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.

This app predicts whether you have Diabetes or not, based on a machine learning model trained on the dataset provided by the National Institute of Diabetes and Digestive and Kidney Diseases.

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."

Over the years, the company has collected basic bank details and gathered a lot of credit-related information. The management wants to build an intelligent system to segregate the people into credit score brackets to reduce the manual efforts.. You are hired as a data scientist to build a machine learning model that can classify the credit score.

In my Bangla news categorization project, I utilized XGBoost for efficient pattern recognition, SVM for handling non-linear relationships, and an ensemble of Random Forest, AdaBoost, and Logistic Regression to collectively enhance precision. This diverse approach ensures robust and accurate classification of Bangla news articles.

Ensemble multi-class classifiers on business license status data.

Sentiment Analysis of Lockdown in India During COVID-19:A Case Study on Twitter

This repository contains code and resources for 'Sentiment Analysis of Tweets', predicting the target values negative, neutral or positive. Explored the twitter dataset and used machine learning algorithms svc, MultinomialNB, RandomforestClassifier, and Adaboost Classifier.