There are 3 repositories under decision-tree-regression topic.
Learning to create Machine Learning Algorithms
Implementation of basic ML algorithms from scratch in python...
ApnaAnaaj aims to solve crop value prediction problem in an efficient way to ensure the guaranteed benefits to the poor farmers. The team decided to use Machine Learning techniques on various data to came out with better solution. This solution uses Decision Tree Regression technique to predict the crop value using the data trained from authentic datasets of Annual Rainfall, WPI Index for about the previous 10 years. This implementation proved to be promising with 93-95% accuracy.
This repository will contain all the stuffs required for beginners in ML and DL do follow and star this repo for regular updates
In this project we are comparing various regression models to find which model works better for predicting the AQI (Air Quality Index).
Machine Learning Software that predicts planets based on their distance from the sun, number of satellites and various properties
Compared different classification and regreesion models performance in scikit-learn by applying them on 20 datasets from UCL website.
Introduction to XGBoost with an Implementation in an iOS Application
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.
Natural Gas Price Prediction System Using IBM Watson services
This is repository about the MachineLaering Basics including all the Machine learning Algorithms
Implements Decision tree classification and regression algorithm from scratch in Python.
All my Machine Learning Projects from A to Z in (Python & R)
Implementation of a 1D Decision Tree Regression model in python.
Weather Prediction iOS Application Using Flask API and AI
Machine Learning algorithms implementation using Python
Academic project for Advances in Data Science and Architecture course
Implementing a music recommender with decision tree.
This repository contains the files and instructions on using Amazon SageMaker to build linear regression, polynomial regression etc to predict the temperature
There is an intense transfer speculation that surrounds all major player transfers today. An important part of negotiations is predicting the fair market price for a player. Therefore, we are predicting this Market Value of a player using the data provided in csv format.
The Korea National Oil Corporation was interested in purchasing shale gas wells from the United States and wanted to predict their production to select wells that maximize profit.
SegmentaciĂłn EnergĂa usando dataset REDD y varios algoritmos incluidos Neural Network
A ReactJS based dashboard predicting crop prices using Decision Tree Regression Algorithm.
Los árboles de decisión son uno de los algoritmos clásicos de machine learning ya que nos ayudan a visualizar las predicciones hechas por nuestro modelo. En este tutorial vemos su uso para regresiones lineares y clasificación, asà como herramientas de ensamble como bagging y boosting.
Building BigMart Sales Prediction
This project analyzes the impact of the broadband and device digital divide upon educational assessment outcomes for math and reading/language arts in K-12 public School Districts across the United States. Completed as a Capstone project for graduation from Flatiron School.
Simple example demonstrating Regression using Decision Trees
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
Machine Learning-based research for UPMC
A regression based modeling project to forecast the sales of Walmart
Prediction of car prices using data from sahibinden.com
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 involves the prediction of house prices in Bengaluru city using Decision Tree Regression in Jupyter Notebook. Through this analysis, we aim to build a regression model that accurately predicts house prices based on the given input features.
The task is to build a machine learning regression model will predict the number of absent hours. As Employee absenteeism is a major problem faced by every employer which eventually lead to the backlogs, piling of the work, delay in deploying the project and can have a major effect on company finances. The aim of this project is to find an issue which eventually leads toward the absence of an employee and provide a proper solution to reduce the absenteeism
Analytical tool to help the company decide whether the employee will stay or not
This model predict the salary of faculty based on their profile