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.

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

No description available

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 project involves developing a predictive model to identify customers who are most likely to subscribe to a term deposit from a retail banking institution. Machine Learning algorithms like Linear Regression and Decision trees are used to get the best predictions.

📊 Logistic Regression Model for Loan Approval Prediction 🏦 Explore this machine learning project to predict whether customers will subscribe to a bank's term deposit based on various demographic and banking-related features. 📈 Analyze the code, 📊 dissect the dataset, and 🧐 evaluate model performance.

This project develops a machine learning system to predict whether clients of a Portuguese banking institution will subscribe to term deposits. Using a dataset from direct marketing campaigns, the model identifies key factors influencing subscription decisions and provides accurate predictions to optimize marketing strategies.

The following mock project describes the process followed in the statistical analysis of data related to a direct marketing campaign of a Portuguese banking institution. The goal of the statistical analysis was to use both Linear and Logistic regression models to make predictions on whether a client would subscribe to the “bank term deposit” product.