Slovenija - Register prostorskih enot / Slovenia - Register of Spatial Units (CC-BY 4.0, Geodetska Uprava RS)

This plugin will enable Apache JMeter users to have similar parameter advantage as LoadRunner

CTU13 CSV Dataset contains Botnet, Normal and Background traffic

Dutch postcodes in CSV format (7zip) and MySQL import script

Edexcel IAL Grade Boundaries Dataset (2015–2025)

A dossier on the ROFF page formatting and typesetting language

An open-source global airports database providing structured airport data in CSV format for developers, researchers, and aviation enthusiasts.

A web app to check your ML model performance on the Term Deposit Prediction dataset by Brajesh Mohapatra.

A proof of concept of a recursion doing stochastic gradient descent for a simple neural network. Done in Python3 with numpy

Dutch postcodes in CSV format (7zip) and MySQL import script (with geo)

This project helps students to predict which colleges and branches they might get based on their EAMCET rank, category, and gender using a dataset..

Rule-based NLP chatbot using TF-IDF & cosine similarity over 1000+ real-world QnA — fast, offline & LLM-free.

Open dataset of countries and territories, including ISO codes, names, regions, and other relevant data for aviation and global applications.

📈 Predict and forecast Apple stock prices using a Stacked LSTM model for accurate stock market insights and decision-making.

A historical dataset of USD to Iranian Rial rates collected from TGJU (Tehran Stock Exchange). This dataset contains Nima Buy USD rates from 1397 to 1404 (Persian calendar).

This is a Titanic Survival Prediction Model developed using Python, Pandas, Scikit-learn, and Jupyter Notebook. The model predicts whether a passenger survived the Titanic disaster based on features such as age, gender, and passenger class.

Stock price prediction and forecasting using Stacked LSTM deep learning on Apple (AAPL) historical data.

This project aims to predict the likelihood of heart disease in patients using machine learning algorithms. It is developed in Python using Jupyter Notebook and trained on a dataset containing clinical features like age, sex, cholesterol level, blood pressure, and more. The goal is to help in early diagnosis and risk assessment.

This project shows how to use a special type of AI called Long Short-Term Memory (LSTM) to predict stock prices. The project is split into two main parts: Training the AI Model and Making Predictions (Inference)