance prediction, achieving high accuracy through various machine learning algorithms.

A Machine Learning project to predict student academic performance using regression and classification models. Includes data preprocessing, visualization, model training, and evaluation on real-world student data.

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

Analysis of Students and Parents Datasets for Bias mitigation and Fairness

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

Using regression analysis, we tested the significance of predictors (such as failures and travel time) to see if they influence the final grades of a student.

We proposed an automated student result analysis system utilizing ASP.NET to streamline grading analysis and manage student performance effectively. This system addresses the challenges posed by manual analysis in today's education landscape, offering a comprehensive platform for evaluating learning outcomes and optimizing institutional effectively