Prafulghagare/ML-Analyzing-the-Best-Fit-Regression-Model-for-Forest-Fire-Prediction

This project focuses on predicting forest fire occurrences using various regression algorithms — Linear Regression, Ridge, Lasso, and ElasticNet — to identify the best-performing model. The dataset used is the Algerian Forest Fires Dataset. ML — Analyzing the Best Fit Regression Model for Forest Fire Prediction This project focuses on predicting forest fire occurrences using multiple machine learning regression algorithms — Linear Regression, Ridge, Lasso, and ElasticNet — to determine the best-performing model. The dataset used is the Algerian Forest Fires Dataset. ## Project Overview The main goal of this project is to build, compare, and evaluate different regression models to find the best fit model for predicting forest fire behavior. This end-to-end process includes data cleaning, exploratory data analysis (EDA), feature engineering, cross-validation, and model performance evaluation. ## Steps Involved 1) Importing essential libraries — NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn 2) Loading the Algerian Forest Fires dataset using Pandas 3) Checking and handling missing values and duplicate entries 4) Cleaning the dataset and creating a new column based on the fire region 5) Verifying data types and reassigning them as needed 6) Creating histograms to understand error distribution 7) Dropping irrelevant columns 8) Encoding independent and dependent features 9) splitting data into train and test sets 10) Checking multicollinearity using a heatmap 11) Removing features with correlation > 0.85 12) Applying feature scaling and standardization 13) Using boxplots to visualize standardization effects 14) Implementing regression models — Linear, Ridge, Lasso, and ElasticNet 15) Evaluating model performance using MAE, R² Score, and Cross-Validation 16) Visualizing results with scatter plots ## Learnings & Insights gs & Insights 1) Hands-on implementation of machine learning models with visualization and cross-validation 2) Understanding performance metrics and identifying the best regression model 3) Effective data cleaning and feature engineering 4) Handling multicollinearity and applying correlation thresholds 5) Model comparison and fine-tuning through cross-validation Model …