The provided dataset, titled "product_price_dataset.csv," contains information about various products across different categories. It can be used for a project titled "Dynamic Product Price Adjustment Using Machine Learning." The dataset includes the following columns:

1) ProductID: A unique identifier for each product. 2) ProductName: The name of the product. 3) Brand: The brand or company that manufactures the product. 4) Category: The category to which the product belongs (e.g., Laptops, Mobile Phones, Wearable Tech, Home Appliances, etc.). 5) Weight: The weight of the product, typically in kilograms. 6) Dimensions: The dimensions of the product, specified as length x width x height. 7) Material: The primary material used in the construction of the product. 8) Color: The color of the product. 9) Rating: The average rating of the product based on customer reviews, usually on a scale of 1 to 5. 10) NumReviews: The number of customer reviews for the product. 11) Price: The current price of the product.

This dataset contains information about 120 different products spanning various categories such as electronics, home appliances, fitness and health, outdoor and sports equipment, and more. The dataset includes products like laptops, smartphones, headphones, smartwatches, gaming consoles, tablets, cameras, drones, fitness trackers, wireless mice, external hard drives, and many others. With this comprehensive dataset, machine learning techniques can be applied to analyze the relationships between product features (such as brand, category, weight, dimensions, material, color, rating, and number of reviews) and the price. The goal would be to develop a dynamic pricing model that can adjust product prices based on these features, potentially helping businesses optimize their pricing strategies and increase profitability. Additionally, the dataset can be used for other tasks such as product recommendation systems, market segmentation, and demand forecasting, among others.

This dataset presents projected household size for 5-year periods between the years of 2011 and 2036 for the state of New South Wales (NSW). The data is presented as aggregations following the …Show full descriptionThis dataset presents projected household size for 5-year periods between the years of 2011 and 2036 for the state of New South Wales (NSW). The data is presented as aggregations following the Australian Statistical Geography Standard (ASGS) 2016 Local Government Areas (LGA). Household projections show the number of households that would form if demographic trends continue and if assumptions about living arrangements are realised over the projection period. A household is two or more people who share a dwelling (house, apartment, townhouse, caravan, etc.) and share food and cooking facilities, and other essentials. Household projections show the future number and type of households living in private dwellings. Private dwellings are self-contained accommodation such as houses, apartments, mobile homes or other substantial structures. It does not include accommodation such as boarding houses, nursing homes or prisons. The household projections also include the implied dwelling demand for those households. This is the likely number of private dwellings needed to accommodate future population-driven demand. For more information please read the Household Projections User Guide. Please note: AURIN has spatially enabled the original data. Copyright attribution: Government of New South Wales - Department of Planning, Industry and Environment, (2016): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 4.0 International (CC BY 4.0)