RIDE helps you with rapid data prototyping with just few clicks. Upload your dataset, explore multifaceted data analytics, visualize trends, and leverage machine learning, all backed by the power of AI and No-Code environment.

An open-source package built on the Polars Library, for data inspection, exploration, visualization, and preprocessing tasks directly on the terminal. Supports multiple file formats (.csv, .excel, .parquet).

Achieved Top 24% globally in Kaggle competition, implementing Time Series Forecasting models with 98.44% accuracy in predicting microbusiness forecasting.

Deep learning project analyzing La Liga soccer data, featuring interactive visualizations and match outcome predictions using PyTorch. Created a comprehensive dashboard for team performance analysis.

Explored machine learning algorithms for detecting fraudulent transactions using the American Express Kaggle dataset. Implemented XGBoost, CatBoost, and logistic regression for accurate fraud prediction.

Developed a comprehensive database management system utilizing both SQL (MySQL) and NoSQL databases (MongoDB, ArangoDB, Neo4j) to handle diverse data types and optimize specific functionalities.

Implemented parallelized sorting algorithms and conducted scaling tests on High Dimensional Data, focusing on performance optimization and efficiency.