YounesDjemmal/Algeria-Forest-Fires-ML-R

# Algeria Forest Fires This project is a part of the data mining course at the Lebanese American Unviersity under the supervision of Dr.Joseph Rebehmed. #### Project Status: [Completed] ## Background During the summer of 2012, Algeria experienced a series of devastating wild fires that affected much of the northern part of the country, particularly the coastal cities. These fires were likely caused by higher than average temperatures, which reached as high as 50 degrees Celsius. In order to prevent the reproduction of such disasters, it is important to be able to predict their occurrence. ## Project Intro/Objective The main objective of this project is to predict the occurrence of forest fires in Algeria during the summer of 2012 based on multiple features related to weather indices. By using advanced machine learning techniques and algorithms, we aim to develop a model that can accurately predict forest fire occurrences and help prevent future disasters. ### Partner * [Rissal Hedna] * github.com ### Tools To achieve our objectives, we will use a variety of tools and algorithms. These include support vector machines, generalized least squares, random forests, and generalized boosted models. We will also use a variety of libraries, such as e1071, MASS, plyr, caret, ROCR, pROC, randomForest, gbm, dplyr, and tidyverse, to perform our analysis and make predictions. Additionally, we will use the caTools library to split our dataset into train and test sets. ### Technologies * R * HTML