Nijat Zeynalov's Projects
Developing, evaluating and monitoring popular XGBoost model.
HalvingGridSearch, HalvingRandomSearch, Bayesian Optimization, Keras Tuner, Hyperband optimization
The objective of this project is to discover insights into consumer reviews.
In this notebook I’ll use the HMP dataset and perform some basic operations using Apache SparkML Pipeline component. This dataset is a public collection of labelled accelerometer data recordings to be used for the creation and validation of acceleration models of human motion primitives.
Scraping Azerbaijani real estate website and sending whatsapp message with AWS Lambda function that automatically triggered by Amazon CloudWatch.
Azerbaijani School Graduate Enrollment Indicators dataset (1995-2023)
azcorpus - The largest NLP corpus for Azerbaijani (1.9M documents, ~ 18M sentences)
The aim of this project is to generate fake news in the Azerbaijani language using LSTM Recurrent Neural Networks. LSTM Recurrent Neural Networks are powerful Deep Learning models which are used for learning sequenced data. Here a LSTM model was trained on 65 thousand samples, and it should be able to generate text.
This project demonstrates how to use OpenSearch with k-NN (K-Nearest Neighbors) search to perform semantic searches on Azerbaijani legal document
Azerbaijani Medical Forum Question Classification
Azerbaijani News Summary Dataset
AzSpeech is a comprehensive voice dataset curated by the Alas Development Center, consisting of over 1000 hours of diverse voice recordings, totaling more than 400,000 individual voice files.
AzVoiceSent is research project focused on sentiment classification from voice transcriptions in Azerbaijani. The project has the potential to provide valuable insights into the sentiment expressed by speakers in various domains and applications.
Multi-classificatiion of boats using CNN
Predict whether the cancer is benign or malignant by using KNN
I have built simple versions of some Neural Network architectures (Alexnet, Inception-v1, Resnet-18, Vgg-16) from scratch by using TensorFlow.
OpenAI's CartPole-v1 environment.
Classifying Diabetes using Artificial Neural Networks
Classify traffic signs using CNN
Cleaning Text Manually and with NLTK.
In the project, I have detected concept drift by using adversarial validation and Kolmogorov-Smirnov test which can also be used in the deployed system.
The paper mainly describes the implementation of the Multilayer Perceptron (MLP) model - that can be used to detect sentiments from the text.
I have used Object Detection API and retrain RetinaNet model to spot weapon objects using just 4 training images.
Document Similarity Search with qdrant
This telegram bot will find easy recipes in Azerbaijani using ingredients you already have in the kitchen.
Ensemble models in machine learning combine the decisions from multiple models to improve the overall performance
Automate your classic machine learning experiments with experimenteer.