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๐ŸŽฏ Passionate AI/ML Engineer | ๐Ÿ Python Expert | ๐Ÿง  Deep Learning Enthusiast


About Me

class TarangKishor:
    def __init__(self):
        self.name = "Tarang Kishor"
        self.role = "AI/ML Engineer"
        self.location = "New Delhi, India ๐Ÿ‡ฎ๐Ÿ‡ณ"
        self.education = "B.Tech (pursuing)"
        self.languages = ["Python", "JavaScript", "SQL"]
        
    def get_expertise(self):
        return {
            "AI/ML": ["Deep Learning", "Computer Vision", "NLP", 
                      "Multi-Agent Systems", "MLOps"],
            "Frameworks": ["TensorFlow", "PyTorch", "scikit-learn",
                          "LangChain", "CrewAI", "XGBoost"],
            "Web": ["Flask", "FastAPI", "HTML", "CSS", "JavaScript"],
            "Data": ["Pandas", "NumPy", "Matplotlib", "Seaborn"],
            "Tools": ["Git", "Docker", "Jupyter", "VS Code"],
            "Databases": ["MySQL", "PostgreSQL", "MongoDB"]
        }
    
    def current_focus(self):
        return [
            "Building Smart Incident Prevention AI Agent",
            "Learning Generative AI & Advanced NLP",
            "Contributing to Open Source AI projects",
            "Exploring Vision Transformers & Multi-Agent AI"
        ]

me = TarangKishor()

Connect With Me

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๐Ÿ”ฅ Featured Projects

Multi-agent AI system that transforms enterprise incident management by automating detection, analysis, and response workflow.

Tech Stack: LangChain โ€ข CrewAI โ€ข Python โ€ข Multi-Agent AI

โญ 3 stars

Comprehensive AI-powered sentiment analysis platform combining machine learning and advanced NLP features.

Tech Stack: Python โ€ข NLP โ€ข Machine Learning โ€ข Flask

๐ŸŽฏ 86% Accuracy | โญ 1 star

Full-stack ML-driven Flask app that helps evaluate Indian property listings using XGBoost classifier.

Tech Stack: Flask โ€ข XGBoost โ€ข Python โ€ข ML

๐Ÿ“Š 250K+ Records | โญ 1 star

Hybrid Attention Vision Transformer combining global self-attention with local convolutional attention for medical images.

Tech Stack: Python โ€ข Vision Transformers โ€ข Deep Learning

โญ 1 star

๐Ÿ”ฅ More Awesome Projects

Deep learning solution for Birds vs Drones classification using advanced Computer Vision techniques.

๐Ÿ’ผ ML-Powered ATS

Machine Learning powered Applicant Tracking System for intelligent resume screening.

Deep Learning model for MRI Brain Tumor Classification with high accuracy (โญ2 stars).

Financial risk assessment platform with MLflow experiment tracking for EMI prediction.

ML-based system to identify and classify fish species from images.

Predict delivery times based on distance, traffic, and shipping method.

Data-driven system connecting food providers with receivers to minimize waste.

ML model to classify toxicity in online comments using NLP.

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๐Ÿ† GitHub Trophies

Random Dev Quote


๐ŸŒŸ Show some โค๏ธ by starring some of my repositories!

โœจ "Code is like humor. When you have to explain it, it's bad." โ€“ Cory House


Thanks for visiting! Let's connect and build something amazing together! ๐Ÿš€

Popular repositories Loading

  1. Smart-Incident-Prevention-Agent Smart-Incident-Prevention-Agent Public

    A multi-agent AI system that transforms enterprise incident management by automating the detection, analysis, and response workflow while maintaining human oversight for critical decisions. Built wโ€ฆ

    3

  2. Leaderboard-API Leaderboard-API Public

    A production-grade **real-time leaderboard REST API** built with FastAPI โ€” powered by a **skip list data structure implemented from scratch**, with no Redis or external sorted-set library.

    Python 3

  3. About-Myself About-Myself Public

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  4. Flipkart-CSAT-Prediction Flipkart-CSAT-Prediction Public

    ML project to predict Flipkart customer satisfaction using NLP & XGBoost

    Jupyter Notebook 2

  5. MRI-Classifier MRI-Classifier Public

    MRI Brain Tumor Classification using Deep Learning

    Jupyter Notebook 2

  6. Shopper_Spectrum_Analysis Shopper_Spectrum_Analysis Public

    This project analyzes online retail transaction data to understand customer behavior and segment customers based on their purchasing patterns. We'll use RFM (Recency, Frequency, Monetary) analysis โ€ฆ

    Jupyter Notebook 2