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Stage 5: Data Evaluation

              The next stage is to test the model if it’s working properly or not. The following are the steps followed to test
              our model based on the above scenario:

               Step 1    We feed the data to the trained model. In this example, Name of the dish and the quantity produced are
                        fed to the trained model.

               Step 2   To feed the data of quantity of unconsumed food of the same dish on previous days.

               Step 3   The model then works upon the entries based on the training it got in the modelling stage.
               Step 4   The model predicts the quantity of food to be prepared for the next day.

               Step 5   The predicted quantity is now compared with the testing data. From the testing data, the quantity of
                        food to be produced for the next day should be total quantity minus the unconsumed quantity.
               Step 6   The model is tested with different datasets at least 10 times during training.

               Step 7   Now the predicted values and actual values are compared to check the efficiency of the model.
               Step 8   The model is said to be accurate if the difference between the predicted value and actual values are
                        similar. If not, then for better accuracy, either the model selection is changed or it is trained on more
                        data.

              After the model is tested and is found to be accurate, it is ready to be deployed in the restaurant for real time
              testing.


                       Data Collection


              Data collection is an age-old mechanism to keep a record of relevant things in our lives. Data collection is not
              rocket science. Every one of us collects and maintains data in some form or other. A tailor keeps his old patterns/
              stitches that he can use in a new dress. A baker keeps a pic of her cakes to keep them and use it as ideas in new
              bakes. Data collection is not difficult, data analysis is. Here, we require Data Science. Data analysis provides an
              insight to the collected data, it adds value to the dataset. It helps AI machines in the process of predictions and
              suggestions.

              Majorly the type of data used in Data Science based projects is numeric or alpha-numerical and that are in
              the form of tables or databases. There are many institutions that maintain such databases for maintenance of
              records. Some examples of datasets are given below:

                 • Banks
                 • ATM machines
                 • Movie theaters

                 • Hospitals
              If you look around you will see there are many such examples of datasets maintained in places like schools,
              coaching centers, offices, etc. These are helpful in gathering information for future reference.


              Sources of Data

              Any type of data required can be collected from various sources. Nowadays we have options of collecting data
              from online as well as offline sources. The following are some of the sources available:



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