There are 2 repositories under regression-model topic.
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).
Machine learning-based application for predicting the injury recovery time period a sports person based on injury type and diet plan.
A NOVEL BLIND IMAGE QUALITY ASSESSMENT METHOD BASED ON REFINED NATURAL SCENE STATISTICS
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.
Machine learning projects to showcase applications of ML in various industries/disciplines/fields
Detecting the functioning level of a patient from a free-text clinical note in Dutch.
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
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 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.
Summer Training on Machine Learning by Internshala, powered by Analytics Vidhya,
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
Previsão de vendas de uma rede de farmácias.
prettyglm provides a set of functions which can easily create beautiful coefficient summaries which can readily be shared and explained.
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.
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 repo is maintained for a web app that lets you predict 'value' of an IPL player based on some player statistics.
a npm package to compute linear regression in javascript
This project sketches out an entire business plan for designing and launching Automated Predictive Maintenance System from scratch
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.
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".
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.
An app allowing you to predict the best possible time possible for a game speedrun.
CSCI 4371: Machine Learning - Final Project
RGB value regression model using tensorflow
Student Project at HFU university for course Data Science and Machine Learning, Summer semester 2023
For a real estate firm, building a house price prediction model based upon various factors. Problem - Regression | Algorithm used -Linear Regression using OLS