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Achievement: Pull SharkAchievement: Quickdraw

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JaleesMoeen/README.md

AI

Hello 👋

Welcome to visit Jalees Moeen

Hey there, it's Jalees Moeen!

My professional experience includes over two years as a IT Analyst at Toyota Motor Corporation and several months as a IT Support Execuitve at The Grand Atrium.

🌟 Got thoughts to share? Whether it's data, tech, or just a friendly hello, my inbox is wide open! Drop me a line at [email protected], and let's conjure up some tech magic together!

😄 Pronouns: He/Him.

⚡ Fun fact: Beyond being a data analyst, I'm also a wanderer! Exploring new places on long walks provides the perfect backdrop for introspection. Check out some snapshots and reels here >> https://www.tiktok.com/@jaleesmoeen?is_from_webapp=1&sender_device=pc

🛠️ Tools of the Trade

Discover some of the tools and technologies I've delved into on my data journey:

  • Python (Pandas, Numpy, SciPy, Matplotlib, Regular Expression)
  • Databases (SQL, PostgreSQL, MongoDB)
  • Visual Storytelling on Tableau
  • JavaScript (Plotly.js)
  • Web Technologies (HTML, CSS, Bootstrap)
  • Spreadsheet Wizardry(Excel, Visual Basic)

Jalees Moeen's GitHub Stats

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  1. House_Price_Forecaster House_Price_Forecaster Public

    Forecasting house prices using machine learning and deep learning techniques for accurate predictions. This project involves creating a regression model to forecast house prices based on various fe…

    Jupyter Notebook

  2. Charity_Funding_Predictor Charity_Funding_Predictor Public

    Predicting the success of funding applicants for a charitable organization using machine learning and predictive modeling. The project involves creating a binary classification model using deep lea…

    Jupyter Notebook

  3. Credit_Risk_Classification Credit_Risk_Classification Public

    About Credit risk poses a classification problem that’s inherently imbalanced. Using a dataset of historical lending activity from a peer-to-peer lending services company, build a model that can id…

    Jupyter Notebook

  4. Home_Sales Home_Sales Public

    This project demonstrated the usage of SparkSQL to read, query, cache, and analyze home sales data, providing insights into average prices based on various criteria.

    Jupyter Notebook