Machine learning has become a prevalent technology owing to the endless benefits it offers. It includes numerous models and techniques. You may have interesting ideas for your Machine Learning project. However, it is essential to know the stages in a Machine Learning project or the lifecycle of an ML project.
Step 1: Problem Statement
For starters, you must thoroughly understand the problem. Every project intends to resolve a particular problem. You shall be able to analyze the problem and come up with a clear problem statement. For example, the Machine Learning project could be around the development of a sales prediction statement. It would be best to define the reason for developing such an application and how it will solve the existing business problems. The problem, in this case, could be the inability to manage large datasets or other complexities.
You must link the problem statement with the project goal. Decomposition is one technique that can help break down the business problem and help define the goal for your machine learning project.
Step 2: Data Collection or Data Acquisition
Data is the most critical element in any of the Machine Learning projects. The second step in your ML project shall be data acquisition and collection.
You shall first look for suitable data sources for your project. Many open-source datasets, such as Google Dataset Search, Amazon Web Services, Kaggle, etc., are available on the web. You can use these datasets for personal/research projects. You can then look for project-specific data sources. For example, the Machine Learning project can include designing a music recommendation system. Spotify, Amazon Music, and other music applications are good sources for acquiring the data. Similarly, your project may be on a recommendation system for streaming apps. You can explore Netflix, Amazon Prime, etc., in such cases.
Social media can be a relevant data source for several projects.
Web scraping is one of the techniques to acquire data from web sources. It involves automatic data extraction using software codes and scripts. You can use this technique to obtain data from specific websites. If you intend to use supervised machine learning, data labelling will be crucial in this process.
You shall make it a point to validate the data you acquire. Industry experts can assist in this process. For example, if you are developing an ML-based fraud detection system, you can ask a banking professional to help in the data validation.
Step 3: Data Preparation
Data collection from varied sources will provide unstructured, structured, and semi-structured data. Most of the data from the web sources are in the unstructured form, such as free text, images, etc.
Data preparation is significant to remove the irrelevant data pieces and the unwanted noise from the data sets. The inadequacies in this process can adversely impact the performance of the machine learning model.
Fig 1: Data Preparation Processes
Three processes can help prepare the datasets for further steps. Data cleaning is one such process to remove unnecessary data and features. The procedure for structured and unstructured data varies. Structured data includes cleaning the inconsistencies and missing values. For instance, the free text, unstructured data includes cleaning the symbols, punctuation marks, etc.
Data transformation is another step to prepare the datasets for modelling and other phases. It is specifically essential for unstructured data. It is not possible to use unstructured data as is. To transform such data pieces into a usable form, PCA, LDA, etc., can be helpful.
Exploratory data analysis can also be relevant in the preparatory phase. The pre-analysis of the data sets is to find out specific patterns, anomalies and validate the assumptions.
Step 4: Data Visualization
By now, you will understand the data you will use for your Machine Learning project. Data visualization will provide you with further insights into the datasets.
You can use two types of plots to gain a better understanding of the data attributes. Univariate plots will provide you with a sense of the attributes, and you can determine the relationships using multivariate plots. Scatterplots and histograms are good choices for univariate plotting.
Step 5: Modelling
Machine Learning is an umbrella of several models and techniques. You can use supervised, unsupervised, or a combination of the two in your ML project.
Before you begin modelling, it is best to take some time to re-evaluate the choices. You shall determine the pros and cons of each model to ensure the most suitable option for your Machine Learning project. You can also create a validation dataset and test harness for the model you select. The division of the datasets for training and testing is another process you can follow.
Once you finalize the model, you can kick-start the modelling phase. For example, if you decide to model your project using Neural Networks, you can adjust the learning rate parameters and the number of layers accordingly. You can continue building the algorithm consequently.
Step 6: Evaluation
This is the final stage in the Machine Learning project lifecycle. You need to verify and validate the ML model. The outcomes of the model shall be as per the goals and expectations.
Performance measurement is one of the easier methods to evaluate the performance of the ML model. You can use performance metrics like accuracy, defect rate, silhouette score, and likewise to measure the performance. You can also ask your peers and experts to provide you with feedback on the ML model. Changes in the ML algorithm as per the findings can improve performance and quality.
The classification of the entire project is a series of steps and stages that can assist in keeping track of the project performance. It can allow you to determine the defects/errors and the root cause of such issues. You can work on such gaps and make sure that you achieve the goals and objectives. Each of the steps above is crucial and contributes significantly to the overall success of the Machine Learning project.