Algerian_Forest_Fire_Linear_Regression

Title: Algerian Forest Fire Temperature Prediction using Linear Regression and Regularization Techniques

Introduction:

In this project, we aim to predict the temperature using Algerian forest fire dataset. We implemented four different regression models including Linear Regression, Ridge Regression, Lasso Regression, and Elastic-Net Regression to find the best fit for our data.

Problem Statement:

The Algerian forest fire dataset contains data on various attributes like temperature, humidity, wind speed, and more. Our goal is to use this dataset to predict the temperature based on the other available features. We chose this problem because predicting temperature can be a useful tool for firefighters to plan and execute their firefighting strategies.

Methodology:

We began by exploring the dataset and performing some data preprocessing to clean and prepare the data for the regression models. We then implemented the four different regression models to predict the temperature. Linear Regression served as our baseline model, and we then used regularization techniques to improve our model performance.

Results:

Our analysis showed that the Elastic-Net Regression model provided the best fit for the data, with the lowest mean squared error and highest R-squared accuracy. We also presented the results for the other regression models and compared their performance to the Elastic-Net model.

Conclusion:

In this project, we successfully implemented various regression models to predict the temperature using the Algerian forest fire dataset. Our results showed that the Elastic-Net Regression model outperformed the other models. We believe that our findings can be useful for firefighters in their efforts to combat forest fires, and we hope that this project inspires further research in this area.