A Comprehensive Guide to Titanic Machine Learning from Disaster

A financial institution wants to accurately predict the probability of loanee/borrower defaulting on a vehicle loan in the first EMI on the due date.

We conduct cluster analysis on customers and machine learning to predict if a customer will receive insurance benefits.

Multi-label AUC plot script

ExcelR-Assignment---Logistic-Regression-Assignment---6

This is an contrast of linear regression model, used to examine the association between the independent variable(category or contineous) with dependent variable(binary), which is an discrete outcome.

The aim is to analyze the data of INN Hotels to find which factors have a high influence on booking cancellations, build predictive models(logisitic regression, decision trees) that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.

Neural Network

The project involves analyzing certain issues of customer churn faced by telecom companies. Models are required to be built so as to predict whether a customer will cancel their service in the future or not and then model comparison measures are made for taking interpretation and recommendations from the best model.

Classification (Ranking) Clients Project

Data Visualization, Random Forest and AUC-ROC Curve

A hotel chain is having issues with cancellations. This project analyzes customer booking data to identify which factors significantly influence cancellations, build models using logistic regression and decision trees to predict cancellations in advance, and help formulate profitable policies for cancellations and refunds for the hotel group

Classification algorithms to predict if a client would default on their loan

Performance Analysis of different ML classifiers for Cardiovascular disease classification

Credit card transactions fraud detection using classic algorithms

This repository contains code and documentation for a machine learning project focused on predictive maintenance in industrial machinery. The project explores the development of a comprehensive predictive maintenance system using various machine learning techniques.

This project presents and discusses data-driven predictive models for predicting the defaulters among the credit card users.About Data Cleaning,Exploratory Data Analysis ,Handling Class Imbalance, Transforming Data , Fitting Different Model ,Cross Validation & Hyperparameter Tunning, Comparison of Model ,Combined ROC Curve, Feature Impotance.

Predicting churn rate of customers using Random Forest

Predicting Customer Response to Telemarketing Campaigns for Term Deposit. Output variable Whether the client has subscribed a term deposit or not.

Analyze the data of INN Hotels to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds