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

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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.

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 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.

Previsão de vendas de uma rede de farmácias.

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.

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.

Machine learning projects to showcase applications of ML in various industries/disciplines/fields

Long Term Weather Forecast App

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

Summer Training on Machine Learning by Internshala, powered by Analytics Vidhya,

Creating an ML model to predict the estimated time of arrival at the dropoff point for a single journey on a ride-hailing app.

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

Data Science project on Cab Fare Prediction, Machine learning algorithms are used to develop a regression model. Problem Statement : The project is about a cab company who has done its pilot project and now they are looking to predict the fare for their future transactional cases. As, nowadays there are number of cab companies like Uber, Ola, Meru Cabs etc. And these cab companies deliver services to lakhs of customers daily. Now it becomes really important to manage their data properly to come up with new business ideas to get best results. In this case, earn most revenues. So, it becomes really important estimate the fare prices accurately.

This project uses machine learning to predict the price of a used car. The model is trained on a dataset of historical car sales data, and it can then be used to predict the price of a car based on its features.

prettyglm provides a set of functions which can easily create beautiful coefficient summaries which can readily be shared and explained.

This project is a study that performs statistical regression analysis for a car buying, selling, and rental company and predicts the total revenue using multiple linear regression based on the analysis

Global video game sales prediction from year 2008 to 2014 approximately using linear regression and decision tree regression with manipulating min_sample_split hyperparameter to achieve higher accuracy /lower overfitting

The purpose of this work is the modeling of the wine preferences by physicochemical properties. Such model is useful to support the oenologist wine tasting evaluations, improve and speed-up the wine production. A Neural Network was trained using Tensorflow, which was later tuned in order to achieve high-accuracy quality predictions.

CSCI 4371: Machine Learning - Final Project

This is the experiment code for the publication "Identifying Informative Nodes in Attributed Spatial Sensor Networks using Attention for Symbolic Abstraction in a GNN-based Modeling Approach".

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.