🏦 Bank Marketing Campaign Prediction using Machine Learning

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📌 Project Overview

This project focuses on predicting whether a customer will subscribe to a bank term deposit using Machine Learning classification models. The work was completed as part of a Data Science & Machine Learning Internship at United Network of Professionals (UNP).

The repository demonstrates an end-to-end machine learning workflow including data preprocessing, feature engineering, model comparison, and performance evaluation.

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🎯 Objectives

• Perform Exploratory Data Analysis (EDA)
• Handle categorical variables using encoding techniques
• Apply feature selection using Recursive Feature Elimination (RFE)
• Train multiple classification models
• Compare performance using accuracy metrics
• Identify the best predictive model

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🧠 Machine Learning Workflow

1. Data Cleaning & Preprocessing
2. Exploratory Data Analysis (EDA)
3. Feature Engineering & Encoding
4. SMOTE Oversampling for Imbalance Handling
5. Model Training & Evaluation
6. Performance Comparison

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🤖 Models Implemented

• Logistic Regression (All Features)
• Support Vector Machine (SVM)
• K-Nearest Neighbours (KNN)
• Decision Tree Classifier
• Random Forest Classifier
• Bagging Classifier
• AdaBoost Classifier
• Gradient Boosting Classifier
• XGBoost Classifier
• Neural Network (TensorFlow)

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📊 Model Performance Comparison (Accuracy)

Model	Accuracy (%)
Logistic Regression (RFE)	91.10
Logistic Regression (All Data)	90.59
Support Vector Machine (SVM)	91.30
K-Nearest Neighbours (KNN)	91.35
XGBoost Classifier	92.33 🏆
Gradient Boosting	92.30
AdaBoost	91.01
Bagging Classifier	91.25
Random Forest	91.28
Decision Tree	90.85
Neural Network (TensorFlow)	88.54

🏆 Best Performing Model: XGBoost Classifier — Accuracy 92.33%

📌 Ensemble learning methods achieved the highest performance, indicating strong non-linear relationships within the dataset.

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📂 Repository Structure

Bank_Final
│
├── Bank_Final.ipynb
└── README.md

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🛠️ Tools & Libraries

• Python
• Pandas & NumPy
• Scikit-learn
• TensorFlow / Keras
• XGBoost
• Matplotlib & Seaborn
• Statsmodels

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🚀 How to Run the Project

Install dependencies:

pip install -r requirements.txt

Open notebook:

Bank_Final.ipynb

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## 👨‍💻 Author

**Aditya Charan Eranki**
Machine Learning | Data Analytics | Data Science Enthusiast