Sports betting is the activity of predicting sports results and placing a wager on the outcome.

Predicting Heart Disease with Python and Machine Learning. In this project, in the first part we will explore and prepare the data before starting the Machine Learning models. Let's try to predict which people have heart problems based on personal and health data. we use some Machine Learning models to make the predictions.

This research advances credit card fraud detection by integrating machine learning and deep learning techniques. Key findings include improved model adaptability through hyperparameter tuning.

This project aims to predict whether a person has diabetes or not using key health metrics such as glucose levels and BMI. The project involves data preprocessing, feature selection, model training, evaluation, and prediction using various machine learning algorithms.

AdaBoost Analysis and Optimization [Machine Learning I UC Project]

The AdaBoost (Adaptive Boosting) algorithm is a popular ensemble method used in machine learning to improve the performance of weak classifiers. It combines multiple weak classifiers to create a strong classifier, focusing more on the misclassified instances in each subsequent iteration.

Machine Learning

Machine Learning project - CMP2024 - Computer Engineering - Cairo University

This is a binary classification problem. There are numerous factors that can contribute to the presence of heart disease. What is the most important factor causing heart disease? Can an accurate classifier be built to predict the presence of heart disease in patients? These are the questions we want to answer with this project.

Deep Learning vs Tranditional ML methods for TB Drug Resistance prediction from Genomic data

Sentiment140 dataset with 1.6 million tweets

learning python day 15

The project provides the credit card approval predictor using different machine learning algorithm , just like the real banks do.

learning python day 14

This repository contains code for a machine learning project focused on predicting the likelihood of a person having diabetes. The project includes the implementation of various classification models and an Artificial Neural Network (ANN) for classification.

With this model: the amount of backlog would be reduced significantly, the amount of staff needed to do the job would be reduced drastically, the processing time would be shortened significantly and more cases of fraudulent transactions would be tracked down in a given amount of data processed - more than 40% increase in efficiency!

Spam Email Detection using Machine Learning Classifier Algorithms

Machine Learning - Practical assignment

"Heart disease and diabetes prediction accuracy through Bagging and AdaBoost ensemble methods for enhanced predictive performance."