Predictive Analysis in Action for Forecasting and Trend Lines

Published in

Globant

13 min readApr 3, 2023

Data Analytics has transformed business and helped various sectors to gain their profits. The approach of scrutinizing the data to respond to the queries, point out the trends, and extract the insights — can lead you to the necessary information to blueprint and make affecting business decisions.

These can be performed in four different ways: -

"What happened?" Achieved through Descriptive Analysis
"Why did this happen?" This comes under Diagnostic Analysis
"What should we do next?" Accomplished through Prescriptive Analysis
"What might happen in the future?" This falls under Predictive Analysis

What will we cover in this article?

This article mainly focuses on the Predictive analysis types, models, techniques, and benefits. This is needed to draw the same in action for Trend lines revealing the overall direction of the data and forecasting by studying historical data and past patterns.

Let's go deep dive and focus on the one which is evolving fast in the different sectors and helping the industries/businesses best in the decision making. i.e., Predictive Analysis

In the world of competition, it's not sufficient to react to every finding or discovery and impromptu complications. Instead, companies/firms need to be progressive thinkers: foresee outcomes, take advantage of opportunities, and prevent losses. These all are becoming facile with the constantly growing and large volume of facts/data and are easily accessible to help the organizations to become more proactive and increase their worth. Detailed knowledge of predictive analytics is only possible with a solid descriptive, diagnostic, and prescriptive research background.

Predictive Analytics: This is a branch of data analytics that foresees future outcomes of events based on historical data, information, and facts. This determines the probability of upcoming incidents, and the same are calculated by using a broad spectrum of modern-day technologies and techniques that include various mathematical processes, statistical modeling, data modeling, artificial intelligence, machine learning, data mining, big data, and a lot more. These predictive analytic techniques can identify the patterns in data to recognize an organization's upcoming risks and opportunities. Using this, highly accurate predictions are made through multiple cycles of trial & error, and it's used by businesses to get deep insight into future events to improve decision-making and facilitate maximized sales/worth.

Steps involved in predictive analytics

Requirement gathering to understand a business flow: Understanding the demand before providing a solution is essential. Therefore, the first step involves gathering relative knowledge and information to outline a course of action. Next, you need to collect sufficient data to properly train the predictive model and identify predictive patterns.

2. Inspection of the business data: You must analyze the data required to train the model. This means eliminating all unwanted information or noise and ensuring sufficient information for the flawless functioning of the model.

3. Preparing for the model: This is the most important step. Here, you need to prepare the product according to the results of your research. The modeling uses predictive analytic techniques like machine learning, big data, data mining, statistical analysis, etc. At the end of the training, the model will learn from the historical data and identify trends accordingly.

4. Evaluation of the prepared model: By working with business analysts and executing trial runs, you can understand whether the model makes sense and delivers according to the needs of the business. This step is necessary because complicated algorithms can lead to false predictions, negatively affecting the business.

5. Working on the flaws and repeating the entire cycle for accuracy: You can evaluate the accuracy by retraining the model with data sets. This continuous process will progressively increase the model's efficiency based on the feedback received.

6. Deploying the final product: When the model reaches a specific efficiency level, it can be deployed for practical use in real-world situations to solve real-time problems.

Prototype Models

Predictive analytics models form the base of data analytics. In addition, template and prototype models make it easier for users to convert current and historical data into mathematically proven predictions that provide future insights. The different types of models used in predictive analytics include:

Predictive Maintenance Model: it predicts the chances of business equipment reaching its breaking points.
Quality Assurance Model: This model is trained at predicting and preventing possible defects in a product, thus maintaining quality and providing customer satisfaction.
Customer Lifetime Value Model: It shortlists the customers who are most likely to reinvest in the services and products offered by a company.
Customer Segmentation Model: This model aims to segregate customers into segments based on similar purchasing characteristics and behavior.

Some common useful predictive analytics techniques/models

The following models are often used.

Regression Model

This statistical system facilitates the determination of patterns in data sets and establishes formula-based relationships between the variables. Regression models estimate the strength of a relationship between variables. The model tracks how actions (independent variables) impact outcomes (dependent variables) and uses that information to predict future impact. When an organization wants to predict a numerical value, a regression algorithm comes into the picture. By defining the relationship between variables, organizations can perform scenario analysis, also popularly known as 'what-if' analysis, to plug in new independent variables and see how they affect the outcome.

