Bank Marketing Data Analysis and Decision Tree Classifier

This repository contains a Python script that analyzes the "Bank Marketing" dataset from the UCI Machine Learning Repository. The primary goal is to predict whether a client will subscribe to a term deposit based on various features using a Decision Tree Classifier.You can view the Colab notebook here.

Overview

The analysis includes:

• Fetching and preprocessing data from the UCI Repository.
• Encoding categorical variables for machine learning models.
• Training a Decision Tree Classifier.
• Evaluating the model's performance using accuracy and other metrics.

Dataset

The dataset used in this analysis is the "Bank Marketing" dataset, which includes:

• Features: Attributes related to the client, including age, job, marital status, education, and more.
• Target: Whether the client subscribed to a term deposit (binary outcome).

Script Details

1. Data Fetching and Preparation

The script uses the ucimlrepo library to fetch the dataset. Features and target variables are separated, and categorical features are encoded using one-hot encoding.

2. Model Training

• The data is split into training and testing sets (80%-20%).
• A Decision Tree Classifier is trained on the training set.

3. Model Evaluation

• The model's accuracy is evaluated on the test set.
• A classification report and confusion matrix are generated to assess the model's performance.

4. Visualization (if applicable)

• Decision Tree visualization (optional): The decision tree can be visualized using the plot_tree function from sklearn.

Results

The model's performance will be displayed in the terminal, including accuracy, confusion matrix, and a detailed classification report.