Please use Python to build an artificial neural network (ANN) model using attached dataset titled (Bank_Predictions.csv) to predict a list of bank accounts (Acc_Closed - (ys)) at risk of closing (what account more likely to be closed or opened - hint: this is a binary classification problem) based-on several features (xs). Please ignore the first three features of the dataset that are (Number, Customer_ID, and Last_Name) because they don't have any impacts on the prediction. So, you could use the rest of features to predict the accounts that are more likely to be closed.

use a portion of the dataset Bank_Predictions which I have provided below (it is only 10 lines because the actual file has over 1000 lines so here is a small snippet);

Number	Customer_ID	Last_Name	Cr_Score	Location	Gender	Age	History	Current_Balance	Num_Of_Products	Has_CrCard	IsActiveMember	Customer_Salary	Acc_Closed
1	15634602	Hargrave	619	France	Female	42	2	0	1	1	1	101348.9	1
2	15647311	Hill	608	Spain	Female	41	1	83807.86	1	0	1	112542.6	0
3	15619304	Onio	502	France	Female	42	8	159660.8	3	1	0	113931.6	1
4	15701354	Boni	699	France	Female	39	1	0	2	0	0	93826.63	0
5	15737888	Mitchell	850	Spain	Female	43	2	125510.8	1	1	1	79084.1	0
6	15574012	Chu	645	Spain	Male	44	8	113755.8	2	1	0	149756.7	1
7	15592531	Bartlett	822	France	Male	50	7	0	2	1	1	10062.8	0
8	15656148	Obinna	376	Germany	Female	29	4	115046.7	4	1	0	119346.9	1
9	15792365	He	501	France	Male	44	4	142051.1	2	0	1	74940.5	0
10	15592389	Hanah	684	France	Male	27	2	134603.9	1	1	1	71725.73	0
11	15767821	Bearce	528	France	Male	31	6	102016.7	2	0	0	80181.12	0

use this template as a guide to complete the task:

# Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset dataset = pd.read_csv('Bank_Predictions.csv') # ------ Part-1: Data preprocessing ---------- # Encoding categorical data # Splitting the dataset into the Training and Test sets # Feature Scaling # ------- Part-2: Build the ANN -------- # import keras library and packages # Initializing the ANN # Adding the input layer and the first hidden layer # Adding second hidden layer # Adding output layer # Compiling the ANN # Fitting the ANN to the training set # Predicting the Test set results # Making the confusion Matrix 

Please include a screenshot with the answer.Thanks!