How to approach a Machine Learning Project

Do you have a project idea and don’t know where to start? Or maybe you have dataset but nothing strikes you on how to build a machine learning model? This article is for you. I’ll further talk about conceptual framework that you can use to approach any machine learning project.

Having a conceptual framework is the most important part in approaching a machine learning project. Conceptual framework allows others to understand how a problem was approached and turned to a project with correct execution. It also creates a standardized process to follow during data analysis and modeling. It encourages one to do better research and work strategically.

Taking a note of above thoughts, let’s move to the approach!

1.  Understand the problem

This is the step where the objective of the project is defined and hence the most important step of the whole process. A clear understanding of problem should be made. It should be understood which type of machine learning problem (Supervised, Unsupervised, Reinforcement) this is. All the assumptions with the problem statement should be taken in consideration.

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2. Get the data

This is a data centric step. It should be clear on what kind of data is needed, how much data is needed, where to get the data from, is the data legally managed, etc. Once you have the data, make sure you know what type of data it actually is (time series, observations, images, etc.), convert the data to a format you require of it, and create training, validation, and testing sets as required.

3. Application Architecture

This step is hugely important. It defines the various layers involved in machine learning cycle and involves the major steps being carried out in the transformation of raw data into training data sets capable for enabling decision making of a system. The application architecture has to be planned beforehand and is expected not to be replicated further once the model building is in progress as changing the application architecture down the road will be hugely expensive and disruptive.

4. Data Ingestion

Data Ingestion step is to prepare data for analysis. It usually includes steps called ETL, where E stands for Extract (taking the data from its current location), T stands for Transform (cleaning and normalizing the data) and L stands for Load (placing the data in a database where it can be analyzed).Enterprises generally have an easy time with extract and load, but run into problems with transform. It can result as an analytic engine sitting idle because it doesn’t have ingested data to process.

5. Data Preprocessing

Data Preprocessing is one of the preliminary steps that takes all the information available to organize it, sort it and merge it. This is a fundamental step performed to get more information from the data. The raw data can have missing or inconsistent values. There can also be redundant rows that can affect the accuracy of model falsely. In order to use an organized and clean data for building a machine learning model, we must follow techniques like data cleaning, data transformation and data reduction to get a better data set.

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6. Model Selection

Model selection is the process of choosing one machine learning model among many candidate models for a predictive modeling problem. There may be many competing parameters when performing model selection beyond model performance, such as complexity, maintainability, and available resources. Probabilistic measures and resampling methods are the two main classes of model selection techniques. It is a process that can be applied both across different types of models (e.g. logistic regression, KNN, SVM, etc.) and across models of the same type configured with different model hyperparameters (e.g. different kernels in an SVM).

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7. Model Tuning

This is a crucial step to maximize performance of the model and improve its accuracy without overfitting or creating too high variance. This can be accomplished by selecting appropriate hyperparameters. Hyperparameters are important as they control the overall behaviour of a model by minimizing the predefined loss function to give better results. We use validation set for tuning the parameters of a model.

8. Prediction

Prediction refers to the output of an algorithm after it has been trained on the desired dataset. The algorithm will generate probable values for an unknown variable for each record in the new data, allowing the model to identify what that value will most likely be. Machine learning model predictions allow businesses to make highly accurate decisions as to the likely outcomes of a question based on historical data, which can be about all kinds of things – customer churn likelihood, possible fraudulent activity, etc. These provide businesses with insights that result in tangible business value.

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9. Logging Framework

A logging framework is designed to standardize the process of logging in an application. This can also come in the form of a third-party tool. Logs are an essential part of troubleshooting application and performance of the infrastructure. When our code is executed on a production environment in a remote machine, we can’t actually go there and start debugging stuff. Instead, in such remote environments, we use logs to have a clear image of what has been performed and when. Log snot only capture the state of our program but also discover possible exceptions and errors in it.

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10. Deployment

This step is the end of the procedure we have followed to approach a machine learning project. Having said that, it also needs to be mentioned that it is one of the most difficult processes of gaining value from machine learning. It requires good coordination between data scientists, IT teams, software developers, and business professionals to ensure the model works reliably in the organization’s production environment. In order to get the most value out of machine learning models, it is important to deploy them seamlessly into production so that businesses can start using them to make practical decisions.

11. Model Monitoring

Models often depend on data pipelines and upstream systems that span multiple teams. Any changes or errors in those upstream systems can change the nature of data flowing into the models, often in silent but significant ways. Models degrade over time and the phenomenon of models degrading in performance is called ‘drift’. Models can degrade for a variety of reasons. The most common reasons fit under the categories of Data Drift and Concept Drift. Regardless of the cause, the impact of drift can be severe, causing financial loss, degraded customer experience and maybe worse.

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12. Model Retraining

Now that we have monitored our model, we have a way to check on our model’s health. Any record that is found unusual should be labelled and a check on classification metrics such as Precision, Recall, F1 score should be kept. If the results are not found to be satisfactory, the model should be retrained.

Following the above approach, it doesn’t seem that one will ever face difficulties in planning about the project. All you need to do is ink down your idea and follow the above approach to turn it into reality.

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