ANN - for Customer Subscription Prediction Using the Bank Marketing Dataset

Problem Statement

Develop an Artificial Neural Network (ANN) model to predict whether a customer will subscribe to a term deposit based on customer demographic, financial, and marketing campaign information. 

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Dataset Summary

Parameter	Value
Dataset Name	Bank Marketing Dataset
Source	UCI Machine Learning Repository
Total Records	45,211
Total Features	16 Input Features + 1 Target Variable
Target Variable	y (Yes/No)
Problem Type	Binary Classification

Steps to Perform ANN Practical

1. Import the required libraries.
2. Load the Bank Marketing dataset.
3. Explore the dataset (shape, columns, data types, summary statistics).
4. Check for missing values.
5. Encode categorical features into numerical values.
6. Separate input features (X) and target variable (y).
7. Split the dataset into training and testing sets.
8. Apply feature scaling to the input features.
9. Build the Artificial Neural Network (ANN) model.
10. Compile the ANN model.
11. Train the ANN model using the training dataset.
12. Predict the target values for the testing dataset.
13. Evaluate the model using accuracy and loss.
14. Generate the confusion matrix.
15. Display the classification report.
16. Interpret the results and draw the conclusion.