🔍 Evaluating Sampling Techniques for Healthcare Insurance Fraud Detection in Imbalanced Dataset.

This project is designed for personal learning and exploration of fundamental machine learning concepts.

Support Vector Machine Classification model is applied on bank dataset containing 41188 rows and 21 columns. The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to assess if the product (b…

"Retail transaction fraud detection project with machine learning models on the Data Mining Cup 2019 dataset."

Expérimentations sur divers modèles et méthodes de Machine Learning pour la classification de textes, et étude des mesures d'évaluation des modèles après normalisation des données

An ML-based project designed to accurately classify email messages as either spam or ham (non-spam)

random forest classification (with hyperparameter tuning) on heart disease dataset.

E-Commerce Customer Churn Prediction using Machine Learning

The primary objective of this study is to develop a dependable and precise prediction model to forecast alterations in Bitcoin's hash rate.

This project explores machine learning techniques, focusing on data preprocessing, model building, and evaluation. It includes data analysis, visualization, various algorithms, and performance comparison. Key topics: data cleaning, feature engineering, model selection, hyperparameter tuning, and evaluation metrics.

Predict the Class of Iris Species

mewto is an R package that allows you to experiment with different thresholds for classification of prediction results in the case of binary classification problems and visualize various model evaluation metrics, confusion matrices and the ROC curve. It also allows you to calculate the optimal threshold based on a weighted evaluation criterion.

Performed model evaluation using evaluation metrics such as accuracy, precision, recall, F1-score etc. Then model interpretation using feature importance, SHAP and LIME. Finally , evaluated model robustness and stability through techniques like bootstrapping or Monte Carlo simulations.

About Machine Learning and Data Analysis on Diamonds Dataset

Successfully established a machine learning model which can determine whether an individual is vulnerable to the Cirrhosis disease or not by predicting its corresponding stage based on a unique set of medical features such as Cholesterol, Prothrombin, etc. pertaining to that person.

"Linear Regression Step by Step" is a repository that provides a comprehensive notebook with step-by-step examples, exercises and libraries to understand and implement Linear Regression easily.

Quality Prediction of red and white wine

Summary Evaluation Tool

CS-GY 6953 Deep Learning Major Project

Coursera Applied Machine Learning in Python