Posted On: Apr 02, 2020
The seven steps in building a machine learning model are,
Data Collection - In this step, we collect the data related to the problem.
Data Preparation - Here, we clean and organize the collected data based on the problem. We remove duplicate data, error data, fill missing data, etc in this process
Choosing an algorithm - As the name suggests, in this stage, you choose the appropriate algorithm for the problem.
Train the algorithm - We use the dataset to train the algorithm to create a model.
Evaluate the model - We use the test data from the dataset to find the accuracy of the model created.
Parameter Tuning - In this step, we tune the model parameters to improve its performance.
Make predictions - In this step, we apply the created model on a real dataset.
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Supervised and unsupervised are the two types of Machine learning algorithms available. In the supervised type, the algorithms are applied to the known labeled data to formulate a model. Labeled data...