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How to predict a continuous variable without any output data ? All i have is Input data

I am working on a cyber security project wherein we have to prioritize vulnerabilities based on the existing features which are mostly categorical variables (also including couple of ordinal variables).
The objective here is to detect vulnerability that is most likely to be exploited, and thereby prioritizing it. Hence we have to predict a score of 0-10 . Whichever is the highest rating that we predict (in this case 10), will be the most critical vulnerability that needs immediate attention.
All that we have are the categorical variables (as input features).
Once again summarizing the problem here :
Current Input features : All categorical variables (with couple of ordinal variables)
Current Output feature : DOES NOT EXIST
Expected Output : Predict a score in the range 0-10, with 10 being most critical vulnerability
Never came across this kind of problem. It definitely looks like Regression is not the answer. Can you please share your thoughts on the same.

I may be misunderstanding but it appears that you don't have the necessary information to make the prediction.
My understanding is that you have category information but no other associations. For some categories you might be able to hard code your prediction based on expert opinion. Predicting a ping sweep is basically benign, for example, just by knowing what it's called. For anything more dynamic you're going to need more information than you listed.

If you can't assign a score yourself, there's no way a machine learning algorithm is going to be able to do it. It can't know what to optimize for.
However, you might find success by using an unsupervised algorithm to cluster your data based on the categorical values, then looking at the clusters and determining which ones seem to have the most important issues. You can find one discussion on categorical k-means clustering here.

How can noisy components be identified using the ICA method with file.edf in python?

I am trying to remove muscle artifacts from an EEG signal corresponding to an epileptic patient. For that, I used the fastICA method with python. The figure below represents the independent components:
enter image description here
Unfortunately, I could not distinguish the components corresponding to the artifacts. Is there a way to help me know which components to remove?

first of thing you should know how an eeg signal look like. I think in the picture attached ICA21, ICA7 and ICA2 is completely noisy data

Not sure if this is possible given the data that you have, but one possibility is to frame it as a supervised problem. Say you have a few epileptic patients' EEGs and a few from non-epileptic patients. You can apply an ICA decomposition to the whole dataset, and then use each component by itself as a feature vector (maybe discretizing it) to predict the class (i.e., epileptic vs. non-epileptic).
The noise components should have no predictive value, so you might be able to find that a cluster of components has a (statistically) significantly higher predictive value than another. This will require manually looking at the accuracy value of each component and making a subjective decision, but maybe it can help as an exploratory analysis.
Of course, this only works if you have data from multiple patients.

Clustering of Tags

I have a dataset (~80k rows) that contains a comma-separated list of tags (skills), for example:
python, java, javascript,
marketing, communications, leadership,
web development, node.js, react
...
Some are as short as 1, others can be as long as 50+ skills. I would like to cluster groups of skills together (Intuitively, people in same cluster would have a very similar set of skills)
First, I use CountVectorizer from sklearn to vectorise the list of words and perform a dimensionr reduction using SVD, reducing it to 50 dimensions (from 500+). Finally, I perform KMeans Clustering with n=50 , but the results are not optimal -- Groups of skills clustered together seems to be very unrelated.
How should I go about improving the results? I'm also not sure if SVD is the most appropriate form of dimension reduction for this use case.

I would start with the following approaches:
If you have enough data, try something like word2vec to get an embedding for each tag. You can use pre-trained models, but probably better to train on you own data since it has unique semantics. Make sure you have an OOV embedding for tags that don't appear enough times. Then use K-means, Agglomerative Hierarchical Clustering, or other known clustering methods.
I would construct a weighted undirected-graph, where each tag is a node, and edges represent the number of times 2 tags appeared in the same list. Once the graph is constructed, I would use a community detection algorithm for clustering. Networkx is a very nice library in python that lets you do that.
For any approach (including yours), don't give up before you do some hyper-parameter tuning. Maybe all you need is a smaller representation, or another K (for the KMeans).
Good luck!

All the TF-IDF, cosine, etc. only works well for very long texts, where the vectors can be seen to model a term frequency distribution with reasonable numeric accuracy. For short texts, this is not reliable enough to produce useful clusters.
Furthermore, k-means needs to put every record into a cluster. But what about nonsense data - say someone with the only skill "Klingon"?
Instead, use
Frequent Itemset Mining
This makes perfect sense on tags. It identifies groups of tags that occur frequently together. So one pattern is, e.g., "python sklearn, numpy"; and the cluster is all the users that have these skills.
Note that these clusters will overlap, and some may be in no clusters. That is of course harder to use, but for most applications it makes sense that records can belong to multiple, or no, clusters.

clustering in python without number of clusters or threshold

Is it possible to do clustering without providing any input apart from the data? The clustering method/algorithm should decide from the data on how many logical groups the data can be divided, even it doesn't require me to input the threshold eucledian distance on which the clusters are built, this also needs to be learned from the data.
Could you please suggest me what is closest solution for my problem?

