Machine learning lifecycle: To process data at every stage that results in models

In the machine learning lifecycle, data processing plays a critical role at every stage, ultimately leading to the development and deployment of effective models. From data collection and preprocessing to model training, evaluation, and deployment, each step requires careful handling of data to ensure accuracy, reliability, and efficiency. By leveraging various techniques such as cleaning, normalization, feature engineering, and validation, data is refined and transformed to extract meaningful insights and patterns. This structured approach to data processing enables machine learning practitioners to build robust models that can generalize well to unseen data and deliver valuable solutions to real-world problems.

CRoss Industry Standard Process for Data Mining (CRISP-DM)

As the 90’s progressed, the need to standardize the lessons learned into a common methodology became increasingly acute. Two of leading tool providers of the day – SPSS and Teradata – along with three early adopter user corporations, Daimler, NCR, and OHRA convened a Special Interest Group (SIG) in 1996 and over the course of less than a year managed to codify what is still today the CRISP-DM, CRoss Industry Standard Process for Data Mining. CRISP-DM was not actually the first. Nevertheless, within just a year or two many more practitioners were basing their approach on CRISP-DM.

• As a methodology, it includes descriptions of the typical phases of a project, the tasks involved with each phase, and an explanation of the relationships between these tasks.
• As a process model, CRISP-DM provides an overview of the data mining life cycle.

The life cycle model consists of six phases with arrows indicating the most important and frequent dependencies between phases. The sequence of the phases is not strict. In fact, most projects move back and forth between phases as necessary.

The CRISP-DM model is flexible and can be customized easily. For example, if your organization aims to detect money laundering, it is likely that you will sift through large amounts of data without a specific modeling goal. Instead of modeling, your work will focus on data exploration and visualization to uncover suspicious patterns in financial data. CRISP-DM allows you to create a data mining model that fits your particular needs.

In such a situation, the modeling, evaluation, and deployment phases might be less relevant than the data understanding and preparation phases. However, it is still important to consider some of the questions raised during these later phases for long-term planning and future data mining goals.

CRISP-DM Methodology

The CRISP-DM process or methodology of CRISP-DM is described in these six major steps:

• Business Understanding
  Focuses on understanding the project objectives and requirements from a business perspective. The analyst formulates this knowledge as a data mining problem and develops preliminary plan
• Data Understanding
  Starting with initial data collection, the analyst proceeds with activities to get familiar with the data, identify data quality problems & discover first insights into the data. In this phase, the analyst might also detect interesting subsets to form hypotheses for hidden information
• Data Preparation
  The data preparation phase covers all activities to construct the final dataset from the initial raw data

• Modeling
  The analyst evaluates, selects & applies the appropriate modeling techniques. Since some techniques like neural nets have specific requirements regarding the form of the data. There can be a loop back here to data prep
• Evaluation
  The analyst builds & chooses models that appear to have high quality based on loss functions that were selected. The analyst then tests them to ensure that they can generalize the models against unseen data. Subsequently, the analyst also validates that the models sufficiently cover all key business issues. The end result is the selection of the champion model(s)
• Deployment
  Generally this will mean deploying a code representation of the model into an operating system. This also includes mechanisms to score or categorize new unseen data as it arises. The mechanism should use the new information in the solution of the original business problem. Importantly, the code representation must also include all the data prep steps leading up to modeling. This ensures that the model will treat new raw data in the same manner as during model development

Characteristics of CRISP-DM

CRISP-DM’s longevity in a rapidly changing area stems from a number of characteristics:

• It encourages data miners to focus on business goals, so as to ensure that project outputs provide tangible benefits to the organization. Too often, analysts can lose sight of the ultimate business purpose of their analysis – the analysis can become an end in itself rather than a means to an end. The CRISP-DM approach helps ensure that the business goals remain at the centre of the project throughout.
• CRISP-DM provides an iterative approach, including frequent opportunities to evaluate the progress of the project against its original objectives. This helps minimize risk of getting to the end of the project and finding that the business objectives have not really been addressed. It also means that the project stakeholders can adapt & change the objectives in the light of new findings.
• The CRISP-DM methodology is both technology and problem-neutral. You can use any software you like for your analysis and apply it to any data mining problem you want to. Whatever the nature of your data mining project, CRISP-DM will still provide you with a framework with enough structure to be useful.

