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I'm trying to create a tool that can estimate how many days a project will take to complete, given dozens or hundreds of factors and parameters.

I don't have much experience with Artificial Intelligence / Neural Networks / Machine Learning but from my understanding, it is good for pattern recognizing. If I make an AI program and train it by feeding it with information on projects we have already completed (factors and number of hours took), it should be able to learn to estimate future projects.

Does anyone know if this is a realistic approach?

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You can build an expert ANN for how long individual tasks may take. There will be more numerous and frequent data which will allow for greater specialization by the system.

 

There are just way too many factors in the overall project for it to be able to accurately predicted without an enormous data set.

If the expert system proves successful, you could couple a governing ANN over the top, which takes the summed project time from all tasks (as predicted by the expert ANN) and some other project factors like total employees, time of the year, budget, etc, and trained on this data from previous projects; and predict from that.

Too much information can make for very noisy data, and non-sensible output in edge cases, especially in this case, where they may be a lack of abundant training data.

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