Why not code your algorithm to create a list of clusters ranging from size 1 to n (which could be defined in a config file so that you can avoid hard coding and just fix it once).
Once that is done, compute the clusters of size 1 to n. Choose the value which gives you the smallest Mean Square Error.
This would require some additional work by your machine to determine the optimal number of logical groups the data can be divided (bounded between 1 and n).

Clustering is an explorative technique.
This means it must always be able to produce different results, as desired by the user. Having many parameters is a feature. It means the method can be adapted easily to very different data, and to user preferences.
There will never be a generally useful parameter-free technique. At best, some parameters will have default values or heuristics (such as Euclidean distance, such as standardizing the input prior to clusterings such as the gap statistic for choosing k) that may give a reasonable first try in 80% of cases. But after that first try, you'll need to understand the data, and try other parameters to learn more about your data.
Methods that claim to be "parameter free" usually just have some hidden parameters set so it works on the few toy example it was demonstrated on.

Utilising Genetic algorithm to overcome different size datasets in model

SO I realise the question I am asking here is large and complex.
A potential solution to variences in sizes of
In all of my searching through statistical forums and posts I haven't come across a scientifically sound method of taking into account the type of data that I am encountering,
but I have thought up a (novel?) potential solutions to account perfectly (in my mind) for large and small datasets within the same model.
The proposed method involves using a genetic algorithm to alter two numbers defining a relationship between the size of the dataset making up an implied strike rate and the
percentage of the implied strike to be used, with the target of the model to maximise the homology of the number 1 in two columns of the following csv. (ultra simplified
but hopefully demonstrates the principle)
Example data
Date,PupilName,Unique class,Achieved rank,x,y,x/y,Average xy
12/12/2012,PupilName1,UniqueClass1,1,3000,9610,0.312174818,0.08527
12/12/2012,PupilName2,UniqueClass1,2,300,961,0.312174818,0.08527
12/12/2012,PupilName3,UniqueClass1,3,1,3,0.333333333,0.08527
13/12/2012,PupilName1,UniqueClass2,1,2,3,0.666666667,0.08527
13/12/2012,PupilName2,UniqueClass2,2,0,1,0,0.08527
13/12/2012,PupilName3,UniqueClass2,3,0,5,0,0.08527
13/12/2012,PupilName4,UniqueClass2,4,0,2,0,0.08527
13/12/2012,PupilName5,UniqueClass2,5,0,17,0,0.08527
14/12/2012,PupilName1,UniqueClass3,1,1,2,0.5,0.08527
14/12/2012,PupilName2,UniqueClass3,2,0,1,0,0.08527
14/12/2012,PupilName3,UniqueClass3,3,0,5,0,0.08527
14/12/2012,PupilName4,UniqueClass3,4,0,6,0,0.08527
14/12/2012,PupilName5,UniqueClass3,5,0,12,0,0.08527
15/12/2012,PupilName1,UniqueClass4,1,0,0,0,0.08527
15/12/2012,PupilName2,UniqueClass4,2,1,25,0.04,0.08527
15/12/2012,PupilName3,UniqueClass4,3,1,29,0.034482759,0.08527
15/12/2012,PupilName4,UniqueClass4,4,1,38,0.026315789,0.08527
16/12/2012,PupilName1,UniqueClass5,1,12,24,0.5,0.08527
16/12/2012,PupilName2,UniqueClass5,2,1,2,0.5,0.08527
16/12/2012,PupilName3,UniqueClass5,3,13,59,0.220338983,0.08527
16/12/2012,PupilName4,UniqueClass5,4,28,359,0.077994429,0.08527
16/12/2012,PupilName5,UniqueClass5,5,0,0,0,0.08527
17/12/2012,PupilName1,UniqueClass6,1,0,0,0,0.08527
17/12/2012,PupilName2,UniqueClass6,2,2,200,0.01,0.08527
17/12/2012,PupilName3,UniqueClass6,3,2,254,0.007874016,0.08527
17/12/2012,PupilName4,UniqueClass6,4,2,278,0.007194245,0.08527
17/12/2012,PupilName5,UniqueClass6,5,1,279,0.003584229,0.08527
So I have created a tiny model dataset, which contains some good examples of where my current methods fall short and how I feel a genetic algorithm can be used to fix this. If we look in the dataset above it contains 6 unique classes the ultimate objective of the algorithm is to create as high as possible correspondence between a rank of an adjusted x/y and the achieved rank in column 3 (zero based referencing.) In uniqueclass1 we have two identical x/y values, now these are comparatively large x/y values if you compare with the average (note the average isn't calculated from this dataset) but it would be common sense to expect that the 3000/9610 is more significant and therefore more likely to have an achieved rank of 1 than the 300/961. So what I want to do is make an adjusted x/y to overcome these differences in dataset sizes using a logarithmic growth relationship defined by the equation:
adjusted xy = ((1-exp(-y*α)) * x/y)) + ((1-(1-exp(-y*α)))*Average xy)
Where α is the only dynamic number
If I can explain my logic a little and open myself up to (hopefully) constructive criticsm. This graph below shows is an exponential growth relationship between size of the data set and the % of x/y contributing to the adjusted x/y. Essentially what the above equation says is as the dataset gets larger the percentage of the original x/y used in the adjusted x/y gets larger. Whatever percentage is left is made up by the average xy. Could hypothetically be 75% x/y and 25% average xy for 300/961 and 95%/5% for 3000/9610 creating an adjusted x/y which clearly demonstrates
For help with understanding the lowering of α would produce the following relationship where by a larger dataset would be requred to achieve the same "% of xy contributed"
Conversly increasing α would produce the following relationship where by a smaller dataset would be requred to achieve the same "% of xy contributed"
So I have explained my logic. I am also open to code snippets to help me overcome the problem. I have plans to make a multitude of genetic/evolutionary algorithms in the future and could really use a working example to pick apart and play with in order to help my understanding of how to utilise such abilities of python. If additional detail is required or further clarification about the problem or methods please do ask, I really want to be able to solve this problem and future problems of this nature.
So after much discussion about the methods available to overcome the problem presented here I have come to the conclusion that he best method would be a genetic algorithm to iterate α in order to maximise the homology/correspondance between a rank of an adjusted x/y and the achieved rank in column 3. It would be greatly greatly appreciated if anyone be able to help in that department?
So to clarify, this post is no longer a discussion about methodology
I am hoping someone can help me produce a genetic algorithm to maximise the homology between the results of the equation
adjusted xy = ((1-exp(-y*α)) * x/y)) + ((1-(1-exp(-y*α)))*Average xy)
Where adjusted xy applies to each row of the csv. Maximising homology could be achieved by minimising the difference between the rank of the adjusted xy (where the rank is by each Unique class only) and Achieved rank.
Minimising this value would maximise the homology and essentially solve the problem presented to me of different size datasets. If any more information is required please ask, I check this post about 20 times a day at the moment so should reply rather promptly. Many thanks SMNALLY.

