This project includes:
Data loading and cleaning
Categorical encoding
Feature–target separation
Model training using Decision Tree
Evaluation (accuracy, classification report, confusion matrix)
Dataset
Dataset Source: https://github.com/Prodigy-Infotech/data-science-datasets/tree/main/Task%203
File used: bank.csv
The dataset contains customer details like:
Age
Job
Marital status
Education
Loan status
Number of contacts
Outcome of previous marketing campaigns
Final subscription result (target variable: y)
Machine Learning Problem
This is a binary classification task.
Target Variable:
y = yes → Customer subscribed
y = no → Customer did not subscribe
Goal: Predict whether a new customer will subscribe to a term deposit.
Steps Performed
- Load and Explore Dataset
Read the CSV file
View dataset shape
Display first few rows
- Data Preprocessing
Many columns are categorical → use Label Encoding
No missing values handling required in this dataset
- Split Data
Training set: 75%
Testing set: 25%
- Model Training
We use:
DecisionTreeClassifier(criterion="entropy", max_depth=5)
- Model Evaluation
We compute:
Accuracy score
Classification report (precision, recall, f1-score)
Confusion matrix