Kaggle: A massive repository with datasets from various domains (e.g., "Bangladesh Flood Prediction," "Air Quality in Dhaka").
Google Dataset Search: A search engine specifically for datasets – type in "Mohadevpur," "Rajshahi," "Bangladesh," or your specific topic to find relevant results globally or locally.
UCI Machine Learning Repository: Classic datasets for various ML tasks.
Data.gov.bd (Bangladesh): The official open data portal for Bangladesh. You dataset might find statistics on population, economy, health, or education specific to the country or even regions.
data.gov (US), data.europa.eu (EU): For global or developed market data.
Time commitment: Minutes to a few hours for searching, downloading, and initial exploration.
Tools: Web browser, Python (Pandas for CSV/Excel).
Why it's free & fast: The data is already collected, often cleaned (to some extent), and ready for download. You just need to find the right one for your project.
Mohadevpur/Bangladesh relevance: You can often find regional data or general datasets that apply to local contexts (e.g., crop yield data from a global agricultural dataset can be adapted).
2. Basic Web Scraping for Tabular Data
How to do it: Identify a website with publicly accessible, structured data (e.g., local news portals with tables of information, government statistics pages that permit scraping, sports results, product listings from non-e-commerce sites that display data openly). Use Python libraries to extract the data.
Time commitment: 4-8 hours (depending on website complexity and your coding speed).
Tools: Python (requests, BeautifulSoup for HTML parsing, Pandas for data structuring). For more complex sites, Scrapy could be an option.