Name: Praveen Liyanage
Type: User
Company: Bluechip Technologies Asia
Bio: I'm an IT student, is passionate about AI and ML and currently works as an AI and ML engineer.
Twitter: PraveenLiyan8
Location: Chilaw, North Western, Sri Lanka
Blog: codefixdp.wordpress.com
Praveen Liyanage's Projects
Here you can find twenty google colab files written for Deep Learning use cases
A project leveraging machine learning to forecast airline delays. Features a user-friendly Flask web interface for inputting flight details and receiving delay predictions.
Amazon stock prices using a Long Short-Term Memory (LSTM) neural network implemented with PyTorch. This project leverages deep learning techniques for accurate time series forecasting.
This repository contains a comprehensive analysis of unemployment trends during the COVID-19 pandemic. The analysis is conducted using Python in a Google Colab notebook (.ipynb file). Key features include data cleaning, visualization, and statistical analysis to understand the impact of COVID-19 on unemployment rates.
A Flask-powered platform delivering real-time data visualization and information on the COVID-19 pandemic.
K-Means clustering implementation for custom segmentation tasks, allowing users to group data points based on similarity for personalized analysis and insights.
EcoPick is a modern online garbage management system that incorporates live tracking, sentiment analysis feedback, and robust user and driver management functionalities.
react_budget_app
Emotion detection Neural Language Processing project build with Flask web framework
Explore the Face Recognition with OpenCV project, a showcase of computer vision prowess. Witness real-time face recognition using OpenCV, blending Haarcascades for precise detection with a simple and extensible recognition algorithm.
This is an application developed as a social media platform based suitable for add fitness routines
Developed using Kotlin
This is a Python project utilizing PyTorch and artificial neural networks for heart failure prediction, analyzing datasets and predicting likelihood based on input features.
This project Implements a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to generate descriptive captions for input images.
This project employs CNN for accurate image classification, leveraging deep learning techniques to recognize and categorize visual content efficiently.
This project features Image Style Transfer using the VGG19 neural network. Style transfer merges the artistic style of one image with the content of another. Leveraging the powerful VGG19 convolutional neural network, it excels in image-related tasks.
This is the project in mobile application development, developed by using Kotlin
This repository contains a Google Colab notebook that demonstrates the classification of the Iris Flower dataset using Logistic Regression. The notebook includes data exploration, preprocessing, model training, evaluation, and visualization of results.
This is a machine learning project that predicts kidney disease based on clinical and laboratory features, enabling users to build and deploy predictive models.
This project aims to provide optimal solutions to a bank by cautioning against the risks associated with loan approvals for businesses. It involves exploratory data analysis, data cleaning, feature engineering, and the application of classification models to predict loan defaulters. The project achieves a 92% accuracy rate in predictions.
A project created using MERN Stack for Module Management function
This project employs the VGG16 neural network for accurate and efficient detection of brain tumors in MRI images.