Welcome to my collection of data analysis projects built using SQL, Python, and R. Each project dives into real-world datasets to uncover insights through data cleaning, exploration, visualization, and statistical modeling.
🌍 International Debt Statistics (SQL) Analyzed global debt trends using SQL queries to extract key insights from the International Debt Statistics dataset. Focused on identifying top borrowing countries, trends over time, and sector-wise debt distribution.
🧪 Kidney Stones & Simpson's Paradox (R) Investigated the classic case of Simpson's Paradox using R. Performed statistical analysis and data visualization to reveal how aggregated data can be misleading in medical decision-making.
🚗 Reducing Traffic Mortality in the USA (Python) Explored traffic accident and fatality data in the U.S. using Python. Conducted data cleaning, EDA, and visualizations to identify high-risk factors and trends contributing to road fatalities.
🔍 Exploratory Data Analysis (EDA) using pandas, dplyr, or SQL queries
🧹 Data Cleaning & Transformation across different tools
📈 Visualizations with matplotlib, seaborn, ggplot2, etc.
🧠 Statistical Modeling & Insights
🧾 SQL Queries for extracting insights from relational databases
📄 Notebooks & RMarkdown Reports for reproducibility
Languages: Python, R, SQL
Libraries: pandas, numpy, seaborn, matplotlib, tidyverse, ggplot2, etc.
Databases: SQLite, PostgreSQL (depending on project)