Lumpy skin Disease prediction using ML

Background

Lumpy skin disease is a highly contagious viral disease that affects cattle, resulting in substantial economic losses within the livestock industry. Timely detection and prediction of Lumpy skin disease outbreaks are essential for implementing effective control and prevention measures. This abstract provides an overview of the predictive modelling techniques utilized to forecast the occurrence and spread of Lumpy skin disease, emphasizing their potential for proactive disease management. To develop predictive models for Lumpy skin disease outbreaks, researchers have employed various data sources and methodologies. These include historical disease records, meteorological data, geographical information systems, and machine learning algorithms. The combination of these diverse datasets has enabled the identification of significant risk factors and the construction of accurate prediction models. Machine learning algorithms, such as support vector machines, random forests, and artificial neural networks, are commonly employed in Lumpy skin disease outbreak prediction

Aim & Objectives

Aim
The main Aim of this project is predicting weather a person is suffering from the lumpy disease or not.
Objective
• Data Cleaning and Pre-processing.
• Data visualization.
• SMOTE Technique.
• Building the ML model.
• LIME and SHAP techniques for interpreting the model.

Software & Tools

Jupyter, Google Collab

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