Problem Statement: 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: https://archive.ics.uci.edu/dataset/222/bank+marketing
A decision tree is a tree-like structure used to make decisions or predictions based on input features. Each node in the tree represents a decision point based on a feature, while each branch represents the outcome of that decision. The final nodes, or "leaves," represent the predicted class or outcome.
Project Workflow:
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Loading the Dataset:The project begins by loading the dataset for analysis. (i)Data Preprocessing: Conducting preprocessing tasks to prepare the data for modeling. (ii)Encoding Categorical Variables: Converting categorical data into a numerical format suitable for machine learning. (iii)Splitting Data: Dividing the dataset into training and testing sets, typically at a ratio like 80/20 or 70/30.
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Training the Decision Tree Classifier: Training a decision tree classifier on the training data.
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Evaluating Model Performance: (i)Accuracy: Measuring the proportion of correct predictions among all predictions. (i)Confusion Matrix: Providing a table showing the true positives, false positives, true negatives, and false negatives. (i)Classification Report: Offering detailed metrics like precision, recall, and F1-score for each class.
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Visualizing the Decision Tree Creating a visual representation of the trained decision tree for better understanding and interpretation.
