This dataset contains a variety of laptop specifications and the price of each device in Euros, the goal is to create a machine learning model to train the model to predict laptop prices.

The dataset is allowed to be modified during the analysis.

Details of the columns:

| | Columns | Description | Type | |--------------------|--------------------------------------|---------------------| | 1 | Company | Laptop manufacturer. | Categorical | | 2 | Product | Brand and Model. | Categorical | | 3 | TypeName | Type (Notebook, Ultrabook, Gaming, etc.)| Categorical | | 4 | Inches | Screen Size. | Numerical (float)| | 5 | ScreenResolution | Screen Resolution. | Categorical | | 6 | CPU_Company | Central Processing Unit (CPU) manufacturer. | Categorical | | 7 | CPU_Type | Central Processing Unit (CPU) type. | Categorical | | 8 | CPU_Frequency | Central Processing Unit (CPU) Frequency (GHz).| Numerical (float) | | 9 | RAM (GB) | Laptop RAM. | Numerical (int) | | 10 | Memory | Hard Disk / SSD Memory. | Categorical | | 11 | GPU_Company | Graphics Processing Units (GPU) manufacturer. | Categorical | | 12 | GPU_Type | Graphics Processing Units (GPU) type. | Categorical | | 13 | OpSys | Operating System. | Categorical | | 14 | Weight (kg) | Laptop Weight (kg). | Numerical (float) | | 15 | Price (Euro) | Laptop price (Euro). | Numerical (float) |

This research study was conducted to analyze the (potential) relationship between hardware and data set sizes. 100 data scientists from France between Jan-2016 and Aug-2016 were interviewed in order to have exploitable data. Therefore, this sample might not be representative of the true population.

What can you do with the data?

• Look up whether Kagglers has "stronger" hardware than non-Kagglers
• Whether there is a correlation between a preferred data set size and hardware
• Is proficiency a predictor of specific preferences?
• Are data scientists more Intel or AMD?
• How spread is GPU computing, and is there any relationship with Kaggling?
• Are you able to predict the amount of euros a data scientist might invest, provided their current workstation details?

I did not find any past research on a similar scale. You are free to play with this data set. For re-usage of this data set out of Kaggle, please contact the author directly on Kaggle (use "Contact User"). Please mention:

• Your intended usage (research? business use? blogging?...)
• Your first/last name

Arbitrarily, we chose characteristics to describe Data Scientists and data set sizes.

Data set size:

• Small: under 1 million values
• Medium: between 1 million and 1 billion values
• Large: over 1 billion values

For the data, it uses the following fields (DS = Data Scientist, W = Workstation):

• DS_1 = Are you working with "large" data sets at work? (large = over 1 billion values) => Yes or No
• DS_2 = Do you enjoy working with large data sets? => Yes or No
• DS_3 = Would you rather have small, medium, or large data sets for work? => Small, Medium, or Large
• DS_4 = Do you have any presence at Kaggle or any other Data Science platforms? => Yes or No
• DS_5 = Do you view yourself proficient at working in Data Science? => Yes, A bit, or No
• W_1 = What is your CPU brand? => Intel or AMD
• W_2 = Do you have access to a remote server to perform large workloads? => Yes or No
• W_3 = How much Euros would you invest in Data Science brand new hardware? => numeric output, rounded by 100s
• W_4 = How many cores do you have to work with data sets? => numeric output
• W_5 = How much RAM (in GB) do you have to work with data sets? => numeric output
• W_6 = Do you do GPU computing? => Yes or No
• W_7 = What programming languages do you use for Data Science? => R or Python (any other answer accepted)
• W_8 = What programming languages do you use for pure statistical analysis? => R or Python (any other answer accepted)
• W_9 = What programming languages do you use for training models? => R or Python (any other answer accepted)

You should expect potential noise in the data set. It might not be "free" of internal contradictions, as with all researches.

Context

This dataset has been created to support a set of experiments about using TFRecord for full GPU usage. I have taken the Fashion Classification Dataset and then I have reduced the size of images (now 256x256) and stored in a set of TFRecords files that can be easily used in TF2 code, for fast processing with GPU and TPU.

Content

It is a set of files in TFRecords format. Each record contains an image of a clothes or boot or something similar, together with a set of metadata. This is the format

LABELED_TFREC_FORMAT = {

   "image": tf.io.FixedLenFeature([], tf.string), 
   "image_name": tf.io.FixedLenFeature([], tf.string),
  "base_colour" : tf.io.FixedLenFeature([], tf.int64),
  "target" : tf.io.FixedLenFeature([], tf.int64)
}

base_colour and target have been codified:

This is the list of Master Categories (target): * Accessories * Apparel * Footwear * Free Items * Home * Personal Care * Sporting Goods

codified with [0, .. 6]

Acknowledgements

Original images taken from Param Aggarwal dataset: https://www.kaggle.com/paramaggarwal/fashion-product-images-dataset

Inspiration

I created this dataset to support a set of experiments around TFRecords. With it, you can easily create a Fashion Classifier that can be quickly trained. Using a two-GPU machine (p100) it takes 120 sec. per epoch and with around 20 epochs you can easily reach 0.994 accuracy on the test set.

Context

The following is the results of an online survey conducted by BoilingSteam.com among the Linux Gamers' Community (n=560, sharing only here answers where respondents explicitly agreed to have their answers made public, i.e. total n size was higher) in end of Q1 2016, to better understand their hardware, usage habits and reactions to several of Valve's Steam Initiatives. Most of the answers are coming from members of the r/Linux_Gaming and r/Linux subreddits, so you need to take in account that this may not be representative of your typical Linux user.

Content

There are many variables in this data set, with both numerical, free text and categorical answers. Every line corresponds to an individual response. Note that answers are anonymous. The first row is the coding you can use for your analysis (that should save a bit of time), the second row is the actual question asked (you can erase it), and the data starts from the third row.

Questions cover some of the following attributes (there are much more in the actual datasheet):

• Demographics / Geography
• Family Situation
• OS used for Work and at Home
• Linux Usage experience
• Linux Gaming Experience
• Type of Gamer (Hardcore or not)
• Playing Exclusively on Linux or not
• Time spent Playing per week
• Budget spent on Linux Games per month
• Games played recently
• Games Bought recently
• Hardware GPU for Gaming
• Hardware GPU Model
• General Hardware at Home using Linux
• Usage of Resellers (Steam, GOG, HumbleBundle)
• Satisfaction of different Resellers
• Awareness of Steam Machines
• Awareness of Steam Controller, Steam Link
• Intent of Purchase of Steam Machines
• Intent of Building Steam Machine DIY
• SteamOS and opinion towards it
• General feeling towards future of Linux
• Stance about DRM
• Stance about WINE
• WINE usage and satisfaction
• and much more...

Acknowledgements

The questionnaire was designed by Ekianjo at BoilingSteam.com. If you have suggestions for improvements of future surveys of the same kind, please reach us on Kaggle or on our contact page: http://boilingsteam.com/about-boiling-steam/

Past Research

You can see some analysis done a previous iteration of this survey (previous data can not be made public however) - this may serve as a good benchmark to measure changes: http://boilingsteam.com/the-three-kinds-of-linux-gamers/

Inspiration

Feel free to play with the data, and share what insights you may find. We are big proponents of making data free in general for transparency purposes, so if your analysis can help generate a better understanding of who are Linux Gamers, this would be a great outcome.