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Python Automation & Data Integration Tools

This repository features a collection of Python-based scripts developed to streamline technical workflows, automate data extraction via APIs, and process large datasets from video systems.

🚀 Key Projects & Use Cases

1. REST API Integration (VCA Analytics)

Developed scripts using the requests library to interface with VCA (Video Content Analysis) REST APIs.

  • Functionality: Automated extraction of real-time metadata and security events.
  • Business Value: Replaces manual data collection, enabling faster system auditing and more accurate reporting.

2. AI-Assisted Data Refinement & Formatting

Leveraged AI and Python to clean, filter, and transform raw data exports into structured formats.

  • Functionality: Processing "messy" data from various sources and converting it into standardized formats (JSON/CSV) for system interoperability.
  • Value: Demonstrates advanced problem-solving by using AI to accelerate data preparation and ensure high data quality.

3. Data Processing & Filtering (Encord)

Custom scripts designed to parse complex data exports from labeling and telemetry systems.

  • Functionality: Reads exported datasets and applies logic-based filtering to isolate specific objects or events based on technical criteria.
  • Value: Transforms raw logs into actionable insights for operational decision-making.

🛠️ Tech Stack

Core

  • Language: Python 3.x
  • Data Handling:
    • pandas: For advanced data structure manipulation and normalization.
    • json / csv: For parsing and saving annotation formats.
    • sqlite3: Built-in engine for high-performance data querying and filtering.

Utilities & Infrastructure

  • API & Communication:
    • requests: For API communication and HTTP protocols.
    • SSE (Server-Sent Events): For handling real-time data streams.
  • Media Management:
    • pytube: For video asset management and processing.
  • UX/UI:
    • tqdm: For real-time progress bar visualization during processing.

📈 Impact on Implementation

These tools were built with modularity and efficiency in mind. They have successfully reduced response times in L2 support environments and simplified the integration handshake between physical security hardware and software platforms.

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