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How to output feature names with XGBOOST feature selection

My model uses feature importance for feature selection with XGBOOST. But, at the end, it outputs all the confusion matrices/results and how many features the model includes. That now works successfully, but I also need to have the feature names that were used in each model outputted as well.
I get a warning that says "X has feature names, but SelectFromModel was fitted without feature names", so I know something needs to be added to have them be in the model before I can output them, but I'm not sure how to handle either of those steps. I found several old questions about this, but I wasn't able to successfully implement any of them to my particular code. I'd really appreciate any ideas you have. Thank you!
from numpy import loadtxt
from numpy import sort
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import classification_report
# load data
dataset = df_train
# split data into X and y
X_train = df[df.columns.difference(['IsDeceased','IsTotal','Deceased','Sick','Injured','Displaced','Homeless','MissingPeople','Other','Total'])]
y_train = df['IsDeceased'].values
X_test = df_test[df_test.columns.difference(['IsDeceased','IsTotal','Deceased','Sick','Injured','Displaced','Homeless','MissingPeople','Other','Total'])]
y_test = df_test['IsDeceased'].values
# fit model on all training data
model = XGBClassifier()
model.fit(X_train, y_train)
# make predictions for test data and evaluate
print("Accuracy: %.2f%%" % (accuracy * 100.0))
# Fit model using each importance as a threshold
thresholds = sort(model.feature_importances_)
for thresh in thresholds:
# select features using threshold
selection = SelectFromModel(model, threshold=thresh, prefit=True)
select_X_train = selection.transform(X_train)
# train model
selection_model = XGBClassifier()
selection_model.fit(select_X_train, y_train)
print(thresh)
# eval model
select_X_test = selection.transform(X_test)
y_pred = selection_model.predict(select_X_test)
report = classification_report(y_test,y_pred)
print("Thresh= {} , n= {}\n {}" .format(thresh,select_X_train.shape[1], report))
cm = confusion_matrix(y_test, y_pred)
print(cm)

How to evaluate the effect of different methods of handling missing values?

I am a total beginner and I am trying to compare different methods of handling missing data. In order to evaluate the effect of each method (drop raws with missing values, drop columns with missigness over 40%, impute with the mean, impute with the KNN), I compare the results of the LDA accuracy and LogReg accuracy on the training set between a dataset with 10% missing values, 20% missing values against the results of the original complete dataset. Unfortunately, I get pretty much the same results even between the complete dataset and the dataset with 20% missing-ness. I don't know what I am doing wrong.
from numpy import nan
from numpy import isnan
from pandas import read_csv
from sklearn.impute import SimpleImputer
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
#dataset = read_csv('telecom_churn_rev10.csv')
dataset = read_csv('telecom_churn_rev20.csv')
dataset = dataset.replace(nan, 0)
values = dataset.values
X = values[:,1:11]
y = values[:,0]
dataset.fillna(dataset.mean(), inplace=True)
#dataset.fillna(dataset.mode(), inplace=True)
print(dataset.isnull().sum())
imputer = SimpleImputer(missing_values = nan, strategy = 'mean')
transformed_values = imputer.fit_transform(X)
print('Missing: %d' % isnan(transformed_values).sum())
model = LinearDiscriminantAnalysis()
cv = KFold(n_splits = 3, shuffle = True, random_state = 1)
result = cross_val_score(model, X, y, cv = cv, scoring = 'accuracy')
print('Accuracy: %.3f' % result.mean())
#print('Accuracy: %.3f' % result.mode())
print(dataset.describe())
print(dataset.head(20))
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)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy_score(y_test,y_pred)
from sklearn import metrics
# make predictions on X
expected = y
predicted = classifier.predict(X)
# summarize the fit of the model
print(metrics.classification_report(expected, predicted))
print(metrics.confusion_matrix(expected, predicted))
# make predictions on X test
expected = y_test
predicted = classifier.predict(X_test)
# summarize the fit of the model
print(metrics.confusion_matrix(expected, predicted))
print(metrics.classification_report(expected, predicted))

