vaddhiparthy / forestfireestimation Goto Github PK

This project represents a challenging regression task that aims to predict the burned area of forest fires in the northeast region of Portugal, using a range of meteorological and other data.

The first step in this project was to conduct an in-depth analysis of the data, including an examination of the various attributes, statistical descriptions, and the presence of any null values. Skewness, kurtosis, and outliers were also considered and visualized. The correlation values were calculated to identify any potential relationships between the variables.

Once a thorough understanding of the data was established, the next step was to prepare the data for model development. This involved encoding the categorical variables, treatment of outliers, scaling the variables, and dividing the dataset into appropriate training and testing subsets. These data preparation steps were crucial for ensuring the validity and reliability of the models developed.

To make the predictions, linear and polynomial regression models were developed and fine-tuned using various techniques such as backward elimination, Ridge, Lasso, and ElasticNet regularization methods. The linear regression model was an excellent base model, but the Polynomial regression model helped to capture the non-linearity of the relationship between the predictors and the response variable. The results of the models were then compared to determine the best-performing model for the task at hand.

In conclusion, this project is a sophisticated and cutting-edge approach to the prediction of forest fire burned areas, utilizing advanced techniques for data understanding, preparation, and model development. The models developed in this project will be an invaluable tool for predicting and mitigating the impact of forest fires in the Northeast Region of Portugal.