Build a decision tree classifier to predict whether a customer will purchase a product or service based on their demographic and behavioral data. Use a dataset such as the Bank Marketing dataset from the UCI Machine Learning Repository.
Dataset is from UCI Machine Learning Repository of Bank Marketing. link - https://archive.ics.uci.edu/dataset/222/bank+marketing
Here, Decision tree classifier is built to predict whether the customer will purchase a product or service. First, the dataset is downloaded and imported in the jupyter notebook. These are the steps used to build the Decision tree classifier, Importing Libraries: Required libraries for data manipulation, visualization, and model building. Loading the Dataset: Load the Bank Marketing dataset. Preprocessing: Handling missing values, encoding categorical variables, and splitting the dataset. Building the Model: Creating a decision tree classifier. Evaluating the Model: Checking model performance using metrics like accuracy, precision, recall, and the confusion matrix. Visualize the Decision Tree.