kandelsatish/Algerian_forest_fire

## Algerian Forest Fire Dataset Analysis ### Overview This project focuses on analyzing the Algerian Forest Fire dataset to predict the burned area using regression models. The analysis involves data cleaning, feature engineering, model development, and evaluation. The following machine learning techniques are used: Multiple Linear Regression, Polynomial Regression, and Regularization (Lasso and Ridge). ### Contents * Dataset Overview * Data Preprocessing * Data Visualization * Model Development * Model Evaluation * Requirements * Results ### Dataset Overview The dataset contains meteorological and forest fire data such as temperature, humidity, wind speed, and rainfall. The goal is to use these features to predict the extent of the burned area. ### Data Preprocessing * Handling Missing Values: Missing values are identified and filled using appropriate imputation techniques (e.g., mean substitution). * Outlier Treatment: Box plots and statistical methods are employed to detect and handle outliers. * Feature Selection and Engineering: Relevant features such as Temperature, Humidity, Wind Speed, and Rain are selected. Interaction terms and polynomial features are created to capture complex relationships. ### Data Visualization Exploratory Data Analysis (EDA) is conducted using visualizations to understand the correlations and patterns within the dataset: * Pair Plots: Show the relationships between features and the target variable (Burned Area). * Histograms and Scatter Plots: Highlight the distributions and dependencies of the data. Observations provide insights into the dynamics influencing forest fires. ### Model Development * Multiple Linear Regression: A baseline model is built using selected features. * Polynomial Regression: Polynomial features are added to capture non-linear relationships. * Regularization Models (Lasso & Ridge): Ridge and Lasso regression models are applied to reduce overfitting and optimize the model's performance. Hyperparameter t …