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layamaryjoy/README.md

πŸ‘©β€πŸ’» About Me

Hi πŸ‘‹, I'm Laya Mary Joy

Data Warehouse & ETL Tester | Aspiring Data Scientist | Data & AI Enthusiast

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πŸš€ Professional Summary

  • πŸ’Ό 3.5+ years of experience as a Data Warehouse & ETL Tester
  • πŸŽ“ MCA – CET College Trivandrum
  • πŸŽ“ BCA – BPC College Piravom
  • πŸ“Š Certified in Data Analytics (Skillovilla)
  • πŸ€– Currently pursuing Data Science & Generative AI (Entri Elevate)
  • 🌟 Aspiring Data Scientist

πŸ”­ Current Work

  • Working on Data Warehouse Testing & ETL Validation
  • Handling test planning, execution, defect tracking using JIRA
  • Building and validating data pipelines using Ab Initio

🌱 Currently Learning

  • Advanced Power BI & Excel
  • SQL (Complex Queries & Optimization)
  • Machine Learning & Deep Learning
  • Generative AI & Prompt Engineering
  • Basics of Cloud (AWS, Azure)

🧠 Skills & Expertise

πŸ’» Data & Analytics

SQL Power BI Excel Python

βš™οΈ ETL & Tools

Ab Initio Unix JIRA GitLab Duo WinSCP PuTTY

πŸ—„οΈ Databases

MySQL SQL Server MongoDB


πŸ€– Data Science & AI Experience

  • πŸ“Š Built Student Performance Predictor (ML Project)
    • Deployed using Streamlit
  • πŸ“ˆ Worked on:
    • Supervised & Unsupervised Learning
    • Classification & Regression Models
  • 🧠 Deep Learning:
    • CNN, RNN, Autoencoders
    • Sentiment Analysis
  • πŸ€– Generative AI:
    • Prompt Engineering
    • ChatGPT-based solutions

πŸ“‚ Tools & Platforms Used

  • Ab Initio (ETL Processing)
  • JIRA (Test Management & Defect Tracking)
  • GitLab Duo (AI-assisted development)
  • Unix, PuTTY, WinSCP
  • Excel, Power BI, SQL

🀝 Collaboration Interests

  • Data Analytics Projects
  • ETL & Data Warehousing Solutions
  • Machine Learning & AI Projects
  • Open Source Contributions

πŸ“« Connect With Me

πŸ“§ Email: layamaryjoy98@gmail.com

πŸ“± Phone: +91-7510383657 | +91-8296780932


πŸ“Š GitHub Stats


⭐ "Turning Data into Insights | Passionate about AI & Data Science"

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  1. autoencoder-mnist-implementation autoencoder-mnist-implementation Public

    Autoencoder-based image reconstruction using the MNIST dataset with TensorFlow and Keras.

    Jupyter Notebook

  2. Classification-Algorithms-Model-Building Classification-Algorithms-Model-Building Public

    Implementation and comparison of five supervised classification algorithms using the Breast Cancer dataset from sklearn.

    Jupyter Notebook

  3. Codeathon-Intermediate-Assessment Codeathon-Intermediate-Assessment Public

    Car price prediction using regression models with Random Forest achieving high accuracy and optimized performance through hyperparameter tuning.

    Jupyter Notebook

  4. intel-image-classification-cnn intel-image-classification-cnn Public

    CNN-based image classification using the Intel Image Classification dataset with TensorFlow in Google Colab.

    Jupyter Notebook

  5. Student_Performance_Predictor Student_Performance_Predictor Public

    Predict student performance using ML and Streamlit

    Jupyter Notebook

  6. Weather-Temperature-Prediction-RNN Weather-Temperature-Prediction-RNN Public

    RNN-based weather temperature prediction using time-series data with TensorFlow.

    Jupyter Notebook