Project overview
In this project, you’ll assume the role of a data scientist tasked with predicting the extent of damage caused by forest fires. Using a real-world dataset, you’ll apply advanced machine learning techniques in Python to build and optimize linear regression models.
Throughout the project, you’ll employ crucial skills such as data cleaning, exploratory analysis, feature engineering, cross-validation, and regularization. By iterating on a reference model, you’ll learn how to systematically enhance model performance. This hands-on experience will strengthen your machine learning expertise and demonstrate your ability to tackle complex, real-world prediction problems.
Objective: Develop an optimized machine learning model to accurately predict forest fire damage, showcasing your proficiency in applying advanced techniques for model improvement.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- Manipulating data to engineer relevant features for modeling
- Selecting optimal models using cross-validation and model selection metrics
- Assessing model performance using k-fold cross-validation
- Applying regularization techniques to optimize linear models
Projects steps
Step 1: Introduction
Step 2: Data Processing
Step 3: Data Visualization
Step 4: Subset Selection
Step 5: Developing Candidate Models
Step 6: K-Fold Cross-Validation
Step 7: Examining Model Weaknesses
Step 8: Next Steps
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