The problem you are facing sounds to me like "Bias Variance Dilemna" from a general point of view. In a nutshell, a more precise model favours variance (sensitivity to change in a single training set), a more general model favours bias (model works for many training sets)
May I suggest not to focus on GA but look at Instance Base Learning and advanced regression techniques. The Andrew moore page at CMU is a good entry point.
And particularly those slides.
[EDIT]
After a second reading, here is my second understanding:
You have a set of example data with two related attributes X and Y.
You do not want X/Y to dominate when Y is small, (considered as less representative).
As a consequence you want to "weigth" the examples with a adapted value adjusted_xy .
You want adjusted_xy to be related to a third attribute R (rank). Related such as,per class, adjusted_xy is sorted like R.
To do so you suggest to put it as an optimization problem, searching for PARAMS of a given function F(X,Y,PARAMS)= adjusted_xy .
With the constraint that D=Distance( achieved rank for this class, rank of adjusted_xy for this class ) is minimal.
Your question, at least for me, is in the field of attribute selection/attribute adaptation. (I guess the data set will later be used for supervised learning ).
One problem that I see in your approach (if well understood) is that, at the end, rank will be highly related to adjusted_xy which will bring therefore no interesting supplementary information.
Once this said, I think you surely know how GA works . You have to
define the content of the chromosome : this appears to be your alpha parameter.
define an appropriate fitness function
The fitness function for one individual can be a sum of distances over all examples of the dataset.
As you are dealing with real values , other metaheuristics such as Evolution Strategies (ES) or Simulated Anealing may be more adapted than GA.
As solving optimization problems is cpu intensive, you might eventually consider C or Java instead of Python. (as fitness at least will be interpreted and thus cost a lot).
Alternatively I would look at using Y as a weight to some supervised learning algorithm (if supervised learning is the target).

