Batch Name: MIP-ML-11 (Machine Learning Intern)

Files relevant for my bachelor thesis on different automatic emotion recognition approaches

Sign Language Detection & Translation Web App

This repository is about a trained Machine Learning model which predicts Whether the Heart Disease is present or not by considering few factors. This ML model is selected by considering different accuracies of various trained ML models.

Code associated wth the InterpretE research paper

Учебные материалы по курсам связанным с Машинным обучением, которые я читаю в УрФУ. Презентации, блокноты ipynb, ссылки

Implementation of unconstrained and constrained convex optimization algorithms in Python, focusing on solving data science problems such as semi-supervised learning and Support Vector Machines.

I have implemented support vector machine classifier on the same dataset but using different kernels and have compared their accuracies with each other

Built a deep learning-based model to recommend movies based on user sentiment. Extracted data using Twitter API, preprocessed data using NLTK, and built machine learning models using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classification methods. Deployed the model on Airflow/EC2 and stored results in Amazon S3. Achieved 70%

Credit Card Fraud Detection: An ML project on credit card fraud detection using various ML techniques to classify transactions as fraudulent or legitimate. This project involves data analysis, preparation, and use of models like Logistic regression, KNN, Decision Trees, Random Forest, XGBoost, and SVM, along with various oversampling technique.

Python scripts with solutions for different Machine Learning tasks

Kali Linux sanal makinesi kullanarak DDoS saldırılarının simülasyonunu gerçekleştirip, oluşturulan veri seti üzerinde makine öğrenme algoritmaları ile saldırı tespiti ve normal trafikten ayırma.

Dive into the world of Machine Learning in this immersive lab course, exploring open-source tools and algorithms such as random forest, SVM, linear regression, PCA, K-means, LDA, KNN, decision tree, and more. Engage in real-world ML projects and deploy your models, gaining practical experience in the forefront of AI technology.

Coronavirus disease (COVID-19) pandemic Visualization & Prediction

PrognosisHub is a multiple disease predictor.

This code loads network data, preprocesses it, reduces dimensions with an autoencoder, and trains multiple classifiers (KNN, RF, LR, SVM) for anomaly detection.

Code for Tensorflow Machine Learning Cookbook

Machine Learning project - CMP2024 - Computer Engineering - Cairo University

Prediction of loan status based on training ML models on historical data and predict status, complete with thorough Data Visualization and Exploratory Data analysis

Towards evaluation of fairness in MDD models: Automatic analysis of symptom differences for gender groups in the D-vlog dataset