Most popular metrics used to evaluate object detection algorithms.

This is the official implementation for the Generative Modeling Density Alignment (GMDA). This work was presented in the paper "Frugal Generative Modeling for Tabular Data" at ECML 2024.

📔 This repository delves into Logistic Regression for loan approval prediction at LoanTap. It covers data preprocessing, model development, evaluation metrics, and strategic business recommendations. Explore model optimization techniques such as confusion matrix, precision, recall, Roc curve and F1 score to effectively mitigate default risks.

Evaluation of 3D detection and diagnosis performance —geared towards prostate cancer detection in MRI.

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 repository contains code for classifying galaxies into three classes: Galaxy, Quasar, and Star, using machine learning techniques. The dataset used in this project is the Sloan Digital Sky Survey (SDSS) dataset.

CNN model to classify garbage

Information Retrieval models implemented in Python

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

Classification problem using multiple ML Algorithms

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.

Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding box formats as in COCO, PASCAL, Imagenet, etc.

Human Resources Analytics

Trained MATLAB models for 82% precision/80% recall, optimized with blob analysis for 25% performance boost. User-friendly alarm system with 500+ engaged users.

The objective of this analysis is to find patterns within the dataset to gain further understanding of the data and leverage it to choose a machine learning algorithm that can recommend a suitable profile for the applicants whose visa should be certified or denied

The goal of this project is to develop a machine learning model that can help banks to identify customers who are likely to churn and take appropriate measures to retain them

ML-FinFraud-Detector is a machine learning project for detecting financial transaction fraud. Utilizing XGBoost, precision-recall, and ROC curves, it provides accurate fraud detection. Explore feature importance, evaluate model performance, and enhance financial security with this comprehensive fraud detection solution.

Lead scoring case study

BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagg…