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pooja2434/README.md

πŸ‘©β€πŸ’» Pooja K R | Data Analyst

Hi, I'm Pooja! A passionate Data Analyst with a strong foundation in SQL, Python, and Machine Learning, Automation. I enjoy transforming data into actionable insights and building automated solutions. 🌍 Chennai, India | πŸ“Š SQL β€’ Python β€’ Tableau β€’ Machine Learning β€’ Deep Learning (Learning) β€’ Report Automation β€’ PySpark(Learning)

πŸš€ About Me

Dynamic and results-driven Data Analyst with 6+ years of experience across Investment Banking and Data Analytics.

  • πŸ“ˆ Skilled in SQL, Python, Tableau, Zoho analytics, Excel, Machine Learning and Report Automation
  • ⚑ Built automated reporting pipelines that saved 6+ hours per week
  • πŸ€– Knowledge with Machine Learning models (Regression, Classification, Clustering)
  • 🧠 Currently learning Deep Learning
  • πŸ“Š Experienced across E-commerce, Banking, Logistics, Aquaculture, Animal Husbandry
  • 🌱 Currently learning PySpark to scale analytics on big data
  • ✨ Turning raw data into actionable insights & powerful stories.
  • Please find my Resume here

πŸ“¬ How to reach me

LinkedIn: Profile
Tableau Public: My Profile Email: pjravilakshmi@gmail.com My Resume: Resume here.

πŸ›  Tech Stack

πŸ’Ύ SQL | 🐍 Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn) | πŸ“Š Tableau | πŸ“ˆ Excel | πŸ€– Machine Learning | 🧠 Deep Learning (Learning) | πŸ”„ Data Cleaning | βš™οΈ Automation | πŸ“¦ PySpark (Learning)

πŸ› οΈ Projects Portfolio

πŸ”Ή SQL examples

βœ”οΈ Joins, Aggregations, CTEs, Window Functions. Business case studies solved with SQL.

  1. Python Projects(Basics & Intermediate) βœ”οΈ Loops, functions, data cleaning, Pandas exercises
  1. 🐍 Report Automation
  • Automated weekly sales reports (Excel/PDF + Email)
  • Saved 4–6 hours/week for business teams

3.🧹 Simple Data Cleaning App (Streamlit)

  • Upload CSV β†’ Clean with Pandas β†’ Download processed file
  • Built with Python + Streamlit

🧠Machine Learning Projects

πŸ“Œ House Price Prediction

  • Description: Developed a linear regression model to predict house prices based on various features.
  • Technologies Used: Python, Pandas, Scikit-learn
  • Key Highlights:
    • Data preprocessing and feature engineering.
    • Model evaluation using RΒ² and RMSE metrics.
    • Visualization of predicted vs. actual prices.

πŸ“Š Business dashboards on Tableau Public πŸ”— View My Profile

πŸ† Highlights

  • πŸ₯‡ Multiple awards for automation & analytics excellence
  • πŸ’‘ Delivered insights across 5+ industries
  • πŸ“’ Passionate about data storytelling & advanced analytics

✨ β€œData + Automation + Machine Learning = Smarter Decisions.”

Pinned Loading

  1. SQL-Projects SQL-Projects Public

    These projects demonstrate my expertise in extracting, analyzing, and interpreting data from relational databases using SQL. They cover table creation, joins, aggregations, CTEs, CASE statements, s…

  2. Python-Projects Python-Projects Public

    These projects showcase my proficiency in Python programming, ranging from basics to advanced data manipulation. They include core programming concepts such as loops and functions, as well as pract…

    Jupyter Notebook

  3. Report-Automation-Python-Project Report-Automation-Python-Project Public

    This project automates the generation of reports by integrating data cleaning, SQL query execution, and email dispatch functionalities using Python. Designed for efficiency and scalability, it allo…

    Python

  4. simple-data-cleaning-app simple-data-cleaning-app Public

    Simple Data Cleaning App A lightweight Streamlit application that demonstrates basic data-cleaning tasks on a sample dataset. The app loads raw data, identifies and handles missing values and dupli…

    Python

  5. Tableau-Dashboards Tableau-Dashboards Public

    This repository contains a collection of interactive Tableau dashboards created as part of my data visualization practice and portfolio. Each dashboard demonstrates different Tableau techniques suc…

  6. House-prediction House-prediction Public

    House Price Prediction using Linear Regression with steps including Data Loading, Data Exploration , Data Preprocessing , Model Training , Model Evaluation

    Jupyter Notebook