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.

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.

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

Dead Simple Result Analysis for VTU Engineering Students

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.

Regression Analysis using dataset from different Industries

Project for VTU result analysis, extraction and visualisations.

This project predicts students' math scores using machine learning and Flask for real-time predictions.

A comprehensive web-based Student Performance Monitoring System designed for academic institutions to track and analyze student academic performance. Faculty can record grades and academic data in real-time, while administrators gain valuable insights through data visualization to make informed decisions for improving student outcomes.

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)

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