#EXRACT

[thatgalacticbaseballer]

He REPAIRED the aventurine stone??????

[castielssuperhell]

lets make a cake! everyone add one ingredient, i'll start, i added some vanilla exact :)

[penandfunkky]

NOT THE VANILLA EXTRACT DO VANILLA BEAN PLANTS OR SMTH

Great job everybody! This garden is looking beautiful!

[haunted-house-heart]

jesus fucking christ

[zooilogicallyimprobable]

Tumblr bullshit has started infecting my dreams, I have straight up dreamed about vanilla extract for like three nights

[kiiingsnake]

i love vanilab exract

[ahmedferradj]

Data mining

1.What's Data mining ?

Datamining is the process of extracting and discovering patterns in large datasets involving methods at the intersection of machine learning, statistics and database systems. Datamining is interdisciplinary subfield of computer-science and statistics with overall goal of extracting information (with intelliegent methods) from a data set and transforming the information into a comprensible structure for further use. Data mining is the analysis step of The KDD process "Knowledge discovery in database".

2.What's KDD process ?

KDD process is known as "Knowledge Discovery in Database".It"s a multi-step process of finding knowledge from large data sets and emphasizes the high-level application of particular datamining methods.It's of interests to researchers in machine learning, pattern recognition, databases, ststistics, artificial intelligence, knowledge aquisition for experts systems and data-visualization. The picture below defines the different steps of KDD process and each of those steps have an input and output entity. The KDD process can't be executed without beginning on data.

3.What are the different steps of the KDD process ?

The overall process of finding and interpretting patterns from data involves the repeated application of the following steps mentioned in the graph above :

Selection : we create a target data set by seecting a part of the overall data set as a sample then focusing on a subset of variables on which discovery is to be performed. The result of these step is a subset of data considered as a sample.

Preprocessing : These step of the KDD process takes the target data set as an input then it applyes data cleaning by removing the noise from the input data set then restucturing the data set. The output of these operation is a preprocessed dataset that can be able to be transformed in the next step.

Data transformation : These step takes the preprocessed data as input and tres to find some useful features depending on the goal of the task and reducing dimension to execute an effective learining datamining.

Data mining : in this phase we will descide whether the goal of KDD process is classification, regression, clustering ...etc. Discover the patterns of interests.

Interpretation : Interpretating mined patterns and consolidating discovered knowledge.

4.What are data mining tasks ?

There are several steps that are defined in the sub-process of KDD especially in datamining steps. In Data mining, there are 02 types of data mining that are :

Predictive mining: predective data mining is the analysis done to predict a future event or other data or trends and to predict something will happen in the near future. Predective data mining offers a better future analysis and to make better decisions to add a value in predective analytics like for example predecting the future customer of a defined service, define the future price of oil and gaz in the world market, define the next ill of an international pandemic, define the future political conflict ... etc. There are 4 types of descriptive data mining tasks which are :

Classification analysis : It is used to retrieve critical and pertinent data and metadata. It categorizes information into various groups. Classification Analysis is best demonstrated by email providers. They use algorithms to determine whether or not a message is legitimate.

Regression Analysis : It tries to express the interdependence of variables. Forecasting and prediction are common applications.

Time Serious Analysis : It is a series of well-defined data points taken at regular intervals.

Prediction Analysis : It is related to time series, but the time isn’t restricted.

Descriptive mining : descriptive data mining is to describe data and make data more readable to human beings, it's used to extract information from previous events and data and to discovering an interesting patterns and association behind data. It's also used to exract correlations, relationships between features and finding new laws and regularities based on data. There are four different types of Descriptive Data Mining tasks. They are as follows :

Clustering analysis : It is the process of determining which data sets are similar to one another. For example, to increase conversion rates, clusters of customers with similar buying habits can be grouped together with similar products.

Summerazation analysis : It entails methods for obtaining a concise description of a dataset. For example, summarising a large number of items related to Christmas season sales provides a general description of the data, which can be extremely useful to sales and marketing managers.

Association rules analysis : This method aids in the discovery of interesting relationships between various variables in large databases. The retail industry is the best example. As the holiday season approaches, retail stores stock up on chocolates, with sales increasing before the holiday, which is accomplished through Data Mining.

Sequence discovery analysis : It's all about how to do something in a specefic order. For instance, a user may frequently purchase shaving gel before purchasing razor in a store.It all comes down to the order in which the user purchases the product, and the store owner can then arrange the items accordingly.

5.Links :

[it-is-no-desert]

obsessed with this fuckin.. gold ass starscream

why is he gold... is this referencing something.. did he get dunked in a vat of molten gold at some point in the comics?? wait how crazy will tumblr let me get with posts.. can i put a poll after an image and some text? OH WAIT I CAN:

perfect. advise me, o wise robot loving mutuals

[crispsandkerosene]

Replies to Comments on my Creators’ Polls

cindysimblr said:

Wings 3D. I want to learn Blender, but I just never get to it and I'm used to how things work in Wings 3D now.

persimmonthusiast said:

Still using Wings 3D, it's simple and does the job. Not sure my laptop could even handle Blender. Now that I started using Milkshape as well for food and clothing,  I might use it in the future for object meshes with morphs as well. But I have no clue how I would mesh something from scratch in Milkshape, it is extremely impractical to use imo 

I’m simultaneously suprised and not surprised to hear of multiple people using Wings 3D. I know Gayars also uses it.

tvickiesims said:

I answered Milkshape in poll but usually I use both Milkshape and Blender. Blender for Sims4Studio exraction and doubling and recalculating normals and Milkshape for resizing, remapping and bringing pieces together. I'm far for being meshing-savvy and Blender scares me a lot so Milshape it is XD

I guessed people might jump from one to the other depending on the task at hand, cue adding “mainly” to the question haha. I do too, though Milkshape is the one I used less often.

treehawkdoesinternets said:

I actually have 3 different versions of blender that I switch between to use different plug-ins. I think 2.49, 2.74, and then one that I keep actually updated lol. Milkshape melts my brain!

