Hey everyone!
I’m trying to build a dynamic pricing model for a project, but I’m stuck on finding a good dataset for dynamic pricing. Anyone know where I can get one?
I’ve checked Kaggle and some govt sites, but most are either outdated or don’t have the right variables. Need something with price changes, demand, and maybe competitor data.
Also, if you’ve worked with a dataset for dynamic pricing before, any tips on cleaning or prepping it? I’m kinda new to this lol.
Thanks in advance! 🙏
Hey! I feel your struggle with finding a good dataset for dynamic pricing. Have you tried Quandl? They’ve got some decent datasets with price changes and demand metrics, especially for industries like retail and e-commerce.
Also, for competitor data, you might wanna check out Data.world. It’s a bit hit or miss, but I’ve found some gems there before.
As for cleaning, I’d recommend using Pandas in Python. It’s super handy for handling missing values and normalizing data. Good luck!
Yo! I was in the same boat a while back. Honestly, Kaggle is hit or miss for dynamic pricing datasets. I’d suggest looking into AWS Data Exchange. They’ve got some niche datasets that include price fluctuations and demand trends.
For cleaning, I’d say focus on removing outliers first. They can mess up your model big time. Also, check out Open Data Soft if you haven’t already. They sometimes have datasets with competitor pricing info.
Hey there! Finding a dataset for dynamic pricing can be tricky, but have you tried scraping your own data? Tools like Scrapy or BeautifulSoup can help you pull real-time pricing data from e-commerce sites. It’s a bit of work, but you’ll get exactly what you need.
For cleaning, I’d suggest using Excel or Google Sheets for smaller datasets. They’re great for quick fixes like removing duplicates or filling in missing values.
Hi! I’ve worked on a dynamic pricing model before, and I used a dataset from UCI Machine Learning Repository. They’ve got some good ones with price changes and demand variables. It’s not perfect, but it’s a solid starting point.
For cleaning, I’d recommend using R if you’re comfortable with it. The dplyr package is a lifesaver for prepping datasets. Also, check out Statista for industry-specific data.
Hey! I’d recommend checking out Data.gov if you haven’t already. They’ve got some datasets that include price changes and demand metrics, though you might need to do some digging.
For cleaning, I’d say start by normalizing your data. It’ll make your model way more accurate. Also, look into Tableau Prep for cleaning and visualizing your dataset for dynamic pricing. It’s super user-friendly.
Hi! I’ve been working on dynamic pricing models for a while now, and I’d suggest looking into Google Dataset Search. It’s a goldmine for finding datasets with price changes and demand variables.
For cleaning, I’d recommend using Python’s NumPy library. It’s great for handling large datasets and performing complex calculations. Also, check out RapidAPI for real-time pricing data.
Wow, thanks so much for all the suggestions, everyone! I checked out Quandl and Data.world, and they’ve got some pretty solid datasets for dynamic pricing. I’m gonna try scraping some data too, as a few of you suggested.
Quick question though—anyone know if there’s a way to automate the cleaning process for large datasets? I’m still getting the hang of Python, but I’ll definitely look into Pandas and NumPy. Thanks again, you guys are awesome! 🙌
