Steps to implement a Machine Learning Algorithm

It’s always a good idea to follow the predefined steps to get a better result for any work. In the field of Machine Learning, there are few steps that need to be followed to get the desired result. Let me list out the basic steps to be followed while working on data.

Below are the steps that need to be followed in a sequential manner.

• Collecting the data – The first step to be performed is to collect the data which can be done in many ways like web scraping or using a dataset available on the internet.
• Analyzing the data – The next step is always to figure out what data exactly are you having in the hand. How different variables are interrelated to each other. Which of the variables can be ignored. For example, suppose we have a dataset of Student details which contains Name, Roll number, Height, Weight, Gender, NationalITy, Student Absence Days and Class. We need to build a machine learning model out of this data. Then, we need to find out that few details are of no use like Roll Number or Name. Post that we have to see the relationship between different variables. This is what Analysis of Data means.
• Data Wrangling – After analyzing the data in hand, we have to clean the data. There might be some missing values that can either be removed or imputed with suitable values. This is an important step to clean the data since it has a direct impact on the accuracy of the model.
• Train and Test – After cleaning the data, implement the required machine learning algorithm to train the model. There are several machine learning algorithms to choose from and train the model. Choose the most appropriate algorithm as per the requirement. Once the training is completed, start testing the model with the test data.
• Accuracy Check – This is the step where you can find out how your model is working. There are multiple accuracy parameters that can be used like Confusion Matrix or RMSE value to name a few.

The steps mentioned above can be performed in different manners and depends on the individual and the requirements. The idea of listing out these steps is to tell you that these are steps that can be followed as a Thumb rule to build a machine learning model.

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