6 Steps to a Successful Machine Learning Project

Creating a machine learning project is a complex process that requires a deep understanding of data science and statistical methods, as well as the ability to work with research, engineering, and product teams.

In this article, we will review and create a basic structure for the subsequent implementation of a machine learning project.

Photo by Edward Howell on Unsplash
Photo by Edward Howell on Unsplash

1. Project start: idea, requirements and data collection

The first step to successfully creating a machine learning project is to understand the essence of the problem, solve it and get the desired result.

Before you start designing a project, you need to understand the task, the data, and the context. You also need to know your goal and how it aligns with the capabilities of machine learning methods.

For example, you want the customer to return to the store again for a purchase. First you need to study the usual behavior of customers in the store. In addition, to develop a model, you need to study a typical user – their age, shopping habits, location, etc. If you have enough quality data at your disposal, then you can develop a machine learning model; however, if such information is not available at the user level (despite the fact that this problem is well suited for machine learning use cases), it will be difficult to build a model.

Some of the data suitable for model development may not be in the database or not in a suitable format, so you should think about making available data available during subsequent tasks in advance.

2. Data analysis

Data analysis helps you identify and make sense of data patterns in the context of your problem. This is where “real data science” begins because now you’re getting down to serious stuff and looking at the raw facts and figures without any preconceived notions of what they might mean.

This step examines the data in a variety of ways, such as adding new variables or changing existing ones, and then checking for any interesting relationships between those variables. For example:

  • Is there a correlation between age and salary for men? If so, how does this affect women working in these companies?

  • What happens when you compare one variable with another? Do they have any effect on each other?

This is an important part of the model development process. This is where you get to know your data and decide what questions you want answered before doing more detailed analysis and developing a model.

3. Data processing and function selection

Preprocessing is the process of converting raw data into a form suitable for analysis and model development. This is one of the most important steps that determines the success of the final model.

There are several ways to preprocess data. It may include one or more of the following steps:

  • Removing unnecessary variables from the data set;

  • Filling in missing values;

  • Reducing the size of the data set and the set of variables;

  • Convert categorical variables to numeric variables (or vice versa);

  • Data point normalization.

4. Model development

It’s time to build the model. There are many open source algorithms and methods available that you can choose from for your task, but it’s often best to start simple.

When choosing an algorithm, you can consider:

  • Data Size: How big is the data? How fast should it be processed? How much data does the algorithm need to train?

  • Problem type: What problems can this algorithm solve? Are there special requirements for data processing? How adequately does the model respond to missing data?

  • Availability: Are there libraries or packages for this algorithm?

5. Model evaluation

After developing a model, it is important to evaluate it and understand how to interpret the results before proceeding with the implementation of the algorithm.

One of the methods for evaluating models is cross-validation (cross-validation). In this process, you train a model on specific datasets and then test its performance on a completely different training set before moving on to real data. This way you make sure your dataset is balanced and the model works well in practice.

6. Implementation of the model

Now that you have your first model ready, the final step is to deploy the model to production. This is one of the most important steps in machine learning because it allows you to use data for real-world problems.

You can choose from two methods of model deployment: manual (someone has to go through the process step by step) or automatic (without human intervention).

However, each method has its drawbacks. For example, manual deployment takes a long time and requires more resources than automatic deployment; it also relies heavily on people who may not be experts in building software applications.

Automatic deployment is much faster and requires fewer resources than manual deployment. In addition, it does not depend on human participation.


Creating a machine learning project is a long process that requires a lot of time and effort before you get the expected results. At each stage of this process there are nuances, we will try to tell about them in detail in the following publications. We hope this post was helpful to you.

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