Advantages of CRISP-DM

The main advantage of CRISP-DM is in its being a cross-industry standard. It means this methodology can be implemented in any DS project notwithstanding its domain or destination. Below, you will find the list of basic advantages of the CRISP-DM approach for Big Data projects.

Flexibility

No team can avoid pitfalls and mistakes at the beginning of the project. When starting a project, DS teams often suffer from the lack of domain knowledge or ineffective models of data evaluation they have. Thus, a project can become successful only if a team manages to reconfigure its strategy and is able to improve technical processes it applies. Another advantage of CRISP-DM approach is its flexibility. This makes it possible for models and processes to be imperfect at the very beginning. It provides a high level of flexibility that helps improve hypotheses and data analysis methods in a regular manner during further iterations.

Long-term Strategy

CRISP-DM methodology allows to create a long-term strategy based on short iterations at the beginning of project development. During first iterations, a team can create a basic and simple model cycle that can easily be improved in further iterations. This principle allows to ameliorate a preliminarily developed strategy after obtaining additional information and insights.

Functional Templates

The amazing benefit of using a CRISP-DM approach is a possibility to develop functional templates for DS management processes. The best way to take as many benefits as possible from CRISP-DM implementation is to create strict checklists for all phases of the work. 

Computer systems now have the ability to automatically learn without being explicitly programmed thanks to machine learning. How does a machine learning system function, though? So, the machine learning life cycle can be used to describe it. Building an effective machine learning project involves a cycle known as the machine learning life cycle. The life cycle’s primary goal is to find a solution for the issue or undertaking.

Knowledge Discovery in Databases – KDD

The term Knowledge Discovery in Databases, or KDD for short, refers to the broad process of finding knowledge in data, and emphasizes the “high-level” application of particular data mining methods. It is of interest to researchers in machine learning, pattern recognition, databases, statistics, artificial intelligence, knowledge acquisition for expert systems, and data visualization.

The unifying goal of the KDD process is to extract knowledge from data in the context of large databases.

It does this by using data mining methods (algorithms) to extract (identify) what is deemed knowledge, according to the specifications of measures and thresholds, using a database along with any required preprocessing, subsampling, and transformations of that database.

An Outline of the Steps of the KDD Process

The overall process of finding and interpreting patterns from data involves the repeated application of the following steps:

1. Developing an understanding of
   1. the application domain
   2. the relevant prior knowledge
   3. the goals of the end-user
2. Creating a target data set: selecting a data set, or focusing on a subset of variables, or data samples, on which discovery is to be performed.
3. Data cleaning and preprocessing.
   1. Removal of noise or outliers.
   2. Collecting necessary information to model or account for noise.
   3. Strategies for handling missing data fields.
   4. Accounting for time sequence information and known changes.
4. Data reduction and projection.
   1. Finding useful features to represent the data depending on the goal of the task.
   2. Using dimensionality reduction or transformation methods to reduce the effective number of variables under consideration or to find invariant representations for the data.
5. Choosing the data mining task.
   1. Deciding whether the goal of the KDD process is classification, regression, clustering, etc.
6. Choosing the data mining algorithm(s).
   1. Selecting method(s) to be used for searching for patterns in the data.
   2. Deciding which models and parameters may be appropriate.
   3. Matching a particular data mining method with the overall criteria of the KDD process.
7. Data mining.
   1. Searching for patterns of interest in a particular representational form or a set of such representations as classification rules or trees, regression, clustering, and so forth.
8. Interpreting mined patterns.
9. Consolidating discovered knowledge.

The terms knowledge discovery and data mining are distinct.

KDD refers to the overall process of discovering useful knowledge from data. It involves the evaluation and possibly interpretation of the patterns to make the decision of what qualifies as knowledge. It also includes the choice of encoding schemes, preprocessing, sampling, and projections of the data prior to the data mining step.

Data mining refers to the application of algorithms for extracting patterns from data without the additional steps of the KDD process.

Model agnostic approach

A model agnostic approach to the machine learning life cycle involves the following major steps, which are given below:

1. Gathering Data
2. Data preparation and wrangling
3. Analyze Data
4. Train the model
5. Test the model
6. Deployment

An enterprise must be able to train, test, and validate machine learning models before deploying them into production in order to produce a successful model. 

