In this project, we will identify the characteristics of women who are more likely to develop cervical cancer and use Automatic Machine Learning H2O AutoML to make prediction on whether or not a certain customer would buy a term deposit. We will also use Explainable AI (XAI) methods such as Variable Importance Plot, Partial Dependence Plot, SHAP Summary Plot, and LIME to explain how each of our feature input affects our model prediction.

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.

I have designed a Term deposit prediction system for banks using Data Science Algorithms as for which of the customers from the banks will be willing to take term deposits as 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.

HOTEL PREDICTION PROJECT DESCRIPTION ## Problem Statement This model predicts the probability of a customer will cancel a booking before checking in the hotel. It would be nice for the hotels to have a model to predict if a guest will actually come. This can help a hotel to plan things like personnel and food requirements. Maybe some hotels also use such a model to offer more rooms than they have to make more money. ## Dataset Information: This data set contains booking information for a city hotel and a resort hotel, and includes information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things. ## Attribute Information: * Hotel: Hotel (H1 = Resort Hotel or H2 = City Hotel) * is_canceled: Value indicating if the booking was cancelled (1) or not (0) * lead_time: Number of days that elapsed between the entering date of the booking into the PMS and the arrival date * arrival_date_year: Year of arrival date * arrival_date_month: Month of arrival date * arrival_date_week_number: Week number of year for arrival date * arrival_date_day_of_month: Day of arrival date * stays_in_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel * stays_in_week_nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel * adults: Number of adults * children: Number of children * babies: Number of babies * meal: Type of meal booked. Categories are presented in standard hospitality meal packages: Undefined/SC – no meal package; BB – Bed & Breakfast; HB – Half board (breakfast and one other meal – usually dinner); FB – Full board (breakfast, lunch and dinner) * country: Country of origin. Categories are represented in the ISO 3155–3:2013 format * market_segment: Market segment designation. In categories, the term “TA” means “Travel Agents” and “TO” means “Tour Operators” * distribution_channel: Booking distribution channel. The term “TA” means “Travel Agents” and “TO” means “Tour Operators” * is_repeated_guest: Value indicating if the booking name was from a repeated guest (1) or not (0) * previous_cancellations: Number of previous bookings that were cancelled by the customer prior to the current booking * previous_bookings_not_canceled: Number of previous bookings not cancelled by the customer prior to the current booking * reserved_room_type: Code of room type reserved. Code is presented instead of designation for anonymity reasons * assigned_room_type: Code for the type of room assigned to the booking. Sometimes the assigned room type differs from the reserved room type due to hotel operation reasons (e.g. overbooking) or by customer request. Code is presented instead of designation for anonymity reasons * booking_changes: Number of changes/amendments made to the booking from the moment the booking was entered on the PMS until the moment of check-in or cancellation * deposit_type: Indication on if the customer made a deposit to guarantee the booking. This variable can assume three categories: No Deposit – no deposit was made; Non Refund – a deposit was made in the value of the total stay cost; Refundable – a deposit was made with a value under the total cost of stay * agent: ID of the travel agency that made the booking * company: ID of the company/entity that made the booking or responsible for paying the booking. ID is presented instead of designation for anonymity reasons * days_in_waiting_list: Number of days the booking was in the waiting list before it was confirmed to the customer * customer_type: Type of booking, assuming one of four categories: Contract - when the booking has an allotment or other type of contract associated to it; Group – when the booking is associated to a group; Transient – when the booking is not part of a group or contract, and is not associated to other transient booking; Transient-party – when the booking is transient, but is associated to at least other transient booking * adr: Average Daily Rate as defined by dividing the sum of all lodging transactions by the total number of staying nights * required_car_parking_spaces: Number of car parking spaces required by the customer * total_of_special_requests: Number of special requests made by the customer (e.g. twin bed or high floor) * reservation_status: Reservation last status, assuming one of three categories: Canceled – booking was cancelled by the customer; Check-Out – customer has checked in but already departed; No-Show – customer did not check-in and did inform the hotel of the reason why * reservation_status_date: Date at which the last status was set. This variable can be used in conjunction with the Reservation Status to understand when was the booking cancelled or when did the customer checked-out of the hotel ## Libraries Used: * pandas * numpy * matplotlib * plotpy * folium * sklearn * eli5 ## Machine Learning Models Used: (1) Random Forest Classifier (2)Logistic Regression ## Steps Involved: (1).Data Import (2). Exploratory Data Analysis (EDA) (3). Model Building (4).Prediction part

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"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.

A machine learning API built using FastAPI to predict customer subscription to long-term deposits based on marketing campaign data. This project preprocesses input data, trains models, and serves predictions through a RESTful API.

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 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).

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.

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Python Project on Term Deposit

The aim of the project is to find the best supervised machine learning model to predict if a costumer is going to subscribe to a term deposit from a bank and be able to use the insights of the data to achieve real business value.

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Term Deposit Subscription Prediction using Ensemble Techniques

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

Project related to term deposit subscription prediction

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""" # 🏦 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

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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.

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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

Bank Term Deposit Subscription Prediction | ML Classification Project

This is term deposit prediction project using ML