Requirement for agriculture

Data

For any analysis, data are important. Data analysis, machine learning and AI involve one common and important feature called “data”. If we do not have data, then we cannot train a model. Big enterprises spend money in big quantities to collect the data. Data can be in any form such as value, text, picture, etc..

We can divide the data into three parts for machine learning:

Training data

It is a part of the data used for training of the model. From these data, our model learns and looks at the input and output.

Validation data

This part of the data is used for continuous assessment of the model. Here training dataset fits along with the changes involving hyperparameters. When the model starts working, then it is used for training.

Testing data

For unbiased evaluation in testing, the data model should be fully trained. In any model testing, data will be input and the model will predict some values without referring the actual output value. Hence testing data are useful for prediction. The predicted value obtained from the model is compared with actual output value for analyzing the result. As in any experiment, this process helps us to know how much a model is perfect from training dataset at the time of training.

After learning the need and types of data, its processing is most important.

In this process, the concept of machine learning arises. So now, we must understand the development of machine learning.