## Description

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# Binary Classification using XGBoost in Python: Manual and Automatic Parameters Tuning

In this ** Data Science Recipe**, the reader will learn:

- How to organise a Predictive Modelling Machine Learning project step by step.
- What are the different steps in Predictive Modelling and Applied Machine Learning.
- How to summarise and present feature variables in Predictive Modelling (Descriptive statistics).
- How to visualise features through histogram, density plot, box plot and scatter matrix.
- How to find correlations among features variables.
- How to visualise target variables.
- How to do data analysis for feature and target variables.
- How to utilise
**Xgboost**,**sklearn**and**pandas**packages in Python. - How to implement
**Boosting Algorithms**for Binary Classification Algorithm in Python. - How to implement
**XgBoost**for Binary Classification Algorithm in Python. - How to setup
**XgBoost**hyper-parameters:**manual**and**automatic**tuning in Python. - How to setup
**RandomSearchCV**and**GridSearchCV**for parameter tuning in Python. - How to compare Algorithms with Accuracy and Kappa in Python.