Now I kind of regret not including “Older Versions of Blender” as a specific option :P

potentialfate-sims  said:

I learned Maya in school, so Blender was the easiest for me to pick up. I'll still do some things in Milkshape, if I have to, but working with that program is like nails on a chalkboard for me 😭😂I far prefer blender, but I can see how it's super intimidating for someone who's new to modelling. 🤔 

oh, I use I think 2.93... the gmdc plugin works okay with it, not perfectly, but worst case I'll pull stuff into milkshape as OBJs if I have to. 

Same! I still use Maya for unwraps tbh. I find that the 2.8+ GMDC pluging does a good job for everything but waist seams for clothing. Those are the only reason I still use MilkShape on the side.

curiousxsubject said:

voted other: i do not make sims 2 custom content. 

And now I’m regretting not putting a “Show me the results” type option :p

[memecatwings]

alright vanilla exract meme is dead tumblr is trying to monetize it

[castielssuperhell]

vanilla extract. reblog it you agree

[hawpmobility]

Why Uber & Ola do not care about riders & drivers?

Global market for ride hailing is pegged at anything above USD 85 billion dollar annually. The market is not only huge but also growing at breathtaking pace of around 17% . To put things in perspective at this pace of growth the market is set to double every 4.5 years. Compare that with your bank deposit which will take 15–20 years to double if you are living in developed countries or developing economies like India and China.

The market has been evolving over the decades with few innovations here and few there. However, advent of category defining company Uber has upended the industry in unprecedented manner. The beauty of Uber platform lies in innovative plumbing of technologies developed before it but after early 2000s. Chief among them are smart phones & Google maps . Such has been the success of Uber’s model that it has assumed an envying place in english language i.e. “Uberization” . The entire shared economy as we see today is inspired by Uber.

And many copycats have also emerged in the same market as Uber salivating the prospects built on the size and growth of the industry.

So What’s the problem?

The very solution which distrupted the market is becoming the key problem and it seems that the market is ready for yet another decadal change.

A little background will help before we move on. Uber relied on efficient matching of drivers and riders by signaling power of prices or fares. It has. utilized its prediction engine combined with real time data to change the prices to match supply with demand or vice versa. The engine increases price to attract drivers to pockets of high demand & reduces the price where the demand is muted. The trick has enabled it to provide more business to its drivers and increased utilization of their vehicles. On the other hand it has successfully provided reliable (really?) vehicle availability to the riders.

But the engine has created problems of its own principally those related to ethics & fair dealing.

I have not understood. Please explain!

The model in which Uber works relies on platform effect. Simply put the higher the number of users on its platform higher will be the value of the participants. For example, an additional driver will ensure more choice and increased competition thereby reducing tariffs to riders or open up new routes. Also, a new rider will increase the earning potential for drivers thus attracting ever more drivers. So once the flywheel started moving it will gather momentum oon its own.

The downside of this mechanism is that the model makes the market winner-takes-it-all. So market will only have 1–2 players to have enough scale to provide value to riders ( choice or fare) or to drivers ( higher business) . Now the winner or couple of winners will have control of market. This is evident in today’s market where drivers pay high commissions and riders accept surge pricing for convenience ( not that they want to). The fares do not reflect economic costs but the level of dependency riders have on Uber and likes. The fares are based on “Willingness-to-pay” which is a euphemism for gouging money as much as can be exracted based on the desperation of riders. A few of you may know that fares not only acccount for distance or demand-supply mismatch but also what is the battery level of your phone which may make you desperate to accept fares.

Moreover, the algorithms used by the incubments use what is called Machine Learning ( ML)) . The programs built on this technology are useful in many situations but inherently biased. The ML models are built by feeding lots of data and finding a pattern which can then be utilized for predictive purposes.

However, many of the readers would know that these models perpetuate the bias in data. For example , many studies have discovered that crime prevention models based on ML have shown bias against minorities and backward section of the society . This has led it further supression of these sections .

Similarly , ML models used by ride hailing app are fed on non-representative data of many situations. For example , on a rainy day couple of riders have accepted very high fares. This will be fed back to model which will show yet higher fares to subsequent riders. It may lead to complete breakdown of demand supply matching framework apart from raising ethical questions.

The unencumbered use of technology is not beneficial for even the drivers who may miss out on business due to high fares.

What’s the solution?

This article does not, in any way, deprecate the use of technology. But strongly backs to augment human capabilities with the use of technology . The decision making ought not be left to machines but it must be enhanced by efficient processing of information.

So can we expect some changes?

Definitely, the market is big & growing and perhaps the users will also want to try out the alternatives to the incumbent.

Source: This article has been originally published on Hawp

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