University Admission Predictor is a sophisticated Flask-based web application designed to predict the likelihood of admission to graduate programs based on student profiles. It leverages a range of regression techniques to evaluate admission chances.This project showcases the practical application of machine learning in educational forecasting.

The purpose of this project is to predict student loan repayment success using a neural network. Neural networks are computational models inspired by the human brain's structure and function, consisting of layers of interconnected nodes or "neurons" that can learn to recognize patterns in data.

Taxi Fare predictor using Random Forest and GridSearchCV for Hyperparameter Tuning

The enhancement of Intelligent Transport Systems (ITS) involves the precise prediction of bike-trip durations, incorporating a comprehensive consideration of Seoul's weather conditions.

Simple ML system for the iris problem

Successfully established a supervised machine learning model that can accurately predict whether the travel insurance claim of a particular customer should be approved or not by a travel insurance agency.

Successfully developed a machine learning model which can accurately predict up to 100% accuracy whether a credit card application of a given applicant would be approved or not, based on several demographic features such as applicant age, total income, marital status, total years of work experience, etc.

Comment classifier model trainer using keras tensorflow, stanza tokenizer and transformers.

Successfully developed a machine learning model which can accurately predict the strength of cement based on various features such as blast furnace slag, water, coarse aggregate, etc.

Successfully established a machine learning model to accurately predict the price of a flight in India based on several features such as duration, days left, arrival time, departure time and so on.

Successfully fine-tuned a pretrained DistilBERT transformer model that can classify social media text data into one of 4 cyberbullying labels i.e. ethnicity/race, gender/sexual, religion and not cyberbullying with a remarkable accuracy of 99%.

Nonprofit foundation Alphabet Soup wants a tool that can help it select the applicants for funding with the best chance of success in their ventures. Using machine learning and neural networks, you’ll use the features in the provided dataset to create a binary classifier that can predict whether applicants will be successful if funded.

Successfully established a machine learning model that can accurately predict the sales of a superstore based on various features such as quantity, profit, discount, postal code, etc. The features are mainly associated with order details and customer demographics.

Deployment ready machine learning model to predict the math scores of students based on various features related to their demographics, background, and academic engagement.

Different types of regression algorithm

The nonprofit foundation Alphabet Soup wants a tool that can help it select the applicants for funding with the best chance of success in their ventures

this computer vision / machine learning project uses YOLO to detect players, referees, and the ball, k-means for pixel segmentation (and to group players by their teams), and optical flow for motion tracking.

This repository includes a report about implementing a Linear Regression model to predict house prices using square footage, number of bedrooms, and number of bathrooms. The model demonstrated reliable performance and successfully predicted house prices for new input data.

Gene expression analysis and classification using XGBoost and Differential Expression Analysis (DES)

Successfully established a machine learning model using PySpark which can precisely predict the energy consumption of the steel industry, up to an r2 score of approximately 99.5%.