ML---Algerian-Forest-Fire

## Introduction * I used a dataset on **Algerian Forest Fires from UCI**. The dataset contains a culmination of forest fire observations and data in two regions of Algeria: the Bejaia region and the Sidi Bel-Abbes region. * The timeline of this dataset is from June 2012 to September 2012. In this project, we focused on whether certain weather features could predict forest fires in these regions using few Machine Learning algorithms

EDA

• In this step, we will apply Exploratory Data Analysis (EDA) to extract insights from the data set to know which features have contributed more in predicting Forest fire by performing Data Analysis using Pandas and Data visualization using Matplotlib & Seaborn.
• It is always a good practice to understand the data first and try to gather as many insights from it.

Model Building

• For Regression Problem algorithm decided to predict the feature FWI (Fire weather Index) which is 90%+ correlated to Classes Feature.
• Models used : Linear regression, Lasso Regression, Ridge Regression
• For Classification algorithm decided to predict the features Classes from the dataset which is Binary classification (fire, not fire).
• Models used : Logistic Regression

Technologies used

Tools used

[][postman]