This application takes inputs related to a URL’s features and depending upon that, it predicts whether the website is malicious or not. After doing various Experimentations it was decided that XGBoost was well performing ML algorithm for this use case The inputs are preprocessed and then given to the XGBoost model Depending upon the output of the model ,Output(whether the website is malicious or not) is displayed Link to Github

It is Machine Learning Classification Problem After Taking various inputs from the user data preprocessing is done like(Handling missing values,handling categorical values,handling outliers,handling distribution,bringing data into a same scale) Various Ml algorithms were considered during this experimentation and by using the optuna library Performance metrics - Recall was used as False Negative were the main concern in this usecase Link to Github

It is Machine Learning Regression Problem After Taking various inputs from the user data preprocessing is done like(Handling missing values,handling categorical values,handling outliers,handling distribution,bringing data into a same scale) Various Ml algorithms were considered during this experimentation and by using the optuna library and model was saved as a pickle file Performance metrics - Adjusted R square was used as a performance metric as this is a regression problem Link to Github