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.
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, así como herramientas de ensamble como bagging y boosting.
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.
The Main Purpose of this project is to predict the closing point of a particular stock. This project involves two supervised learning algorithms i.e. multiple linear regression and decision tree regressor. Both of them have different accuracy score. I got 99.9 accuracy by using multiple linear regression and 97% by using decision tree regressor
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