You replace all your missing values with 0 at that line : dataset = dataset.replace(nan, 0). After this line, you have a full dataset without missing values. So, the .fillna() and the SimpleImputer() are useless after that line.

raise ValueError ValueError: Found array with 0 feature(s) (shape=(124, 0)) while a minimum of 1 is required

I am trying to apply a PCA (Principal component analysis) on a dataset with 124 rows and 13 features. I'm trying to see how many features to use (via Logistic Regression) to get the most accurate prediction, I have this code here:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/'
'machine-learning-databases/wine/wine.data', header=None)
from sklearn.model_selection import train_test_split
X, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=0.3, stratify=y, random_state=0)
# standardize the features
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train_std = sc.fit_transform(X_train)
X_test_std = sc.transform(X_test)
from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import PCA
# initializing the PCA transformer and
# logistic regression estimator:
pca = PCA() #prof recommends getting rid of m_components = 3
lr = LogisticRegression()
# dimensionality reduction:
X_train_pca = pca.fit_transform(X_train_std)
X_test_pca = pca.transform(X_test_std)
"""
rows = len(X_train_pca)
columns = len(X_train_pca[0])
print(rows)
print(columns)
"""
# fitting the logistic regression model on the reduced dataset:
for i in range(12):
lr.fit(X_train_pca[:, :i], y_train)
y_train_pca = lr.predict(X_train_pca[:, :i])
print('Training accuracy:', lr.score(X_train_pca[:, :i], y_train))
I get the error message: raise ValueError("Found array with %d feature(s) (shape=%s) while"
ValueError: Found array with 0 feature(s) (shape=(124, 0)) while a minimum of 1 is required.
To my understanding, the for loop range is correct at 12 because it will go through all 13 features (0 through 12) and I am trying to have the for loop go through all the features (go through logistic regression with one feature, then two, then 3.... on and on until all 13 features and then see what their accuracies are to see how many features works best).

To your error:
X_train_pca[:, :i] when i=0 will give you an empty array, which is invalid as an input of .fit().
How to solve:
If you want to fit the model with only intercept, you can explicitly set fit_intercept=False in LogisticRegression() and add one extra column (to the leftmost) in your X filled with 1 (to act as the intercept).

Trying to implement XGBoost into my Artificial Neural Network

I'm completely unaware as to why i'm receiving this error. I am trying to implement XGBoost but it returns with error "ValueError: For a sparse output, all columns should be a numeric or convertible to a numeric." Even after i've One Hot Encoded my categorical data. If anyone knows what is causing this and a possible solution i'd greatly appreciate it. Here is my code written in Python:
# Artificial Neural Networks - With XGBoost
# PRE PROCESS
# 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 OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer([('encoder', OneHotEncoder(), [1, 2])],
remainder = 'passthrough')
X = np.array(ct.fit_transform(X), dtype = np.float)
# 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=0.20, random_state = 0)
# Fitting XGBoost to the training set
from xgboost import XGBClassifier
classifier = XGBClassifier()
classifier.fit(x_train, y_train)
# Predicting the Test set Results
y_pred = classifier.predict(x_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
# Applying k-Fold Cross Validation
from sklearn.model_selection import cross_val_score
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)
accuracies.mean()
accuracies.std()

added Standardscaler but receive errors in Cross Validation and the correlation matrix

