Great Machine-Learning Practitioner's Outline

Let's talk about the 𝐛𝐚𝐬𝐢𝐜 𝐨𝐮𝐭𝐥𝐢𝐧𝐞 to be in our minds while working on 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬.
To the very basic, I break it down into 𝐭𝐡𝐫𝐞𝐞 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬.

1- 𝐏𝐫𝐞𝐩𝐚𝐫𝐞 𝐝𝐚𝐭𝐚:
𝐈𝐦𝐩𝐨𝐫𝐭 𝐃𝐚𝐭𝐚: Focusing on the method of fetching data from the source.
𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐃𝐚𝐭𝐚: Here, we perform the exploratory data analysis (EDA) to check the nature of the data, assumptions for the models, dimensionality reduction, feature extraction, data scaling, etc. This step is the combination of EDA and Data Preprocessing.
𝐒𝐩𝐥𝐢𝐭: Choose a wise split ratio and divide the set into train and split data.

2- 𝐁𝐮𝐢𝐥𝐝 𝐌𝐨𝐝𝐞𝐥:
𝐁𝐚𝐬𝐞𝐥𝐢𝐧𝐞: We set a baseline for model accuracy depending on the business problem, we will address.
𝐈𝐭𝐞𝐫𝐚𝐭𝐞: This step is pivotal in model building. Here we train the model to meet the baseline accuracy we set earlier. This step may involve multiple iterations of model building involving model tuning using traditional or novel approaches.
𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞: We need to test the finalized model passed through the loop of the iterate step. The behavior of the model on unseen data is evaluated. Any post-building assumptions for the model are also checked.

3- 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐞 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 - 𝐓𝐡𝐞 𝐜𝐫𝐨𝐰𝐧 𝐰𝐢𝐧𝐧𝐢𝐧𝐠 𝐬𝐤𝐢𝐥𝐥:
Today, many people can pick a dataset, explore it, and train a model over it whether by their skills or using Chat-GPT. But communicating results coupled with business understanding is not on everyone's plate. Communicating results is not just to give a numerical figure of accuracy or other metrics. It's a complete process of understanding the underlying problem, the business process, and the target audience. The success of the whole above-mentioned process lies in how well a machine-learning practitioner makes the audience believe that his designed solution is the best possible way to solve the subject problem. This success is achieved by strong communication skills, analytical skills, a receptive mind, and an understanding of the business. That's where a good machine-learning practitioner turns into a great one!

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