AnkithRathor/Algerian-Fire-Forecaster

# **Algerian Fire Forecaster** **Author**: Ankith Rathor **Email**: ankithrathor126@gmail.com **Website**: datascienceportfol.io **LinkedIn**: (linkedin.com) ## # **Table of Contents** - Introduction - Objective - Data Collection - About the Dataset - Python libraries used - Project Work flow 1. Data Exploration 2. Data Wrangling 3. Exploratory Data Analysis 4. Model Building 5. Prediction - Limitations - Conclusion ## **Introduction** * 'Algerian Fire Forecaster' - A cutting-edge project leveraging Machine Learning and Python to predict wildfire risks in Algeria. * This project analyzes key factors like Temperature, Relative Humidity, Wind speed, Rain level,Fine Fuel Moisture Code, Duff Moisture Code, Drought Code, Initial Spread Index, Buildup Index and predicts Fire Weather Index. * By fusing meteorological data with machine learning, this project aims to provide timely insights for effective firefighting and emergency response, offering a proactive approach to wildfire management in Algeria. ## **Objective** * The objective of this project is to utilize Python and machine learning techniques for predicting the Fire Weather Index (FWI) in different locations across Algeria. ## **Data Collection** * The data for this project was collected from Kaggle, a popular platform for data science competitions and datasets. * Check out the raw dataset here * Check out the cleaned dataset here ## **About the Dataset** ### Data Set Information: - The dataset includes 244 instances that regroup a data of two regions of Algeria,namely the Bejaia region located in the northeast of Algeria and the Sidi Bel-abbes region located in the northwest of Algeria. - 122 instances for each region. - The period from June 2012 to September 2012. The dataset includes 11 attribues and 1 output attribue (class) The 244 instances have been classified into fire(138 classes) and not fire (106 classes) classes. ### At …