This is the code I built to apply a multiple linear regression. I added standard scaler to fix the Y intercept p-value which was not significant but the problem that the results of CV RMSE in the end changed and have nosense anymore and received an error in the code for plotting the correlation Matrix saying : AttributeError: 'numpy.ndarray' object has no attribute 'corr'
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
from scipy import stats
from scipy.stats.stats import pearsonr
# Import Excel File
data = pd.read_excel("C:\\Users\\AchourAh\\Desktop\\Multiple_Linear_Regression\\SP Level Reasons Excels\\SP000273701_PL14_IPC_03_09_2018_Reasons.xlsx",'Sheet1') #Import Excel file
# Replace null values of the whole dataset with 0
data1 = data.fillna(0)
print(data1)
# Extraction of the independent and dependent variables
X = data1.iloc[0:len(data1),[1,2,3,4,5,6,7]] #Extract the column of the COPCOR SP we are going to check its impact
Y = data1.iloc[0:len(data1),9] #Extract the column of the PAUS SP
# Data Splitting to train 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 =0.25,random_state=1)
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
# Statistical Analysis of the training set with Statsmodels
X = sm.add_constant(X_train) # add a constant to the model
est = sm.OLS(Y_train, X).fit()
print(est.summary()) # print the results
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import math
lm = LinearRegression() # create an lm object of LinearRegression Class
lm.fit(X_train,Y_train) # train our LinearRegression model using the training set of data - dependent and independent variables as parameters. Teaching lm that Y_train values are all corresponding to X_train.
print(lm.intercept_)
print(lm.coef_)
mse_test = mean_squared_error(Y_test, lm.predict(X_test))
print(math.sqrt(mse_test))
# Data Splitting to train and test set of the reduced data
X_1 = data1.iloc[0:len(data1),[1,2]] #Extract the column of the COPCOR SP we are going to check its impact
X_train2, X_test2, Y_train2, Y_test2 = train_test_split(X_1, Y, test_size =0.25,random_state=1)
X_train2 = ss.fit_transform(X_train2)
X_test2 = ss.transform(X_test2)
# Statistical Analysis of the reduced model with Statsmodels
X_reduced = sm.add_constant(X_train2) # add a constant to the model
est_reduced = sm.OLS(Y_train2, X_reduced).fit()
print(est_reduced.summary()) # print the results
# Fitting a Linear Model for the reduced model with Scikit-Learn
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import math
lm1 = LinearRegression() #create an lm object of LinearRegression Class
lm1.fit(X_train2, Y_train2)
print(lm1.intercept_)
print(lm1.coef_)
mse_test1 = mean_squared_error(Y_test2, lm1.predict(X_test2))
print(math.sqrt(mse_test1))
#Cross Validation and Training again the model
from sklearn.model_selection import KFold
from sklearn import model_selection
kf = KFold(n_splits=6, random_state=1)
for train_index, test_index in kf.split(X_train2):
print("Train:", train_index, "Validation:",test_index)
X_train1, X_test1 = X[train_index], X[test_index]
Y_train1, Y_test1 = Y[train_index], Y[test_index]
results = -1 * model_selection.cross_val_score(lm1, X_train1, Y_train1,scoring='neg_mean_squared_error', cv=kf)
print(np.sqrt(results))
#RMSE values interpretation
print(math.sqrt(mse_test1))
print(math.sqrt(results.mean()))
#Good model built no overfitting or underfitting (Barely Same for test and training :Goal of Cross validation but low prediction accuracy = Value is big
import seaborn
Corr=X_train2.corr(method='pearson')
mask=np.zeros_like(Corr)
mask[np.triu_indices_from(mask)]=True
seaborn.heatmap(Corr,cmap='RdYlGn_r',vmax=1.0,vmin=-1.0,mask=mask, linewidths=2.5)
plt.yticks(rotation=0)
plt.xticks(rotation=90)
plt.show()
enter code here
Do you have an idea how to fix the issue ?

I'm guessing the problem lies with:
Corr=X_train2.corr(method='pearson')
.corr is a pandas dataframe method but X_train2 is a numpy array at that stage. If a dataframe/series is passed into StandardScaler, a numpy array is returned. Try replacing the above with:
Corr=pd.DataFrame(X_train2).corr(method='pearson')
or make use of numpy.corrcoef or numpy.correlate in their respective forms.