And for checking the performance of our model, we use a Test set. 1.5 Split the X and Y Dataset into the Training set and Test setДЇor building a machine learning model, we need to train our model on the training set. The next step is splitting the dataset into Training and Test set. So, the first two columns, represents the Geography variable. So, when you load the dataset after running this line of code, you will get your data something like this. Now the next step is- 1.2 Load the Dataset dataset = pd.read_csv('Churn_Modelling_dataset.csv') So in step 1, we imported all required libraries. PandasВ is a tool used for data wrangling and analysis. MatplotlibВ is a plottingВ library, that is used for creating a figure, plotting area in a figure, plot some lines in a plotting area, decorates the plot with labels, etc. It also has functions for working in the domain of linear algebra, Fourier transform, and matrices. NumPy is an open-source Python library used to perform various mathematical and scientific tasks.В NumPy is usedВ for working with arrays. In data preprocessing the first step is- 1.1 Import the Libraries- import numpy as np So the first step in the Implementation of an Artificial Neural Network in Python is Data Preprocessing. I hope now you understood the problem statement. So that bank can offer something special for the customers whom the predictive model predicts will leave the bank. This predictive model has to predict for any new customer that he or she will stay in the bank or leave the bank. Now the bank has to create a predictive model based on this dataset for new customers. The bank uses these independent variables and analyzes the behavior of customers for 6 months whether they leave the bank or stay and made this dataset. The last feature is the dependent variable and that is customer exited or not from the bank in the future( 1 means the customer will exit the bank and 0 means the customer will stay in the bank.) So these all are independent variables of the Churn Modelling dataset. This dataset has Customer Id, Surname, Credit Score, Geography, Gender, Age, Tenure, Balance, Num of Products they( use from the bank such as credit card or loan, etc), Has Credit card or not (1 means yes 0 means no), Is Active Member ( That means the customer is using the bank or not), estimated salary. Now letвЂ™s move on to the next layer and that is- Output Layer.

IвЂ™ll discuss this in the implementation part. The main purpose of doing standardization or normalization is to make all values in the same range. So take all these independent variables for one person or one row.ДЄnother important point you need to know is that you need to perform some standardization or normalizationВ on these independent variables. In more simple words, suppose there are different independent variables like a personвЂ™s age, salary, and job role. The important thing you need to remember is that these independent variables are for one observation. So the independent variable 1, independent variable 2, and independent variable n. In terms of an artificial neural network, the input layer contains independent variables.

And that signal is the Input signal in terms of the human brain. For example, if you touch some hot surface, then suddenly a signal sent to your brain. These senses are whatever you can see, hear, smells, or touch. So in terms of the human brain, these input signals are your senses.

I think now you may have a question in your mind that What signals are passed through the Input layer?.

Neural network python code