This e-commerce app is built using modern Android development practices with Kotlin and Jetpack Compose. It includes features like MVVM architecture, Room database, Hilt for dependency injection, Retrofit for API calls, and Firebase for authentication, analytics, and notifications. It leverages Coroutines and Flow for asynchronous programming...

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Tiểu Luận Chuyên Ngành

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A revamped website for TLC Computer Solutions, a tech support store in Las Vegas.

This project automates TLC analysis by capturing and processing TLC plate images, saving time and improving accuracy. We utilize image recognition techniques to measure distances on TLC plates and automatically store the data in an MES.

ETL pipeline for NYC TLC Trip Record Data using Microsoft Fabric Lakehouse, Data Factory, Dataflows, and SQL Stored Procedures.

Laravel Lumen API for querying and analyzing NYC TLC yellow taxi trip data stored in Parquet files via PHP

A CLI tool to batch-process NYC TLC yellow taxi trip data, optimize delivery routes, and store results in SQLite.

First app I ever built. Probably needs some TLC. This was released to the app store when I was a Junior in College

This project implements a data engineering pipeline to ingest, clean, and transform New York City Taxi and Limousine Commission (TLC) trip data using Apache Spark and Apache Airflow, storing processed data in a Lakehouse for efficient querying and reporting.

An end-to-end data pipeline built to process the massive and complex NYC TLC taxi dataset (~4 billion records). It uses Python, Polars, and Airflow to solve the core challenge of evolving data schemas. The final output is a clean, unified dataset stored in yearly-partitioned Parquet files, optimized for analysis.

This repository contains a data ingestion pipeline that fetches Trip Record Data from the Taxi and Limousine Commission (TLC) in New York, processes it using Dagster, and stores it in AWS S3. The pipeline is designed to automate the data ingestion process, making it easy to collect and store the data for further analysis and use.

The competition dataset is based on the 2016 NYC Yellow Cab trip record data made available in Big Query on Google Cloud Platform. The data was originally published by the NYC Taxi and Limousine Commission (TLC). The data was sampled and cleaned for the purposes of this playground competition. Based on individual trip attributes, participants should predict the duration of each trip in the test set. File descriptions train.csv - the training set (contains 1458644 trip records) test.csv - the testing set (contains 625134 trip records) sample_submission.csv - a sample submission file in the correct format Data fields id - a unique identifier for each trip vendor_id - a code indicating the provider associated with the trip record pickup_datetime - date and time when the meter was engaged dropoff_datetime - date and time when the meter was disengaged passenger_count - the number of passengers in the vehicle (driver entered value) pickup_longitude - the longitude where the meter was engaged pickup_latitude - the latitude where the meter was engaged dropoff_longitude - the longitude where the meter was disengaged dropoff_latitude - the latitude where the meter was disengaged store_and_fwd_flag - This flag indicates whether the trip record was held in vehicle memory before sending to the vendor because the vehicle did not have a connection to the server - Y=store and forward; N=not a store and forward trip trip_duration - duration of the trip in seconds

# NYC-taxi-trip-time-prediction The competition dataset is based on the 2016 NYC Yellow Cab trip record data made available in Big Query on Google Cloud Platform. The data was originally published by the NYC Taxi and Limousine Commission (TLC). The data was sampled and cleaned for the purposes of this playground competition. Based on individual trip attributes, participants should predict the duration of each trip in the test set. Here is the presentation link: https://github.com/soumya1999rta/NYC-Taxi-Trip-Prediction/blob/main/NYC_TAXI_TRIP_TIME_PREDICTION_Soumya_Ranjan_Mishra.pdf # Data fields id - a unique identifier for each trip **vendor_id **- a code indicating the provider associated with the trip record **pickup_datetime** - date and time when the meter was engaged **dropoff_datetime** - date and time when the meter was disengaged **passenger_count** - the number of passengers in the vehicle (driver entered value) **pickup_longitude** - the longitude where the meter was engaged **pickup_latitude** - the latitude where the meter was engaged **dropoff_longitude** - the longitude where the meter was disengaged **dropoff_latitude** - the latitude where the meter was disengaged **store_and_fwd_flag** - This flag indicates whether the trip record was held in vehicle memory before sending to the vendor because the vehicle did not have a connection to the **server** - Y=store and forward; N=not a store and forward trip **trip_duration** - duration of the trip in seconds