This project demonstrates automated tracking and analysis of cryptocurrency market data using the CoinGecko API. It features historical data storage in Excel and professional visualizations for Bitcoin, Ethereum, Ripple, Dogecoin, and Binance Coin, all implemented in Python and Jupyter Notebook.
Explores global Covid-19 datasets using Microsoft SQL Server, performing data cleaning, analysis, and generating insights through visualizations and dashboards.
Demonstrates data cleaning and preprocessing on the Nashville Housing dataset using SQL Server, handling missing, duplicate, and inconsistent data, and standardizing values for accurate analysis.
This project scrapes and analyzes the Top 100 largest public companies in the United States by revenue from Wikipedia. Built using Python, Requests, and BeautifulSoup, it automates the collection of key company information including names, revenues, rankings, industries, and headquarters locations. The extracted data is cleaned, validated, and structured using Pandas to ensure accuracy and usability. The resulting dataset can be leveraged for financial trend analysis, ranking comparisons, and industry-level insights. This project demonstrates practical skills in data extraction, preprocessing, and preparation for visualization or further data analysis.
This project scrapes and analyzes the Top 10 largest private companies in the United States from publicly available sources. Using Python, Requests, and BeautifulSoup, it automates data extraction and structures the dataset with Pandas. The cleaned data enables revenue analysis, industry comparisons, and insights into private sector trends, demonstrating practical skills in data extraction, preprocessing, and preparation for further data analysis.
Analyzes a movies dataset to explore relationships between features like budget, gross revenue, runtime, ratings, votes, and release year. Visualizations using Python libraries (Pandas, Seaborn, Matplotlib) highlight strong correlations and trends for better data-driven insights.
Developed a Python-based web scraper to extract product data from Amazon, including titles, prices, ratings, and availability. The project leverages Requests and BeautifulSoup for robust data extraction and demonstrates automated tracking of product price changes for insightful analysis.
Showcases interactive Tableau dashboards created by Kareeb Sadab, transforming complex datasets into dynamic visualizations, KPI tracking, and trend analyses for actionable insights and data-driven decision-making.
This project showcases the Data Professional Survey Dashboard built in Power BI by Kareeb Sadab. It provides deep insights into global data professional trends, including salary benchmarks, programming language preferences, workforce demographics, and overall job satisfaction. The dashboard demonstrates advanced data modeling, visualization, and analytical skills, transforming raw survey data into actionable business intelligence for informed decision-making.
Dhaka-1211, Bangladesh