sdas1983/3.Algerian-Forest-Fires-Dataset-EDA

# 3.Algerian Forest Fires Dataset Exploratory Data Analysis (EDA) This project explores the Algerian Forest Fires dataset, performing data preprocessing, exploratory data analysis (EDA), and implementing a Logistic Regression model to predict forest fire occurrences. ## Dataset Overview The dataset contains data related to forest fires in two regions of Algeria: Bejaia and Sidi-Bel Abbes. The dataset includes various meteorological and fire weather indices, along with a class label indicating the occurrence of fire. ## Project Structure - **Data Preprocessing** - Load and clean the dataset. - Handle missing values and incorrect data. - Convert data types as needed. - Encode categorical variables. - **Exploratory Data Analysis (EDA)** - Visualize the distribution of classes (fire/no fire). - Analyze the data by month and region. - Explore relationships between variables using boxplots, count plots, and correlation heatmaps. - **Model Building** - Prepare the data for modeling. - Implement Logistic Regression for fire prediction. - Evaluate the model using various metrics (accuracy, precision, F1-score). - Visualize the model's performance with confusion matrix and ROC curve. ## Data Preprocessing 1. **Loading the Dataset**: The dataset is loaded from a CSV file and inspected for any missing values or erroneous data. 2. **Region Assignment**: The dataset is divided into two regions: Bejaia and Sidi-Bel Abbes. 3. **Data Cleaning**: Invalid rows are removed, and columns are stripped of any leading or trailing spaces. 4. **Type Conversion**: Necessary columns are converted to appropriate data types (e.g., `int`, `float`). ## Exploratory Data Analysis (EDA) 1. **Boxplots**: Visualize the distribution of continuous variables such as Temperature, Rain, and various fire weather indices. 2. **Class Distribution**: Analyze the distribution of fire/no fire classes across the dataset. 3. **Month-wise Analysis**: Explore the occurrence of forest fires by month and …