This repository contains data analysis project that aims to predict bank deposit based on customer details using logisitic regression.

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Utilizing logistic regression, predict customer term deposit subscriptions based on features like age and income, enabling targeted marketing for enhanced campaign effectiveness in banking.

Prediction model for bank term-deposit subscription using Machine Learning Models (Logistic Regression, Random Forest, Decision Tree & SVM). Achieved ~90% accuracy with hyper parameter tuning.

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.

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

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

Bank Marketing Term Deposit Prediction using Power BI (EDA), Logistic Regression, Random Forest, and SHAP for explainability.

Bank-Marketing-Term-Deposit-Prediction predicts term deposit subscriptions using UCI dataset. Preprocessing, EDA, Logistic Regression, Random Forest models evaluated with F1-Score, ROC-AUC. LIME explains predictions, highlighting duration, poutcome, month. Random Forest excels, suggests targeting retirees/students, optimizing campaigns.

Bank Marketing Predictor — Predict if a client will subscribe to a term deposit using Logistic Regression, scikit-learn & Streamlit. Includes full notebook for training + a user-friendly web app for live predictions.

TermDeposit Prediction: Hosted on Streamlit, analyzing bank data to predict term deposit subscriptions. Uses Logistic Regression, XGBoost, and Random Forest (91% accuracy). Includes EDA, preprocessing, and GridSearchCV tuning. Requires Pandas, Scikit-learn, XGBoost.

ML-Bank-Marketing-Predictor is a machine learning project that predicts whether a bank customer will subscribe to a term deposit using SVM and Logistic Regression. The model is trained on the Bank Marketing Dataset, deployed on AWS, and includes a web interface for real-time predictions.

This is a small project where I use an open-source dataset in order to train a prediction model based on Logistic Regression to predict whether a customer made a deposit in the bank.

Bank marketing and customer analytics project. It uses supervised learning (Logistic Regression, Random Forest) for *term deposit prediction* and unsupervised learning (K-Means) for *customer segmentation*, with a focus on feature engineering and model explainability.

Bank marketing and customer analytics project. It uses supervised learning (Logistic Regression, Random Forest) for *term deposit prediction* and unsupervised learning (K-Means) for *customer segmentation*, with a focus on feature engineering and model explainability.

Bank marketing and customer analytics project. It uses supervised learning (Logistic Regression, Random Forest) for *term deposit prediction* and unsupervised learning (K-Means) for *customer segmentation*, with a focus on feature engineering and model explainability.

A bank marketing prediction ML project uses customer and campaign data to predict which clients will subscribe to a term deposit. By applying algorithms like logistic regression or random forest, it helps banks target likely responders, improving marketing efficiency and conversion rates.

This project predicts customer subscription to term deposits in a bank marketing campaign using Logistic Regression, Random Forest, and SMOTE to handle class imbalance. It demonstrates key steps in data preprocessing, model building, and evaluation to improve prediction performance.

- Predicted the pattern from previous data and came out most significant result for bank. - Result was, the type of customers on which the bank should target in their telemarketing campaign to sell maximum term deposit in minimum attempts, thus saving time, money and energy. - Data was cleaned before modelling - Prediction model was built using logistic regression in SAS

The Portuguese bank offered its customers a short-term deposit campaign, and customers either participated or did not participate in this campaign. The response of customers to the campaing is predicted according to the data including the customers various information. Logistic regression and K-Nearest neighbor algorithms are used to operate this prediction model.

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.

This project is to build a model in a dataset of more than 41,000 records to predict the best number of customers buying term deposits of the bank, so as to achieve the purpose of reducing the bank's investment in marketing. Perform a Chi-square test to explore and understand the relationship between the predictor variable and the target variable in the dataset, and preprocess the dataset, such as normalizing the variables in the dataset. Use SAS to build multiple prediction models, such as Decision tree, Maximal decision tree, Logistic regression, Random forest, Support Vector Machines, Neural Networks and Auto-Neural Networks. Use Area Under Curve (AUC) and The misclassification rate compares these seven models and determines the best two models as Neural Networks and Auto-Neural Networks. Utilized:SAS Enterprise Miner,Decision tree, Logistic Regression, Random Forest, Support Vector Machines, Neural Networks,AUC,Misclassification Rate,Chi-square Test

Financial Institute attracts funds through deposits, loans, and bonds rather than from tangible property. And one major issue with granting loans is if the borrowers could fulfill their obligation or not. So we used bank data from 2001-2009 to come up with a model to help make the process of loan approval less tedious and reduce the risk of loan default. The data we used has information like their credit history, credit score, the purpose of the loan, their demographic details, income, and the target variable (status of the current loan(1/0)). For this, I used logistic regression as the prediction model. Which predicted with an accuracy of 82% when tested on our test model.