afislonge/Algerian-forest-fire-ML

This repository contains the analysis and modeling of the Algerian Forest Fires dataset. The project includes data cleaning, feature engineering, model building, and evaluation. # Algerian Forest Fires Analysis This repository contains the analysis and modeling of the Algerian Forest Fires dataset. The project includes data cleaning, feature engineering, model building, and evaluation. ## Table of Contents 1. Effective Handling of Errors 2. Appropriate Feature Selection and Engineering 3. Creation of Insightful Visualizations 4. Clear and Meaningful Observations 5. Implementation of Multiple Linear Regression and Polynomial Regression Models 6. Application of Regularization Techniques 7. Effective Use of Cross-Validation and Hyperparameter Tuning 8. Comprehensive Evaluation of Model Performance 9. Testing the Model on Unseen Data ## 1. Effective Handling of Errors ### Overview This section focuses on ensuring the dataset is clean and free from errors such as missing values, duplicates, and incorrect data types. Proper handling of these errors is crucial for building reliable models. ### Steps: 1. **Loading the Dataset:** We load the dataset using pandas. 2. **Checking for Missing Values:** We identify any missing values and decide to drop or impute them based on the situation. 3. **Handling Duplicates:** We remove any duplicate records from the dataset. 4. **Data Type Conversion:** Ensure the data types are appropriate, especially for date columns. 5. **Outlier Detection and Removal:** Outliers are identified and handled using the IQR method to prevent skewing the analysis. ## 2. Appropriate Feature Selection and Engineering ### Overview Feature selection and engineering are critical steps in the data science process. This section involves selecting the most relevant features and creating new ones to enhance model performance. ### Steps: 1. **Correlation Analysis:** We analyze the correlation matrix to identify features with the strongest relationships to the target variable. 2. **Feature Selection:** Based on the correlation, we select the most impactful features. 3. **Feature Engineering:** We create new features through tran …