Machine learning-based application for predicting the injury recovery time period a sports person based on injury type and diet plan.

Training of a neural network for nonlinear regression prediction with TensorFlow and Keras API.

Models Supported: VGG11, VGG13, VGG16, VGG16_v2, VGG19 (1D and 2D versions with DEMO for Classification and Regression).

A NOVEL BLIND IMAGE QUALITY ASSESSMENT METHOD BASED ON REFINED NATURAL SCENE STATISTICS

A system and web app to discover good deals of rental properties, built and automated on a serverless architecture.

This project aims to develop a machine learning model that predicts the demand for bike sharing in a given location. By analyzing historical data on weather conditions, day of the week, and other factors, we aim to create a model that can accurately forecast the number of bikes that will be rented at different times.

This repo covers the basic machine learning regression projects/problems using various machine learning regression techniques and MLP Neural Network regressor through scikit learn library

Detecting the functioning level of a patient from a free-text clinical note in Dutch.

The objective of this project is to study the COVID-19 outbreak using basic statistical techniques and make short term predictions using ML regression methods.

Long Term Weather Forecast App

A machine learning approach to the inverse design of microstrip patch antennas by predicting optimal physical dimensions from desired performance metrics.

In this project I have implemented 14 different types of regression algorithms including Linear Regression, KNN Regressor, Decision Tree Regressor, RandomForest Regressor, XGBoost, CatBoost., LightGBM, etc. Along with it I have also performed Hyper Paramter Optimization & Cross Validation.

This repository documents a complete ML workflow to model Uber fares in Paris, from granular EDA and feature engineering to building and fine-tuning a stacking regressor on 10k real-world rides.

Designed Vanilla RNN, LSTM and GRU models to predict the state of charge of a lithium-ion battery used in an EV

This repository showcases a machine learning project that leverages PyTorch to implement a linear regression model for predicting house prices in Boston. It uses the well-known Boston Housing Dataset, incorporating a complete pipeline from data preprocessing and loading to model training, evaluation, and result visualization.

This project focuses on developing a machine learning model to predict the price of diamonds based on various attributes. By analyzing a dataset that includes information about the carat weight, cut, color, clarity, and other factors, we aim to create a model that can accurately estimate the price of diamonds.

An end-to-end MLOps project demonstrating a modular machine learning pipeline for predicting student performance, featuring a Flask web interface and deployment on AWS.

Previsão de vendas de uma rede de farmácias.

Predict health insurance costs using a linear regression model built with Python. This project trains and evaluates a regression model on real healthcare data to estimate insurance expenses based on demographic and health features. It’s part of a machine-learning project inspired by the freeCodeCamp Linear Regression Health Costs Calculator challen