✋🏼🛑 This one stop project is a complete COVID-19 detection package comprising of 3 tasks: • Task 1 --> COVID-19 Classification • Task 2 --> COVID-19 Infection Segmentation • Task 3 --> Lung Segmentation

A two-stage predictive machine learning engine that forecasts the on-time performance of flights for 15 different airports in the USA based on data collected in 2016 and 2017.

The project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.

Landscape of ML/DL performance evaluation metrics

A dataset containing over 70,000 data points, 12 features, and one target variable were used to analyze if machine learning could predict if an individual has cardiovascular disease.

Analytical tool to help the company decide whether the employee will stay or not

PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS

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.

calculate ROC curve and find threshold for given accuracy

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

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.

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.

Decision Tree classifier using Logistic Regression

This code includes reading the data file, data visualization, variable splitting, model building, prediction and different metrics calculation using knn.

Neural Network

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.

Multi-label AUC plot script

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

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.