Let's start by the problem: You consider the fact that some features lead to some of your classes a 'strike'. You are taking a subset of your data and try to establish a rule for the strikes. You do establish one but then you notice that the accuracy of your rule depends on the volume of the dataset that was used to establish the 'strike' rate anyway. You are also commenting on the effect of some samples in biasing your 'strike' estimate.
The immediate answer is that it looks like you have a lot of variation in your data, therefore you will in one way or another need to collect more to account for that variation. (That is, variation that is inherent to the problem).
The fact that in some cases the numbers end up in 'unusable cases' could also be down to outliers. That is, measurements that are 'out of bounds' for a number of reasons and which you would have to find a way to either exclude them or re-adjust them. But this depends a lot on the context of the problem.
'Strike rates' on their own will not help but they are perhaps a step towards the right direction. In any case, you can not compare strike rates if they are coming from samples of different sizes as you have found out too. If your problem is purely to determine the size of your sample so that your results conform to some specific accuracy then i would recommend that you have a look at Statistical Power and how does the sample size affects it. But still, to determine the sample size you need to know a bit more about your data, which brings us back to point #1 about the inherent variation.
Therefore, my attempt to an answer is this: If i have understood your question correctly, you are dealing with a classification problem in which you seek to assign a number of items (patients) to a number of classes (types of cancer) on the evidence of some features (existence of genetic markers, or frequency of their appearance or any other quantity anyway) about these items. But, some features might not exist for all items or, there is a core group of features but there might be some more that do not appear all the time. The question now is, which classifier do you use to achieve this? Logistic regression was mentioned previously and has not helped. Therefore, what i would suggest is going for a Naive Bayesian Classifier. The classifier can be trained with the datasets you have used to derive the 'strike rates' which will provide the a-priori probabilities. When the classifier is 'running' it will be using the features of new data to construct a likelihood that the patient who provided this data should be assigned to each class.
Perhaps the more common example for such a classifier is the spam-email detectors where the likelihood that an email is spam is judged on the existence of specific words in the email (and a suitable training dataset that provides a good starting point of course).
Now, in terms of trying this out practically (and since your post is tagged with python related tags :) ), i would like to recommend Weka. Weka contains a lot of related functionality including bootstrapping that could potentially help you with those differences in the size of the datasets. Although Weka is Java, bindings exist for it in Python too. I would definitely give it a go, the Weka package, book and community are very helpful.

No. Don't use a genetic algorithm.
The bigger the search space of models and parameters, the better your chances of finding a good fit for your data points. But the less this fit will mean. Especially since for some groups your sample sizes are small and therefore the measurements have a high random component to them. This is why, somewhat counterintuitively, it is often actually harder to find a good model for your data after collecting it than before.
You have taken the question to the programmer's lair. This is not the place for it. We solve puzzles.
This is not a puzzle to find the best line through the dots. You are searching for a model that makes sense and brings understanding on the subject matter. A genetic algorithm is very creative at line-through-dot drawing but will bring you little understanding.
Take the problem back where it belongs and ask the statisticians instead.
For a good model should be based on theory behind the data. It'll have to match the points on the right side of the graph, where (if I understand you right) most of the samples are. It'll be able to explain in hard probabilities how likely the deviations on the left are and tell you if they are significant or not.
If you do want to do some programming, I'd suggest you take the simplest linear model, add some random noise, and do a couple simulation runs for a population like your subjects. See if the data looks like the data you're looking at or if it generally 'looks' different, in which case there really is something nonlinear (and possibly interesting) going on on the left.

I once tackled a similar problem (as similar as problems like this ever are), in which there were many classes and high variance in features per data point. I personally used a Random Forest classifier (which I wrote in Java). Since your data is highly variant, and therefore hard to model, you could create multiple forests from different random samples of your large dataset and put a control layer on top to classify data against all the forests, then take the best score. I don't write python, but i found this link
http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
which may give you something to play with.

Following Occam's razor, you must select a simpler model for small dataset and may want to switch to a more complex model as your dataset grows.
There are no [good] statistical tests that show you if a given model, in isolation, is a good predictor of your data. Or rather, a test may tell you that given model fitness is N, but you can never tell what the acceptable value of N is.
Thus, build several models and pick one with better tradeoff of predictive power and simplicity using Akaike information criterion. It has useful properties and not too hard to understand. :)
There are other tests of course, but AIC should get you started.
For a simple test, check out p-value