XGBClassifier.md

# Write Python3 code here
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
   
# Importing the dataset
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values
   
# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
  
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2 = LabelEncoder()
  
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
onehotencoder = OneHotEncoder(categorical_features = [1])
  
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]
   
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size = 0.2, random_state = 0)
  
# Fitting XGBoost to the training data
import xgboost as xgb
my_model = xgb.XGBClassifier()
my_model.fit(X_train, y_train)
   
# Predicting the Test set results
y_pred = my_model.predict(X_test)
   
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

You can get the dataset from kaggle here 👉: https://www.kaggle.com/aakash50897/churn-modellingcsv?select=Churn_Modelling.csv