For Y we have used df.iloc which says that take all the rows and just take the 8th column(target column).The last column is the target column and we name it Y.For X we have used df.iloc which says that take all the rows and include only 0:8(0,1,2,3,4,5,6,7) columns.Here we are keeping the first 8 columns as features and we name it X.This dataset is comprised of 8 input variables that describe the medical details of patients and one output variable to indicate whether the patient will have an onset of diabetes within 5 years.ĭf = pd.read_csv('',header=None).In this tutorial, we are going to use the Pima Indians onset of diabetes dataset.Importing accuracy_score and train_test_split from sklearn to calculate the accuracy and split the data respectively.įrom sklearn.model_selection import train_test_splitįrom trics import accuracy_score Step 2 – Loading the Data.Importing XGBClassifier from xgboost module to model it.Importing Pandas for reading the CSV file.

Windows pip install xgboost Linux sudo pip install xgboost Step 1 – Importing Required Libraries Refer to this XGBoost Installation guide.Step 6 – Testing the XGBoost Model Performance.Step 5 – Making predictions on the Test Data.

Easy xgboost install windows