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This kit is a machine learning kit that predicts droughts and their severities based on meteorological indicators. The use case of such a kit would be to predict the severity of drought and the possibility of drought in a specific region which can be used to analyze and prepare for such an oncoming disaster. The proposed model takes in meteorological indicators of a specific area and predicts the possibility of drought in that area in the near future. The algorithm used is Random forest classifier.

EDA

Used to perform exploratory data analysis on the data and analyze a model. Used for extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

Machine learning and data visualization

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning. The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.

Kit Solution Source

Dataset: https://www.kaggle.com/datasets/cdminix/us-drought-meteorological-data collab link: https://colab.research.google.com/drive/1-379ZQTuTE9LU_UQxz5T34Id5Zk8DGNh?usp=sharing

Kit Deployment Instructions

1. Download the data set from https://drive.google.com/file/d/1VQ3HZ3ADQLxGHX5s-vDK43qlsI4-Y0uw/view?usp=sharing 2. Access the google collab notebook through https://colab.research.google.com/drive/1-379ZQTuTE9LU_UQxz5T34Id5Zk8DGNh?usp=sharing 3. View the collab file through the link.
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