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🎯
Learning and Implementing
🎯
Learning and Implementing
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Achievement: Pull Shark

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Achievement: Pull Shark

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13-Prabhat/README.md

🧭 About Me

class Prabhat:
    def __init__(self):
        self.role        = "Third-year B.Tech CSE Student"
        self.university  = "Galgotias University"
        self.location    = "New Delhi / Bihar, India"
        self.focus       = "Ethical Hacking & Cybersecurity"
        self.currently   = ["Python", "Java", "Data Structures & Algorithms", "Bash"]
        self.building_in = "Open Source + Data Science"

    def say_hi(self):
        print("Thanks for stopping by — let's build something!")

me = Prabhat()
me.say_hi()

🎯 Current Goals

  • 🔐 Building deep, practical skills in ethical hacking & cybersecurity
  • 🧑‍💻 Growing a portfolio through open-source contributions
  • 📊 Shipping end-to-end data science projects
  • 🎓 Strengthening fundamentals through active coursework & exam prep

🌱 Currently Learning

  • Java · Data Structures & Algorithms
  • Git & GitHub workflows
  • Bash scripting & backend fundamentals
  • Security tooling & CTF-style problem solving
🕵️ Click to reveal a fun fact
I'm most productive at 2 AM debugging something that worked perfectly fine five minutes ago. 🐛☕

🚀 Featured Project — My Original Build

📊 E-commerce Customer Behavior Analysis & Segmentation

Fully designed and built end-to-end — not a fork, not an issue fix.

Python Streamlit License Stars

Analyzes real e-commerce transaction data (the UCI Online Retail dataset) to understand customer buying behavior end-to-end — cleaning, EDA, RFM (Recency, Frequency, Monetary) analysis, K-Means clustering, and a live Streamlit dashboard that turns raw transactions into segments a marketing team could actually act on.

🔍 Click to expand: how it works
Stage What Happens
🧹 Data Cleaning Handles missing values, duplicates, and type conversions on raw retail transactions
📈 EDA Visualizes purchase patterns, order frequency, and revenue distribution
🧮 RFM Engineering Computes Recency, Frequency, and Monetary scores per customer
🎯 Segmentation K-Means clustering (Elbow Method + Silhouette Score) groups customers into segments like Champions, At Risk, and Hibernating
📊 Dashboard A Streamlit app makes the segments explorable interactively

Debugging note: this project went through multiple audit cycles across the pipeline, the app, and the notebooks — including tracking down a sneaky .gitignore issue that was silently blocking deployment to Streamlit Community Cloud.

🛠️ Other Original Builds

🏙️ civicFix Snap a photo of a pothole, broken streetlight, or overflowing bin — tag it, put it on a map, and let the community upvote it so authorities stay accountable.

🍽️ SmartDineQR Scan, order, and pay at a restaurant table with no waiters and no printed menus.

📊 GitHub Stats


🐍 Contribution Snake

Snake animation

🧰 Tech I Work With

🤝 Let's Connect

LinkedIn GitHub

⭐ If any of this caught your eye, a star on the project means a lot!

Pinned Loading

  1. E-commerce-Customer-Behavior-Analysis-Segmentation E-commerce-Customer-Behavior-Analysis-Segmentation Public

    This project analyzes online store data to understand customer buying behavior, identify key patterns, segment customers, and generate insights that help improve marketing, retention, and revenue.

    Python 1

  2. civicFix civicFix Public

    See a pothole, broken streetlight, or overflowing bin? Snap it, tag it, and let CivicFix bring it to the map — where communities upvote issues and authorities stay accountable.

  3. Library_Management_System Library_Management_System Public

    A simple tool that helps a library keep track of its books, who borrowed them, and when they’re due. It makes checkouts easy, returns smooth, and helps staff find information fast.

    Java

  4. Homonid Homonid Public

    Homonid transforms AI-generated text into natural, human-like writing with adjustable styles like academic, classic, and more for smoother, less detectable content.

    HTML

  5. SmartDineQR SmartDineQR Public

    Smart Dine QR lets you scan, order, and pay at restaurants with ease—no waiters, no printed menus.

    Java