Machine learning Life cycle

In this page, We will learn about Machine learning Life cycle, What is Machine learning Life cycle?, What is Gathering Data?, What is Data preparation?, What is Data Wrangling?, What is Data Analysis?, What is Train Model?, What is Test Model?, and What is Deployment?

What is Machine learning Life cycle?

Machine learning has given computers the ability to learn on their own without having to be explicitly programmed. However, how does a machine learning system function? As a result, the machine learning life cycle can be used to explain it. A machine learning project's life cycle is a cyclic method for developing an effective machine learning project. The life cycle's primary goal is to find a solution to the problem or project.

There are seven important steps in the machine learning life cycle, which are listed below:

  1. Gathering Data
  2. Data preparation
  3. Data Wrangling
  4. Analyse Data
  5. Train the model
  6. Test the model
  7. Deployment

Understanding the problem and knowing the problem's purpose are the most critical aspects of the entire process. As a result, before beginning the life cycle, we must first comprehend the problem, as a positive outcome is contingent on a thorough comprehension of the situation.

To address an issue, we develop a machine learning system called a "model" in the complete life cycle process, and this model is built by providing "training." However, we need data to train a model, therefore the life cycle begins with data collection.

1. What is Gathering Data?

The first phase in the machine learning life cycle is data collection. This step's purpose is to identify and collect all data-related issues.

We must first identify the numerous data sources, as data can be obtained from a variety of places, including files, databases, the internet, and mobile devices. One of the most crucial stages in the life cycle. The output's efficiency will be determined by the quantity and quality of the data collected. The more data there is, the more accurate the prediction will be.

The following tasks are included in this step:

  • Identify various data sources
  • Collect data
  • Integrate the data obtained from different sources

We obtain a cohesive set of data, also known as a dataset, by completing the aforementioned task. It will be used in the following steps.

2. What is Data preparation?

We must prepare the data for further processing after it has been collected. Data preparation entails putting our data in an appropriate location and preparing it for use in machine learning training.

In this step, we combine all of the data and then randomize the order of the data. This step can be further broken down into two steps:

  • Data exploration: It is utilized to figure out what kind of data we're dealing with. We must comprehend data's features, format, and quality. An effective outcome is the result of a greater knowledge of data. Correlations, general patterns, and outliers can all be found here.
  • Pre-processing of data:The next step is to prepare the data for analysis.

3. What is Data Wrangling?

Cleaning and turning raw data into a usable format is referred to as data wrangling. It is the process of cleaning the data, picking the variable to utilize, and changing the data into a suitable format for further analysis. It's one of the most crucial parts of the entire procedure. To overcome the quality concerns, data cleaning is required.

It is not required that the data we have gathered be of constant use to us; part of the data may be useless. Data acquired in real-world applications may have a variety of problems, including:

  • Missing Values
  • Duplicate data
  • Invalid data
  • Noise

As a result, we clean the data using a variety of filtering approaches.

The aforesaid concerns must be identified and resolved because they can have a detrimental impact on the quality of the final product.

4. What is Data Analysis?

Now the cleaned and prepared data is passed on to the analysis step. This step involves:

  • Selection of analytical techniques
  • Building models
  • Review the result

The goal of this step is to create a machine learning model that will study the data using a variety of analytical approaches and then evaluate the results. It begins with the identification of the problem type, followed by the selection of machine learning techniques such as classification, regression, cluster analysis, association, and so on, followed by the construction of the model using prepared data, and finally the evaluation of the model.

As a result, in this stage, we take the data and build the model using machine learning algorithms.

5. What is Train Model?

The following stage is to train the model, in which we increase our model's performance in order to achieve a better solution to the problem.

Datasets are used to train the model, which is then used to train the model using various machine learning techniques. A model must be trained in order for it to comprehend the numerous patterns, rules, and features.

6. What is Test Model?

We test our machine learning model once it has been trained on a specific dataset. We check the correctness of our model in this stage by feeding it a test dataset.

The percentage correctness of the model is determined by testing it against the project or problem's requirements.

7. What is Deployment?

The final phase in the machine learning life cycle is deployment, which involves putting the model into action in a real-world system.

We deploy the model in the real system if the above-prepared model produces an accurate output that meets our requirements at a reasonable pace. However, before launching the project, we'll see if it improves performance by utilizing accessible data. The deployment step is equivalent to completing a project's final report.