This project aims to enhance disaster preparedness and risk management in India by developing a Geographical Risk Prediction Model (GRPM) that predicts natural disasters such as floods and landslides. Leveraging Python, Tableau, and logistic regression algorithms, the model utilizes data from weather and news sources to provide accurate, location-specific risk predictions with a user-friendly interface. The GRPM addresses data collection challenges and is designed to support authorities in making informed decisions to mitigate risks, safeguard lives, and allocate resources effectively.
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This project develops a Geographical Risk Prediction Model (GRPM) using Python, Tableau, and logistic regression to predict natural disasters like floods and landslides in India. By analyzing weather and news data, the model provides accurate, location-specific risk assessments to aid in disaster preparedness and resource allocation.
Akj0805/Geographical-Risk-Prediction-Model
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This project develops a Geographical Risk Prediction Model (GRPM) using Python, Tableau, and logistic regression to predict natural disasters like floods and landslides in India. By analyzing weather and news data, the model provides accurate, location-specific risk assessments to aid in disaster preparedness and resource allocation.
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