-orders : Order -robot_parts Robot_part - robot models: Robot model - customers:

Question

-orders : Order -robot_parts Robot_part - robot models: Robot model - customers: Customer - sales associates Sales associate +create new robot part) + create new robot model() + create new customer() + create new sales associate() + create new order) + save(filename : string) + open(filename: strin robot models -orders Robot model ustom Order -name string - model number: int torso Robot_part head : Robot_part - locomotor : Robot_part - arm : Robot_part - battery: Robot part + cost): double + max_speed(): double Customer - order number: int -date string name: string - customer number int Robøt_model - customer: Customer customer phone number string - sales associate Sales associate - email address strin Robot model : Robot model -Status: int + robot cost) double + extended price(): double max battery life(): double torso sales associates sales associate robot _parts Sales associate Robot part # name : string # model number : int # cost : double name: string - emplovee number int Hea - power:double Arm - max power double description : string # image filename : strin Battery Locomotor - power available: double - max ener - max power: double double Torso - battery_compartments: int - max arms: int

Explanation / Answer

Remember the quality of your inputs decide the quality of your output. So, once you have got your business hypothesis ready, it makes sense to spend lot of time and efforts here. With my personal estimate, data exploration, cleaning and preparation can take up to 70% of your total project time.

Below are the steps involved to understand, clean and prepare your data for building your predictive model:

Finally, we will need to iterate over steps 4 – 7 multiple times before we come up with our refined model.

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