For Kaggle Competition - https://www.kaggle.com/competitions/predict-the-success-of-bank-telemarketing a community prediction competition hosted by iitm**
Project Overview This project analyzes direct marketing campaigns conducted by a banking institution. The campaigns were executed through phone calls, with multiple contacts sometimes necessary to determine if a client would subscribe to a bank term deposit. The goal is to predict whether a client will subscribe to the term deposit ('yes') or not ('no').
Dataset Description The dataset contains various client-related features including: • Demographics (age, job, marital status, education) • Financial indicators (balance, housing loan, personal loan) • Campaign-specific information (contact duration, campaign frequency) • Historical context (previous campaign outcomes)
The solution implements a comprehensive machine learning pipeline including:
Data Processing • Feature engineering for date-related columns • Binary variable encoding • Handling missing values through imputation • Standardization of numerical features • One-hot encoding for categorical variables
Model Architecture The final solution employs a stacking ensemble approach combining: • Random Forest Classifier • Gradient Boosting Classifier • Neural Network (MLP) • Support Vector Machine • K-Nearest Neighbors • Naive Bayes • Logistic Regression (meta-learner)
Feature Selection & Dimensionality Reduction • PCA implementation for dimensionality reduction • SelectKBest with mutual information for feature selection • Comprehensive feature importance analysis**
Model Performance The stacking ensemble model achieves robust performance through: • Cross-validation to ensure reliability • F1-score optimization for balanced prediction • Comprehensive validation against holdout data • Multiple base models to capture different patterns in the data
Visualization The project includes various visualizations: • Target variable distribution • Feature distributions and correlations • Box plots for numerical features vs target • Stacked bar plots for categorical features
Future Improvements • Feature engineering based on domain knowledge • Hyperparameter optimization using Bayesian methods • Implementing more sophisticated ensemble techniques • Deep learning approaches for feature extraction
• Python 3.x • pandas • numpy • scikit-learn • xgboost • matplotlib • seaborn*
In order to run this on your system follow these steps:
- clone this repository using git clone {url}
- cd Predict the Success of Bank telemarketing-Machine Learning Project
- make sure you have python installed on your machine
- pip install -r requirements.txt