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Welcome to DSSE, Abdul. So, you trying to predict pipe corrosion based on water flow. I assume that the corrosion measurement is taken in different timesteps than the water flow measurements. If you have a fixed timestep for your corrosion measure both sum and average would work just fine, as this would be two features scaled by $\dfrac{1}{n}$, where $n$ is ...

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If you are looking to find effect of each feature on CTR then a correlation matrix can help you with it and you can do statistical test to prove it. But if you want to find hidden causes then it a classical problem of prediction vs causation. Suppose we find that giving discount help increase our campaign you may conclude that giving discount helps but it ...

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If you use any forecasting model, then it will do over fitting on your data set, as you have just 19 observations. At least 1000 observations are the good point to start applying any machine learning model. I need to predict Response Rate, % Promoters, % Detractors and % Neutrals all of which are numeric variables. You can do data exploration where you ...

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There certainly is theory, or at least competing methodologies, behind ETL and Data Warehousing, for a start look at the Inmon vs Kimball methodologies. In a nutshell (I could talk for days on this subject), Bruce Inmon's (the Father of Data Warehousing) methodology revolved around building a large, loosely 3rd normalized data warehouse from multiple sources,...

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First of all I just want to say that I am not a data engineer and there is definitely someone out there that can answer this better than me. I do think that there is a lot of theory behind data engineering. It is also very interesting. I too thought that it was boring and I was more interested in just data science/ machine learning. I am not sure if I can ...

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