python - Machine Learning directions -

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i have few questions regarding machine learning problem.

i have dataset (of 10000 data points) else. dataset consists of survey responses on product concepts, done across different demographic groups, geographical regions , time frames. respondents asked opinions on product concepts (so products not in market yet), , responses recorded numerical variables. there @ least 1 missing value in each row in dataset because dataset encapsulates many different surveys, each of 1 product concept. there no predetermined target variable.

i asked come machine learning model based on dataset. i've tried out supervised learning (regression , classification) feel still not right, since there no target variable (so kind of randomly took 1 variable in dataset target variable). unsupervised learning clustering sounds plausible, have no idea can achieve that.

in short, i'm lost regards sort of predictive model(s) can build such dataset. i've talked person regression/classification models, , said they'd rather have dataset analyzed in such way can automatically insights (insights predictive models suitable dataset, is), instead of me assigning target variable , implementing supervised learning.

i'd appreciate if can give me directions regards how approach problem. in advance.

i'm working python i'd prefer can done scikit learn , similar ml packages.





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