Resampling exercise to predict accuracy, precision, and sensitivity in credit-loan risk

Compared the metrics and performance of different classification algorithms on Heart Failure dataset from UCI ML Repository

Human Resources Analytics

Amex Analyze This is a data science competition held by American Express across all the Indian Institute of Technology Institutes across India. I had participated in this competition in 2018, it was based on predictive modelling where we need to train a model to solve a bank problem - Analyze This 2018

Insurance Cross Sell Opportunity Forecast through machine learning algorithm

Mail SPAM Detector

Identifying Books on Library Shelves using Supervised Deep Learning.

A Swift implementation of mAP computation for Yolo-style detections

Information Retrieval models implemented in Python

(projeto ainda não finalizado) - Este repositório contém um projeto de uma seguradora deseja começar a vender seguro de veículos para clientes que já possuem plano de saúde.

Built a simple search system using Lucene. Indexed 100 text documents using the bbc-news sports dataset. Showed the impact of indexing the data well on precision and recall. Have included the queries used to arrive at the precision and recall.

Supervised Machine Learning and Credit Risk

Submissions for Data Science: Principles, Algorithms, and Applications (CS839) @ UW-Madison

Using supervised machine learning to predict credit risk. Trying oversampling, under sampling, combination sampling and ensemble learning to find the model with the best fit

CNN model to classify garbage

Fraud detection with SMOTE (Synthetic Minority Over-sampling Technique)

Using 21 predictor variables and applying simple Logistic Regression, predicting whether a particular customer will switch to another telecom provider or not. In telecom terminology, this is referred to as churning and not churning, respectively.

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.

Using Collaborative Filtering predicting Movie Rating and K-nearest Neighbours & SVM algorithms for Number ClassificationNumber Classification