Malware Prediction in Software

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10 in stock


Malwares are software viruses. Once a computer can be infected by malware, criminals can hurt consumers and enterprises in many ways. The purpose of this project is to explore and analyse the data to find out the varieties of software issues. All potential features are extracted and feature selection technique is applied to define the target variable and input feature matrix. Various classification algorithms are used to classify the potential malwares and best classifier is recommended. Model explainability is used explain the top few candidate features based on feature importance.

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10 in stock

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Malwares are software viruses. Once a computer can be infected by malware, criminals can hurt consumers and enterprises in many ways. The purpose of this project is to explore and analyse the data to find out the varieties of software issues. All potential features are extracted and feature selection technique is applied to define the target variable and input feature matrix. Various classification algorithms are used to classify the potential malwares and best classifier is recommended. Model explainability is used explain the top few candidate features based on feature importance.

Salient Features:
– Comprehensive data analysis and visualizations
– Applying Dimensionality reduction technique
– Applying multiple ML algorithms
– Comparison of model performance
– Recommendation of the best algorithm for the prediction task

Tech Stacks:
Numpy, Pandas, Scikit Learn, Matplotlib, Seaborn, GridSearch, Python

Industry:
IT, Software Network

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