Final Project : Project based on a real life Business Problem. In this Project, you will be using all the skills that you have acquired throughout this course. Problem Statement Your client is a retail banking institution. Term deposits are a major source of income for a bank. A term deposit is a cash investment held at a financial institution. Your money is invested for an agreed rate of interest over a fixed amount of time, or term. The bank has various outreach plans to sell term deposits to their customers such as email marketing, advertisements, telephonic marketing and digital marketing. Telephonic marketing campaigns still remain one of the most effective way to reach out to people. However, they require huge investment as large call centers are hired to actually execute these campaigns. Hence, it is crucial to identify the customers most likely to convert beforehand so that they can be specifically targeted via call. You are provided with the client data such as : age of the client, their job type, their marital status, etc. Along with the client data, you are also provided with the information of the call such as the duration of the call, day and month of the call, etc. Given this information, your task is to predict if the client will subscribe to term deposit. Data You are provided with following files: 1. train.csv : Use this dataset to train the model. This file contains all the client and call details as well as the target variable “subscribed”. You have to train your model using this file. 2. test.csv : Use the trained model to predict whether a new set of clients will subscribe the term deposit. Data Dictionary Here is the description of all the variables : Variable Definition ID Unique client ID age Age of the client job Type of job marital Marital status of the client education Education level default Credit in default. housing Housing loan loan Personal loan contact Type of communication month Contact month day_of_week Day of week of contact duration Contact duration campaign number of contacts performed during this campaign to the client pdays number of days that passed by after the client was last contacted previous number of contacts performed before this campaign poutcome outcome of the previous marketing campaign Subscribed (target) has the client subscribed a term deposit? How good are your predictions? Evaluation Metric The Evaluation metric for this competition is accuracy. Solution Checker You can use solution_checker.xlsx to generate score (accuracy) of your predictions. This is an excel sheet where you are provided with the test IDs and you have to submit your predictions in the “subscribed” column. Below are the steps to submit your predictions and generate score: a. Save the predictions on test.csv file in a new csv file. b. Open the generated csv file, copy the predictions and paste them in the subscribed column of solution_checker.xlsx file. c. Your score will be generated automatically and will be shown in Your Accuracy Score column You can also check out the baseline Python Notebook provided to get started.

No description available

"Bank Marketing Term Deposit Prediction: A classification project using machine learning algorithms to predict whether a customer will subscribe to a term deposit based on demographic and financial data. Aim: Develop a model to improve marketing strategies and increase subscription rates."

This project is dedicated to make a prediction model whether a call marketing successifully sold a term deposit of the Kaggle dataset Bank marketing campaigns dataset | Opening Deposit.

Group academic research project predicts bank client term deposit subscriptions using data science and machine learning, with a deployed application for real-time predictions through streamlit

A project focused on predicting bank term deposit subscriptions using classification models. The repository contains data analysis, feature engineering, and model implementation steps aimed at maximizing prediction accuracy.

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.

This project creates Decision Trees of a Portuguese Bank data and tries to predict if the client will subscribe a term deposit at the bank. It uses two famous approach, Gini Index and Entropy on given csv file. It also displays the created trees at the end of the prediction.

This project predicts whether a bank client will subscribe to a term deposit (`y = yes/no`) based on their personal and campaign-related data. The goal is to help marketing teams target potential customers efficiently while understanding the drivers behind each prediction using explainable AI (SHAP).

No description available

No description available

Machine learning project predicting customer subscription to a bank term deposit using marketing data. Includes data cleaning, EDA, model building, and evaluation.

Bank Term Deposit Subscription Prediction | ML Classification Project

""" # 🏦 BANK MARKETING TERM DEPOSIT PREDICTION PROJECT ## 📌 Project Description This project focuses on predicting whether a bank customer will subscribe to a **term deposit** based on demographic, behavioral, and economic features. The goal is to help the bank marketing team **optimize campaign strategies**, reduce unnecessary calls

The investment and portfolio department of the Bank of Portugal has been conducting direct marketing campaigns to identify potential customers who would subscribe to their term deposits. The analysis aims at developing a robust machine learning algorithm to predict if a potential client will subscribe to the Bank’s term deposits.

No description available

Developed an end-to-end machine learning model to predict customer term deposit subscriptions. Performed EDA on demographics, campaign data, and economic indicators, applied logistic regression with feature scaling, and evaluated results using classification metrics to identify key predictors

ML Bank Marketing Prediction - Term Deposit Subscription Predictor Project

No description available

The increasingly vast number of marketing campaigns over time has reduced its effect on the general public. Economic pressures and competition have led marketing managers to invest on directed campaigns with a strict and rigorous selection of contacts: lesser contacts should be done, with better success rate. Although telemarketing is a direct mode of communication with the prospective customer, this may make customers grumpy. In this Project, Exploratory Data Analysis, Statistical Tests, Imbalanced Data treatment and Predictive modeling is used in determining the main characteris-tics that affect success and selection of potential buying customers. Classifica-tion algorithms like Logistic Regression, CART, KNN, and ensemble algo-rithms like random forest and XG Boost were used to build the model using the most popular tool python and the appropriate model is selected based on F1-score, ROC, Accuracy value and False Negative value (FN). Further, this project also attempts to provide model interpretability that may help bank target the right customers.

To develop a predictive model that determines whether a banking customer is likely to subscribe to a term deposit based on their demographic, financial, behavioural, and marketing attributes. This enables the bank to optimize marketing campaigns, improve customer targeting, and enhance overall conversion efficiency.

No description available

No description available

This project develops a machine learning classification system to predict whether a bank customer will subscribe to a term deposit product (YES / NO) based on telemarketing campaign data.

Bank Marketing Survey to predict customer response for term deposit

Capstone Project 17.1 – Comparing Classifiers for Bank Term Deposit Subscription Prediction | Berkeley ML & AI Professional Certificate

Machine Learning Project Repository for Bank Term Deposit Prediction using Logistic Regression and Random Forest (UCI Dataset)

Project akhir kelompok 6 kelas Engine dengan judul "Bank Marketing : Prediction Whether The Customer Will Subscribe to Term Deposit”

Data mining project for binary classification label prediction to find out if bank customers would subscribe to a new term deposit

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.