In order to test, tweak, and optimize models to produce more value, it has become more crucial to cut down on the time required for data preparation. Teams may speed up machine learning and data science initiatives to create an immersive business consumer experience that speeds up and automates the data-to-insight pipeline in order to prepare data for both analytics and machine learning initiatives.

1. Gathering Data

The first stage of the machine learning life cycle is data gathering. This step’s objective is to locate and collect all data-related issues.

The different data sources must be identified in this step since data can be gathered from a variety of sources, including files, databases, the internet, and mobile devices. It is one of the most crucial phases of the life cycle. The effectiveness of the output will depend on the quantity and caliber of the data gathered. The prediction will be more accurate the more data there is.

This step includes the below tasks:

• 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 carrying out the aforementioned task. It will be used in further steps.

2. Data Preparation and Wrangling

Data preparation is the process of organizing the data in a way that will be useful for machine learning training.

This stage involves gathering all the data in one place before randomly sorting it.

This step can be further divided into two processes:

• Data exploration

To understand the type of data we have to work with, data exploration is performed. We must comprehend the qualities, formats, and properties of the data.A more accurate grasp of the data results in successful results. We discover correlations, broad trends, and outliers in this.

• Data pre-processing

Cleaning and transforming unusable raw data into a usable format is known as data pre-processing. It is the process of preparing the data for analysis in the following phase by properly formatting it, choosing the variable to utilize, and cleaning the data. It is among the most crucial steps in the entire procedure. In order to address the quality issues, data cleaning is necessary.

It is not necessary that data we have collected is always of our use as some of the data may not be useful. In real-world applications, collected data may have various issues, including:

• Missing Values
• Duplicate data
• Invalid data

As a result, the data is cleaned using a variety of filtering approaches.

The aforesaid problems must be found and fixed since they have the potential to reduce the quality of the outcome.

3. 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 examine the data with a variety of analytical methods and then evaluate the results. In order to develop the model using the prepared data, first determine the problems. Then choose machine learning techniques like classification, regression, cluster analysis, association, etc., and we evaluate the model.

Learn more about exploratory data analysis using data visualizations here.

4. Train model

The model must now be trained in order to increase its performance and produce better results when solving problems.

The model is trained using a variety of machine learning algorithms using datasets. A model must be trained in order for it to comprehend the numerous patterns, rules, and features.

Become the best at training and deploying machine learning models.

5. Test model

A machine learning model is tested once it has been trained on a particular dataset. In this step, the model is given a test dataset to evaluate its accuracy.

Testing the model determines the percentage accuracy of the model as per the requirement of project or problem.

6. Deployment

Deployment, the final stage of the machine learning life cycle, involves integrating the model into a practical system.

The model gets deployed in the actual system if it is giving an accurate output that meets the requirements quickly enough. However, the project is evaluated to see if it is leveraging the data at hand to improve performance before deployment. The deployment phase is similar to making the final report for a project.

Introduction to Predictive Modeling

Predictive analytics uses methods from data mining, statistics, machine learning, mathematical modeling, and artificial intelligence to make future predictions about unknowable events. It creates forecasts using historical data. 

Based on past and present data, predictive modeling is a machine learning technique that forecasts or predicts anticipated future occurrences. Almost anything can be predicted using predictive models, from loan risks and weather forecasts to your next favorite TV show. Predictions frequently address issues like whether a credit card transaction is fraudulent or whether a patient has heart trouble.

To anticipate the future, predictive analytics seeks to identify the contributing elements, collects data, and applies machine learning, data mining, predictive modeling, and other analytical approaches. Insights from the data include patterns and relationships between several aspects that may not have been understood in the past. Finding those hidden ideas is more valuable than one might realize. Predictive analytics are used by businesses to improve their operations and hit their goals. Predictive analytics can make use of both structured and unstructured data insights.

Organizations have chosen to gather enormous volumes of data in recent years, believing that if they gather enough of it, it will eventually result in useful business insights. Even Facebook and Instagram offer analytics to corporate accounts. However, no matter how much data there is, it is useless if it is in its raw form. It becomes increasingly challenging to distinguish important business information from irrelevant data when there is more data to sort through. A data insights strategy is based on the idea that in order to fully utilize data, one must first decide why they are using it and what commercial value they want to derive from it.

Gathering insights from data

Here is how to obtain insights from data and make use of it:

1. Defining the problem statement/business goal.

Establish the project’s objectives, deliverables, scope of the work, and business goals. Create a questionnaire to collect data depending on the business objective.

