vb100/KMeans-FlaskApp

Stars
1
Language
Python
Created almost 3 years ago
Updated over 2 years ago

vb100/KMeans-FlaskApp

vb100

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Transform Python script for K-Means into Flask application

multivariate-lstm

Jupyter Notebook

Pneumonia-X-Rays-of-Human-Lungs-AI-project

This projects includes both back-end (Keras, Flask with dependencies) and front-end/deployement (HTML, CSS, JS) parts. The project perform X-rays of human lungs classifiaction using Convolutional Neural Networks with automated image pre-processing and internal procedures. Saved files of inspected models are saved on specific folders in project directory.

Jupyter Notebook

deploy-ml-mlflow-aws

Deploying Models to Production with Mlflow and AWS Sagemaker

Visualize-3D-MRI-Scans-Brain-case

The notebook introduces how to visualize 3D MRI data (Brain Scans case)

Jupyter Notebook

AnomaliesDetection-with-TimeSeriesAnalysis

This is an experimental of anomalies detection by applying different approach to the problem. PCA component regularization method, K-Mean Clustering, SVM and Gausian Distribution models has been used to detect anomalies on time series data.

Jupyter Notebook

whisper_ai_finetune

Fine-tune WhisperAI model to your language

Tensorflow-ANN-TimeSeries-Forecasting-Weather

This notebook demonstrates how to implement Tensorflow & queries to forecasting for a very big dataset. ANN with 2 hidden layers has been used for ANN. The data shifting technique has been used to generate Train and Test (validation) sets.

Jupyter Notebook

Yahoo-Finance-Stock-Scrapper-Selenium

This Selenium based Scrapper gather historical prices of stocks that are defined in the external CSV file, collect all the price values with it's timestamp, then structuring data and write to Excel file with readable data structure where timestamp correspond to all stocks that has a price value at that day. All scrapping proccess takes even few day/nights because of Selenium speed and set delay time. Algorithm made by Python. © Vytautas Bielinskas 2018

ml-ops-ci

MLOps-Notes

Machine Learning Operations (MLOps) Noted collected from various sources

mlops-track-perf-branches

Deploy_Kubernetes_AWS_EC2

This manual explain how to setup AWS EC2 instance and set-up a Kubernetes Cluster inside

openai-test-api

Test OpenAI API: text completion, code completion with Codex, and image generation with DALLE-E

Booking.com-Price-Scrapper-Selenium

This Python algorithm will gather data of prices for 365 next days for specific chosen hotel on Booking.com. There can be some bug for testing on different properties but for our target property is worked perfectly. (© Vytautas Bielinskas)

aws-cicd-pipeline

Experimenting on CICD and Terraform

langchain_pydantic

Example of using LangChain with Pydantic to get output in specific data format in Python

ImageSimilarityApp

This app. perform image similarity calculations for a given image.

Jupyter Notebook

Ensemble-Learning-Testing-Classifiers

Testing and Exploration of Ensemble Learning with poor dataset (66 obs). Results are provided at the end of the Notebook.

Jupyter Notebook

XgBoost-machine-Learning-Template-including-Hyperparameter-Tuning

This Machine Learning algorithm [Python] evaluate data features and deliver it's importances with the best set of hyperparameters of XgBoost Classifier. It has been implementing by Hyperparameter Tuning. Easy adaption to any othet ML algorithm. © Vytautas Bielinskas

immobilienscout24.de-Scrapper

This Python based Web Scrapper gather all possible values of real estate properties features from www.immobilienscout24.de that is the biggest Real Estate web listing page in Germany Real Estate market. This scrapper gather data for rental properties. Suprising is that only BeautifulSoup was enough to build this scrapper. And magical thing - Geocoding by connection to Google Maps API is implementing inside this Python code! © Vytautas Bielinskas 2017-2018

github-action-to-ecr

Repo demonstrating how to create an AWS ECR Image with Github Action

XgBoost-Evaluate-Data-Features-Machine-Learning

This code uses innovative XgBoost Machine learning algorithm that analyze data features and evalute it's importances in order to make data driven solutions in real estate. © Vytautas Bielinskas

Zoopla-Scrapper-by-Selenium

This Python made web scrapper uses Selenium and BeatufulSoup to gather data of rental propertien from one of the biggest real estate listings website in UK. Just paste the search URL and click run. There are another scrapper for Sales and improved scrapper to collect some historical data. © Vytautas Bielinskas 2017

Deep-Learning---Recognize-Infected-Cells-by-Malaria--Adam--Sigmoid-

The project contain folders for labeled infected and non-infected cells by malaria. The code define an 4 layers ANN that learning to recognize infected cells by a given shuffled examples from working directory and provide a learning curve as output. For optimization Adam and MiniBatch methods have been applied (Tensorflow).

Real-Estate-Data-Scrapper-Structuing-UpdatingExcel

This is improved Python algorithm that replace Machine learning (ML) approach to identyfing comparative ones properties from big mass of scrapped data. Now this algorithm changed and the biggest consideration is under Natural Language Proceesing (NLP): to weight keywords in description blocks and sort the most suitable properties to the top of list and update existing Excel. If you have 1000 or more scrapped properties it can save 1 full day of work. The big attention was paid to clear and readable Python code. © Vytautas Bielinskas

Booking.com-Data-Scrapper-Selenium

This Python made web scrapper gather all possible data (features) of properties in chosen area in ant place of the world. Due to dynamic content that is delivered by JavaScript scripts, Selenium was choosen as the best Python libary to gather such data. © Vytautas Bielinskas 2017

langchain_sequentialchain_demo

This code is an example of LLM LangChain SequentialChain usage in Python

spark_ml_train_model

This repo contains files required to follow example on Youtube which shows how to train Spark ML model from scratch

Jupyter Notebook