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Farhan71140/README.md

Typing SVG


〔 01 〕 — WHO AM I?

class FarhanUddin:
    def __init__(self):
        self.name       = "Mohammed Farhan Uddin"
        self.role       = "Python Full-Stack Developer"
        self.location   = "Hyderabad, India 🇮🇳"
        self.education  = "B.Tech CSE @ MRIT (2021-2025)"
        self.email      = "mohdfarhanuddin002@gmail.com"

    @property
    def currently(self):
        return [
            "🔨 Building scalable REST APIs",
            "📖 Mastering FastAPI + Django",
            "☁️  Exploring AWS Architecture",
            "🤖 Working on AI-powered backends",
        ]

    @property
    def ask_me_about(self):
        return ["Python", "FastAPI", "REST APIs",
                "MySQL", "Backend Design"]

    def __str__(self):
        return "Clean code. Fast APIs. Zero excuses."


〔 02 〕 — TECH ARSENAL

⚙️ Languages

Python JavaScript C

🚀 Backend

FastAPI Django REST API

🗄️ Databases

MySQL PostgreSQL

🎨 Frontend

HTML5 CSS3 Bootstrap

🛠️ Tools & Cloud

Git GitHub Postman AWS VS Code


〔 03 〕 — PROJECTS

🛍️ Madam Choice Footwear — Full-Stack E-Commerce App

Stack: FastAPI MySQL HTML CSS Bootstrap

Metric Result
🗃️ Products Managed 50+ records with real-time cart & order tracking
⚡ API Response Time Reduced by 35% via efficient routing & middleware
🔍 Database Speed Improved by 40% via indexed SQL optimization
📱 Page Load Time Reduced by 30% with mobile-first Bootstrap UI
🔌 REST APIs Built 10+ covering auth, products, orders
💰 Expense Tracker Application — Backend System

Stack: FastAPI MySQL REST APIs Python

Metric Result
📊 Transactions Handled 100+ with multi-category filtering
⏱️ API Response Time Consistent sub-200ms across all endpoints
✅ Uptime During Testing 99% — zero downtime
🤖 Manual Effort Saved 60% via automated report generation
🗄️ Data Redundancy Reduced by 45% with normalized schema
🤖 Assessment Recommendation Engine — AI Backend

Stack: FastAPI Semantic Search Python ML

Metric Result
🎯 Relevance Accuracy 85%+ using semantic search
⚡ Response Time Reduced by 50% via caching & pre-processing
📦 Data Processed 1,000+ records cleaned and structured
📈 Recommendation Precision Improved by 30% over keyword-matching baseline
🔐 Cybersecurity Threat Detection — ML System

Stack: Python Machine Learning Feature Engineering Ensemble Methods

Metric Result
🎯 Detection Accuracy 92% on validation datasets
🗂️ Records Processed 5,000+ network activity records
📉 Feature Dimensionality Reduced by 35% without performance loss
🛡️ False Positive Rate Reduced by 28% via hyperparameter tuning

〔 04 〕 — GITHUB STATS


〔 05 〕 — CERTIFICATIONS

🏆 Certificate 🏢 Issuer 📅 Year
☁️ AWS APAC Solutions Architecture Forage 2026
🤖 Retrieval-Augmented Generation for AI IBM SkillsBuild 2025
🐍 Python Programming Fundamentals HackerRank 2025

〔 06 〕 — CONNECT

Gmail GitHub LinkedIn


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