Pseudo-labeling for tabular data

Here are the codes for the "Deep Forest classifier for wetland mapping using the combination of Sentinel-1 and Sentinel-2 data" paper.

Use auto encoder feature extraction to facilitate classification model prediction accuracy using gradient boosting models

Machine Learning in Python to assess fire risk in satellite imagery and environmental conditions.

Algerian Forest Fire Prediction

Neural Ocean is a project that addresses the issue of growing underwater waste in oceans and seas. It offers three solutions: YoloV8 Algorithm-based underwater waste detection, a rule-based classifier for aquatic life habitat assessment, and a Machine Learning model for water classification as fit for drinking or irrigation or not fit.

The aim is to find an optimal ML model (Decision Tree, Random Forest, Bagging or Boosting Classifiers with Hyper-parameter Tuning) to predict visa statuses for work visa applicants to US. This will help decrease the time spent processing applications (currently increasing at a rate of >9% annually) while formulating suitable profile of candidate…

This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD).

A machine learning model to predict whether a customer will be interested to take up a credit card, based on the customer details and its relationship with the bank.

Predict the operational status of waterpoints to help the Tanzanian Government provide more clean water to its population using a Machine Learning Classifier

Created Hate speech detection model using Count Vectorizer & XGBoost Classifier with an Accuracy upto 0.9471, which can be used to predict tweets which are hate or non-hate.

This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD) with MRI data.

Example notebooks to produce the models used in the SexEst web application.

The aim of this study is to predict how likely individuals are to receive their H1N1 flu vaccine. We believe the prediction outputs (model and analysis) of this study will give public health professionals and policy makers, as an end user, a clear understanding of factors associated with low vaccination rates. This in turn, enables end users to …

Telco Churn Analysis and Modeling is a comprehensive project focused on understanding and predicting customer churn in the telecommunications industry. Utilizing advanced data analysis and machine learning techniques, this project aims to provide insights into customer behavior and help develop effective strategies for customer

This repository contains five mini projects covering several main topics in Data Mining, such as data preprocessing, clustering and classification.

Supervised ML- Built a Multi-Class classification model to find the relation between features of a mobile phone(RAM, Internal Memory etc) and its selling price. Model will predict the price range indicating how high the price is.

Webapp that predict whether a claim is a fraudulent or not by asking user to put a csv file as mention in schema.json.

This is an end-to-end project that focuses on predicting credit card default using machine learning techniques. The project includes data validation,data preprocessing, model training, evaluation, and deployment.

This research goal is to build binary classifier model which are able to separate fraud transactions from non-fraud transactions.