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taeefnajib/README.md

taeefnajib

Header

Hey there, I'm Taeef! 👋

Data Scientist

I'm a Data professional, AI Enthusiast and Pythonista with a mission to drive innovation through intelligent solutions and contribute meaningfully to AI research.

💡 What drives me: Turning complex data challenges into elegant solutions that make a real-world impact.

🛠 Core Tech Arsenal

  • Language:
    • Python
    • SQL
    • Spark
  • Core ML Expertise:
    • 🎯 Supervised Learning: Classification, Regression with advanced ensemble methods
    • 🔍 Unsupervised Learning: Clustering, Anomaly Detection
    • ⏱️ Time Series: Forecasting with Prophet, ARIMA & SARIMA
    • 🎬 Recommender Systems: Collaborative & Content-based filtering
    • 🏗️ Feature Engineering: Advanced preprocessing and feature extraction
    • 📊 Model Evaluation: Cross-validation, hyperparameter tuning (Optuna, GridSearch)
    • 🔬 Explainable AI: SHAP, LIME, Facets for model interpretability
  • Advanced Libraries: XGBoost • CatBoost • LightGBM • PyCaret • Prophet

🧠 Deep Learning & Neural Networks

  • Architectures: ANN, CNN, RNN, LSTM
  • Framework: PyTorch for production-ready models

🗣️ Natural Language Processing

  • Libraries: SpaCy, NLTK, Transformers

👁️ Computer Vision

  • Tools: OpenCV, SAM, YOLO for object detection and image processing

🚀 MLOps & Deployment

  • Production Pipeline:
    • 🔄 Version Control: DVC for data versioning. Git/GitHub for collaborative development
    • 📈 Experiment Tracking: MLFlow for model management
    • 🐳 Containerization: Docker for scalable deployments
    • 🌐 API Development: FastAPI, BentoML for REST APIs
    • 📱 Web Apps: Flask, Streamlit, Gradio for interactive dashboards
    • 🐳 Containerization: Docker for scalable model deployment
    • 🔄 CI/CD: GitHub Actions for automated ML pipelines

📊 Data Visualization and Analytics

  • Power BI
  • DAX
  • Tableau
  • Superset

🏗️ Data Engineering

  • Data Pipeline & Orchestration:
    • 🏔️ Data Warehouse: Snowflake for scalable cloud data warehousing
    • 🔄 ELT/ETL: Meltano, Airbyte for data integration and extraction
    • 🛠️ Data Transformation: dbt for analytics engineering and data modeling
    • 📅 Workflow Orchestration: Dagster, Apache Airflow for pipeline management

☁️ Cloud Platforms

  • Cloud Infrastructure:
    • Amazon Web Services (AWS): EC2, S3, Lambda, SageMaker, RDS
    • Google Cloud Platform (GCP): Compute Engine, BigQuery, Cloud ML Engine

🤝 Let's Connect!

I'm always excited to collaborate on innovative projects and discuss the latest in AI/ML! :

LinkedIn

"Data is the new oil, but insights are the refined fuel that powers innovation."
💫 Always learning, always building, always innovating! 💫

Anurag's GitHub stats

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  1. ficto ficto Public

    Ficto is a Python package that allows you to effortlessly generate realistic dummy data in CSV or JSON format.

    Python 17 1

  2. SlickBot---Generate-Viral-Tiktok-Video-Scripts SlickBot---Generate-Viral-Tiktok-Video-Scripts Public

    SlickTok is a web app that lets you generate script for your Tiktok videos that has the potentials to go viral.

    TypeScript 13 2

  3. Aximos Aximos Public

    Aximos is an innovative AI-powered tool that transforms your content into engaging mini-podcasts featuring natural conversations between AI hosts.

    TypeScript 4

  4. customer-churn-prediction customer-churn-prediction Public

    This project includes a ML pipeline to preprocess a dataset of an Internet Service Provider (ISP) and train a model to predict whether a customer will churn or not. It uses hyperparameter optimizat…

    Jupyter Notebook

  5. Sales-Forecasting-Using-ARIMA Sales-Forecasting-Using-ARIMA Public

    In this project, we are going to predict the future monthly sales of Perrin Freres Champagne. The dataset, Perrin Freres Monthly Champagne Sales, is collected from Kaggle, which was posted by MD. M…

    Jupyter Notebook 2

  6. Car-Listing-ELT-Analytics Car-Listing-ELT-Analytics Public

    In this project, I've created an ELT pipeline using DLT, Dagster, Postgres and added interactive dashboard using Superset.

    Python