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[Project 07]

Developed a machine learning model to predict wildfire risk zones across the United States.

Machine Learning

US Wildfire Risk Mapping and Prediction using Random Forest

Academic Project - Cpurse: Data Wrangling and Transformation

Machine learning model to predict wildfire risk zones across the United States, with over a million rows of data.

[Details]

Developed a machine learning model to predict wildfire risk zones across the United States. Applied data wrangling and transformation techniques to clean and prepare datasets for predictive analysis, highlighting areas most susceptible to wildfire events.

The Project involved:

  • Data Preprocessing and Transformation: Data cleaning, feature engineering, binary encoding, outlier detection using IQR, and log transformation.


  • Data Visualization and Aggregation: Created correlation matrices, line plots, bar charts, dual-axis combo charts, interactive heatmaps, and performed data aggregation for better insights.


  • Machine Learning: Built a Random Forest Classifier to predict high-risk wildfire zones, achieving an accuracy of 94.61%.

[Industry]

Machine Learning

[My Role]

Analytics and Project Lead

[Platforms]

Google Colab

[Timeline]

Oct 2025

View Google Colab

View Google Colab

View Google Colab

[My Contributions]
[01] Data Processing & Feature Engineering:

Cleaned and preprocessed the wildfire dataset, handled missing values, engineered relevant environmental features, and prepared the final modeling dataset.

[01] Data Processing & Feature Engineering:

Cleaned and preprocessed the wildfire dataset, handled missing values, engineered relevant environmental features, and prepared the final modeling dataset.

[01] Data Processing & Feature Engineering:

Cleaned and preprocessed the wildfire dataset, handled missing values, engineered relevant environmental features, and prepared the final modeling dataset.

[02] Model Development & Evaluation

Implemented a Random Forest model for wildfire risk prediction, tuned hyperparameters, and evaluated performance using metrics such as accuracy and feature importance scores.

[02] Model Development & Evaluation

Implemented a Random Forest model for wildfire risk prediction, tuned hyperparameters, and evaluated performance using metrics such as accuracy and feature importance scores.

[02] Model Development & Evaluation

Implemented a Random Forest model for wildfire risk prediction, tuned hyperparameters, and evaluated performance using metrics such as accuracy and feature importance scores.

[03] Risk Mapping & Visualization

Developed an interactive U.S. wildfire risk heatmap using geographic coordinates and model outputs; created clear visualizations to communicate feature contributions and risk levels.

[03] Risk Mapping & Visualization

Developed an interactive U.S. wildfire risk heatmap using geographic coordinates and model outputs; created clear visualizations to communicate feature contributions and risk levels.

[03] Risk Mapping & Visualization

Developed an interactive U.S. wildfire risk heatmap using geographic coordinates and model outputs; created clear visualizations to communicate feature contributions and risk levels.

[Key Learnings]
Data Preparation

Gained hands-on experience cleaning, transforming, and engineering environmental datasets for ML readiness.

Data Preparation

Gained hands-on experience cleaning, transforming, and engineering environmental datasets for ML readiness.

Data Preparation

Gained hands-on experience cleaning, transforming, and engineering environmental datasets for ML readiness.

Model Using Geospatial Mapping

Improved understanding of feature importance, visualization, and geospatial mapping for actionable insights.

Model Using Geospatial Mapping

Improved understanding of feature importance, visualization, and geospatial mapping for actionable insights.

Model Using Geospatial Mapping

Improved understanding of feature importance, visualization, and geospatial mapping for actionable insights.

Workflow & Time Management

Strengthened discipline in balancing accuracy, data quality, visualization clarity, and project timelines.

Workflow & Time Management

Strengthened discipline in balancing accuracy, data quality, visualization clarity, and project timelines.

Workflow & Time Management

Strengthened discipline in balancing accuracy, data quality, visualization clarity, and project timelines.

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