Estimating effort of building machine learning model I know it depends on the problem and various other factors like data availability, the complexity of the use case, the workload of developer ..etc, but can someone suggest effort estimation of building a minimum viable product.

I have taken the following parameters into consideration while preparing effort estimation(close to 3months) for building a price prediction model based on two problem definitions(no sample data supplied by client), which would be integrated with web interface. 

Data loading &amp; pre-processing - 1 week
Development of model - 2.5 month 
Deployment - 2 weeks.

not sure whether it's overestimated or underestimated.

Note: Please do not close with the comment "too broad" as I do not have much/can not provide more information. Requesting expert advice.
Data Loading &amp; Pre-Processing : depending on your data-size, this is a highly variable measure. we can agree that this will be the second longest task.


Edit : This phase ,for me, was about collecting the relevant columns and structuring a csv file to be read and loaded in a python environment.


Model Development : again, this will highly depend on your data ( and i'm referring to complexity and structure of your data rather than the size) , if you have a lot of features at hand, and your problem is complex ( demands a lot of effort in feature engineering and feature selection ) , this will be the longest task at hand and will probably take up most of your time
I would say deployment would take something in weeks like you mentionned depending on the complexity ( yes again ) of your production environnement.


Please take my statement lightly as i don't have much experience in this field. I've had the opportunity to deploy some models as webservices, and it took me less than your projections. However, our problems are quite different.. so my numbers could be out of match.