The bank wants to use telephonic campaigns to market the term deposit but it is costly, so the task is to predict which customer is most likely to convert, thus helping the bank target the right customers. The bank provides the data such as the customer's demographics, job type data. Machine learning models can be used to make predictions

This project focuses on utilising machine learning techniques to predict the effectiveness of bank marketing campaign. Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Machine, XGBoost, K Nearest Neighbor, Naive Bayes, Support Vector Machine, and Artificial Neaural Networks algorithms are used to build a model for prediction.

This repository is about BANK-MARKETING-CAMPAIGN-PREDICTION, here i do Exploratory Data Analysis, Machine Learning, and HyperParameterTuning to predict wheter the client has subscribed to the term deposit or not

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.

No description available

A bank campaign prediction using Logistic Regression to predict whether customer deposit or not

This project aims to predict whether a client will respond positively or negatively to a marketing campaign conducted by a bank. The campaign utilizes phone calls as its communication channel. The dataset provided contains information related to direct marketing campaigns and encompasses various client attributes and campaign-specific features.

No description available

No description available

This project focuses on building a Decision Tree Classifier to predict customer responses to a bank's marketing campaign.

Machine Learning for Bank Subscription prediction after Marketing campaign

Bank Marketing Campaigns data Prediction using different machine learning models

Bank Marketing Campaign Prediction | Machine Learning Classification | UNP Internship Project

Machine Learning based predictions for the success of a bank marketing campaign

Machine Learning Project on Optimizing Customer Response Prediction in Bank Marketing Campaigns

Prediction of the Success of the Bank Telemarketing Campaign Using Various Classification Algorithms; A Machine Learning Approach by Python

This project is to built machine learning model to predict the client will subscribe a term deposit based on bank marketing campaigns historical data. Exploratory data analysis and machine learning prediction model deployed in Hugging Face platform.

This project builds a machine learning model to predict whether a bank customer will subscribe to a term deposit. Using demographic, financial, and campaign data, the model helps banks target high-potential customers, reduce marketing costs, and improve campaign efficiency through accurate and explainable predictions.

Machine learning project to predict client responses in bank marketing campaigns. Uses Logistic Regression and Random Forest with SMOTE to handle class imbalance, enhancing prediction accuracy for targeted outreach.

Classification and prediction on bank marketing dataset. Analyzed the data of a Portuguese bank’s past marketing campaign in order to find which customers should be targeted using machine learning techniques and comparing the different models. Models used: Logistic Regression, Random Forest, Deep Learning.

This project is based on the analysis of the previous campaign data of a Portuguese Bank with the help of Machine Learning Algorithms and deploying it in Flask for a user interface. Predictions are given for the upcoming campaigns with 82% accuracy.

A machine learning pipeline for predicting customer responses to bank marketing campaigns. This project demonstrates data preprocessing, feature engineering, and model training using scikit‑learn, with deployment examples via FastAPI for real‑time predictions.

Bank Marketing Campaign - Term Deposit Prediction :This machine learning project is based on the Bank Marketing dataset from a Portuguese banking institution. The goal is to build a classification model that predicts whether a customer will subscribe to a term deposit based on their profile and interaction history with the bank.

This repository contains the code for the Prediction of Bank Campaign's Outcom project, as created for the class 'Machine Learning Under a Modern Optimization Lens', during the Fall Semester 2022 at MIT Sloan, Master of Business Analytics

Explores various machine learning models to predict customer behavior regarding bank deposits. By analyzing historical data, the study compares models like decision trees, logistic regression, and random forests, aiming to optimize prediction accuracy and improve customer targeting for marketing campaigns.

This repository contains an end-to-end machine learning project for predicting the effectiveness of a bank's marketing campaign. The project leverages Amazon SageMaker for model training, deployment, and prediction, and AWS S3 for storing and managing datasets. The model is built using XGBoost, one of the most efficient machine learning algorithms

This project demonstrates how to build, train, and deploy an XGBoost model on AWS SageMaker to predict bank marketing campaign outcomes. It includes data preprocessing, model training, deployment as an endpoint, and making predictions. Ideal for those learning to use machine learning on AWS cloud infrastructure.

A Bank Management Prediction System is a machine learning tool that predicts whether a customer will subscribe to a product like a term deposit. It analyzes data such as age, job, balance, marital status, and past campaign results to help banks target the right customers and improve marketing decisions efficiently.

A machine learning project that uses classification and clustering to optimize bank marketing campaigns. The system predicts term deposit subscriptions and segments customers for targeted outreach using models like Random Forest, XGBoost, and K-Means. Includes real-time prediction via a Streamlit app.

This project involves the analysis of bank marketing campaigns and the prediction of term deposit subscriptions. By utilizing machine learning techniques, we aim to uncover patterns in customer behavior and predict whether a client will subscribe to a term deposit based on various features.