2. Collection of data based on the answers to the questions created based on the problem statement.

Based on the questionnaire, collect answers in the form of datasets.

3. Integrate the data obtained from various sources.

Data from many sources are prepared for analysis using data mining for predictive analytics. This provides a complete view of the customer interactions.

4. Data Analysis

Examining, cleansing, transforming, and modeling data with the aim of identifying pertinent information to draw a conclusion is the process of data analysis.

5. Validate assumptions, hypotheses and test them using statistical models.

Statistical analysis enables validation of the assumptions, hypotheses, and tests them using statistical models.

6. Model generation

Algorithms are used to construct models that automate the process of combining new and old data. To improve outcomes, multiple models can be mixed.

7. Deploying the model

By automating the decisions based on the modeling, predictive model deployment offers the option of deploying the analytical results into the everyday decision-making process to provide results, reports, and output.

Poor models and accuracy due to incorrect or inadequate data might result in chaos. To get insights and train the model, a suitable dataset is also absolutely essential. Although predictive analytics has its own difficulties, it can produce priceless commercial results, such as stopping customer churn, optimizing business spending, and satisfying customer demand.

Models and Algorithms

Predictive analytics uses a number of methods from fields like machine learning, data mining, statistics, analysis, and modeling. Machine learning models and deep learning models are two major categories for predictive algorithms. Despite having unique advantages and disadvantages, they all share the ability to be reused and trained using algorithms that follow criteria specific to a given industry. Data gathering, pre-processing, modeling, and deployment are all steps in the iterative process of predictive analytics that results in output.

Once a model is built, we may input new data to generate predictions without having to repeat the training process, but this has the drawback that it requires a huge quantity of data to train. Because predictive analytics relies on machine learning algorithms, it needs accurate data classification in labels to function properly and accurately. The model’s inadequate ability to generalize its conclusions from one scenario to another raises concerns about generalizability. Although there are certain problems with the conclusions from a predictive analytics model’s applicability, these problems can sometimes be resolved using techniques like transfer learning.

Predictive analytics model

CLASSIFICATION MODEL

Of all the models, it is one of the easiest. Based on what it has discovered from the old data, it classifies fresh data. They can be utilized for multiclass classification as well as binary classification by responding to binary questions such as True/False and Yes/No. Some classification techniques include Decision Trees and Support Vector Machines.

Eg. : Loan approval is a classic use case of a classification model. Another example is spam detection messages/emails.

CLUSTERING MODEL

A clustering model clusters data points according to their shared attributes. Despite the fact that there are numerous clustering algorithms, none of them can be deemed the best for all application scenarios. It is an unsupervised learning algorithm, as opposed to supervised classification.

Eg.: Grouping students from a school-based on their location in a city for commute services. Grouping customers based on their item preferences to recommend products related to their interests.

FORECAST MODEL

It deals with metric value prediction, calculating a numerical value for new data based on the lessons from prior data, and is one of the most popular predictive analytics methods. It can be applied wherever numeric data is available.

Eg.: Traffic prediction at a city’s main road during different periods.

OUTLIERS MODEL

It is based, as the name implies, on the dataset’s anomalous data items. A data input error, measurement error, experimental error, data processing mistake, sample error, or natural error can all be considered outliers. Although certain outliers can lead to subpar performance and accuracy, others aid in the discovery of uniqueness or the observation of fresh inferences.

Eg.: Credit/Debit card theft.

TIME SERIES MODEL

It can be used for any sequence of data points with a time period as the input parameter. It uses the past data to develop a numerical metric and predicts the future data using that metric.

Eg.: Weather prediction, Share market/cryptocurrency price prediction.

Random Forests, Generalized Linear Model, Gradient Boosted Model, K-means clustering, and Prophet are a few popular forecasting algorithms. Combining decision trees, random forests use the “bagging” or “boosting” strategy to try to attain the lowest error possible. A more advanced variation of the general linear model that trains very quickly is the generalized linear model. Any type of exponential distribution type for the response variable can provide a clear insight of how the predictors affect the result.

Predictive Analytics as said already has many applications in different domains. To mention a few, 

• Healthcare
• Collection Analytics
• Fraud detection
• Risk Management
• Direct Marketing
• Cross-sell