Loan Eligibility Prediction Model: A machine learning application to predict loan approval based on applicant data. Includes a web interface for submitting loan applications and receiving predictions. Built with Python and Jupyter Notebook.

Simple and flexible classical ML module that can be used for recording baseline ML performance.

This project detects spam messages in SMS, including those written in regional languages typed in English. It uses an extended SMS dataset and applies the Monte Carlo method with various supervised learning algorithms to improve spam detection.

Ce projet est une proposition de solution au Rakuten Data Challenge. L'objectif est de mettre en œuvre différentes méthodes de Machine Learning et Deep Learning pour résoudre le problème.

This project analyzes employee attrition data to uncover key factors, predict turnover, and develop strategies for retention, ultimately enhancing organizational stability and performance.

a predictive model for bankruptcy risk assessment using machine learning algorithms and financial indicators extracted from company financial statements

This project evaluates logistic regression, random forest, decision tree, and gradient boosting classifier models for fake news detection. Using labeled data, it analyzes accuracy, confusion matrices, and ROC curves to understand each model's effectiveness in discerning between real and fake news.

This project aims to predict bank customer churn using a dataset derived from the Bank Customer Churn Prediction dataset available on Kaggle. The dataset for this competition has been generated from a deep learning model trained on the original dataset, with feature distributions being similar but not identical to the original data.

Using various machine learning models (Logistic Regression, Gaussian Naïve Bayes, KNN, Gradient Boosting Classifier, Decision Tree Classifier, Random Forest Classifier.) to predict whether a company will go bankrupt in the following years, based on financial attributes of the company; Addressed the issue of imbalanced classes, different importance

A machine learning application, deployed using Flask, is designed to identify the presence of kidney disease in patients by analyzing various medical features.

This project aims to predict breast cancer using machine learning and deep learning techniques.

Titanic Kaggle.

Notebooks and references for the submission to SnakeCLEF, 2021 edition.

This project aims to predict the occurrence of diabetes using machine learning techniques. The dataset used for this analysis is the "diabetes_prediction_dataset.csv" file, which contains various features related to an individual's health condition.

Scripts, figures and working notes for the participation in FungiCLEF-2022, part of the 13th CLEF Conference, 2022

Different ML Algorithms both in scripts & Jupyter Notebooks

Credit Fraud Detection of a highly imbalanced dataset of 280k transactions. Multiple ML algorithms(LogisticReg, ShallowNeuralNetwork, RandomForest, SVM, GradientBoosting) are compared for prediction purposes.

Telecom Customer Churn Analysis & Prediction project uses Gradient Boosting for precise predictions, Power BI for churn pattern visualizations, and Streamlit for interactive insights. With robust code and meticulous data preprocessing, stakeholders access accurate predictions to optimize retention and drive profitability.

Employees Attrition prediction and identifying its main influencing features.

Applying boosting techniques such as GradientBoostingClassifier, GradientBoostingRegressor (eXtreme Gradient Boosting (XGBClassifier and XGBRegressor functions). This repo contains class examples and the project.