bank-marketing

The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit.

Marketing refers to activities undertaken by a company to promote the buying or selling of a product or service. Marketing includes advertising, selling, and delivering products to consumers or other businesses. Our data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone ca…

Trained a model that estimates if a lead is likely to be converted based on lead behavior in historical customer data using ML.

This project was developed during the “Introduction to Machine Learning” Bootcamp organized by Global AI Hub in collaboration with Akbank.

A Laravel-based web system for aggregating, validating and analyzing monthly Excel performance reports from bank branches. Supports multi-file upload, Persian date validation, employee/branch reference lookup, async processing via queues, and consolidated dashboard with Excel export.

Bank Marketing Data Set Binary Classification in python

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Bank Marketing Prediction using R

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

Explainable AI decision tree classifier predicting bank term-deposit subscriptions using SHAP values — includes leakage-aware feature selection and human-readable prediction explanations.

Fortivus is a machine learning project that uses Logistic Regression to analyze real bank marketing data. It predicts whether a customer will subscribe to a term deposit, supporting smarter marketing strategies and data‑driven decisions.

基于 Python、MySQL 和 Power BI 的电商会话行为与购买转化分析项目

CyberSoft Machine Learning 03 - Overview

Predicting bank customer subscription to term deposits using Logistic Regression and Random Forest. Includes SHAP explainability for 5+ individual predictions, ROC curve, F1-score evaluation, and feature importance analysis. Built with Python, Scikit-learn & SHAP.

Analyzing user data to predict their choice of enrolling in a term deposit. Deriving insights from the data to target customers during marketing.

Bank Marketing Data Set

Machine Learning project to predict customer term-deposit subscriptions using the Bank Marketing Dataset through data preprocessing, feature engineering, and classification models.

Machine learning project predicting bank term deposit subscriptions using customer demographics and campaign data. Features multiple ML models (Random Forest, Logistic Regression, Decision Tree), SMOTE for class balancing, and interactive Streamlit app for real-time predictions.

Decision tree classifier predicting customer subscription using GridSearchCV optimisation achieving 93.6% accuracy on Bank Marketing data. | Prodigy InfoTech Data Science Internship — Task 03

End-to-end ML portfolio project for bank campaign targeting: customer scoring, top-k policy simulation, SQLite score storage, Streamlit/FastAPI demo, and Tableau dashboard.