This project will help users predict the likelihood of forest fires in different months, facilitating proactive measures and planning for forest fire prevention and management, thus potentially reducing the devastating impacts of these fires. It leverages the RandomForestRegressor machine learning model to predict the month of a forest fire based on other available features, transforming categorical variables to numerical for more accurate results. This script enables efficient data analysis and visualization of a forest fire dataset, providing valuable insights into variable correlations through heatmaps.
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Played around with Python libraries like Pandas, NumPy, and Scikit-learn to dig into a forest fires dataset. Dealt with missing values and switched categorical variables to numerical. Built and trained a model to guess the 'month' feature and split the data for training and testing. The model did pretty well, hitting over 90% accuracy on both sets
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typicalrobot/forestfires
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Played around with Python libraries like Pandas, NumPy, and Scikit-learn to dig into a forest fires dataset. Dealt with missing values and switched categorical variables to numerical. Built and trained a model to guess the 'month' feature and split the data for training and testing. The model did pretty well, hitting over 90% accuracy on both sets
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