Useful case: Organizations might use a regression model to determine how a product's qualities affect the likelihood of purchase. By analyzing the relationship between the product's color and the likelihood of purchase, an organization might see a correlation between the yellow color product and more sales. Because correlation doesn't equal causation, the organization might explore how other factors affect the likelihood of purchasing, such as size, seasonality, or product placement. They can use these insights to help with marketing efforts or product development to determine which products might perform well. It can also be used to predict the relationship between reckless driving and the total number of road accidents caused by a driver or the effect on sales and spending a certain amount of money on advertising. Mostly as:

Financial forecasting (like house price estimates or stock prices)
Sales and promotions forecasting
Testing automobiles
Weather analysis and prediction
Time series forecasting

The most often utilized Regression Analysis methods are:

Linear Regression
Logistic Regression
Polynomial Regression
Ridge Regression
Lasso Regression
Quantile Regression
Bayesian Linear Regression
Principal Components Regression
Partial Least Squares Regression
Elastic Net Regression

A detailed discussion of the Regression analysis techniques is in this article.

Decision Trees Model

A decision tree is a visual chart that resembles an upside-down tree. Starting at the "roots," one moves down through a continually narrowing range of options, each of which describes a potential outcome of a decision. While decision trees solve all kinds of classification problems, they can answer much more complex questions when employed in predictive analytics. In simplest terms, this model places data in different segments known as 'branches' based on other variable parameters. Decision trees can be handled effortlessly and readily understood as data is extracted as per user requirements in a short period.

A decision tree is a supervised learning algorithm and a popular method for visualizing analytical models. Decision trees assign inputs to two or more categories based on a series of "if-then" statements (known as indicators) arranged in the form of a flow diagram. The goal of using a decision tree is to create a training model that can predict the class or value of an input variable by learning simple decision rules inferred from training data.

Useful case: An airline might want to know the best time to fly to a new destination it's planning to serve on a weekly basis. It might also want to know what price point to set for such a flight and which customer segments to target. Given these factors, the airline can use a decision tree to gain insight into the consequences of selling tickets to x destination at price point y targeting audience z. The income of an individual whose income is unknown can be predicted based on available information such as occupation, age, and other continuous variables.

Decision trees are used for handling non-linear data sets effectively. Most often utilized types of Decision trees:

Categorical variable decision trees
Continuous variable decision trees

A detailed discussion of the Decision tree techniques is in this article.

Classification Model

Classification is a prediction technique that entails calculating the probability that an item belongs to a particular category. A problem with two classes is called a binary classification problem, while a problem with more than two classes is a multi-class classification problem. Classification models generate a continuous value that expresses the probability that an observation belongs to a particular class — also known as confidence. Classification models place data into categories based on historical knowledge. Classification begins with a training dataset where each piece of data has already been labeled. The classification algorithm learns the correlations between the data & labels and categorizes any new data. These algorithms are useful for sorting data into classes.

Useful case: They can help companies predict, if a particular website visitor is a "purchaser" or a "browser," or if a subscriber is a "monthly" or "yearly" type of customer. Classification models can help organizations more efficiently allocate resources, human or otherwise. For example, companies become better able to keep inventory at appropriate levels and prevent the overstaffing of a store at certain hours. The most common example of classification in a commercial use case is spam filters that label incoming emails as 'spam' or 'not spam' based on predefined criteria or fraud detection algorithms that flag anomalous transactions. Banks often use classification models to identify fraudulent transactions. The algorithm can analyze millions of previous transactions to learn what future fraudulent transactions might look like and alert customers when activity on their accounts looks suspicious.

Most often utilized types of classification:

Binary Classification
Multi-Class Classification
Multi-Label Classification
Imbalanced Classification

A detailed discussion of the Classification techniques is in this article.

Clustering Model

Clustering models place data into groups based on similar attributes. A clustering model uses a data matrix, which associates each item with relevant features. With this matrix, the algorithm will cluster together items with the same features, identifying patterns in the data that might previously have been hidden. Clustering is one of the most popular data mining techniques, which uses machine learning to group objects into categories based on their similarities, thereby splitting a large dataset into smaller subsets. Clustering is one of the most popular unsupervised classification techniques.

Useful case: Organizations can use clustering models to group customers and create more personalized targeting strategies. For example, a restaurant might cluster their customers based on location and only mail flyers to customers who live within a certain driving distance of their newest location. Clustering customers based on similar purchase habits or lifetime value, thereby creating customer segments and enabling the business to create personalized marketing campaigns at scale. This will help you to understand the preferences of your customers to scale up your business. Clustering can be divided into two subgroups:

Hard clustering means data points are directly assigned to categories. Each data point either belongs to a cluster completely or not.

Soft clustering assigns a probability that a data point belongs in one or more clusters rather than assigning that data point to a cluster.

Most often utilized types of Clustering:

Centroid-based Clustering
Density-based Clustering
Distribution-based Clustering
Hierarchical Clustering

A detailed discussion of the Clustering techniques is in this article.

Time Series Model

Time series models capture data points in relation to time. Because so much of the world's data can be modeled as a time series, time is one of the most common independent variables used in predictive analytics. A typical model might use the last year of data to analyze a metric and then predict that metric for the upcoming weeks. This allows organizations to forecast and explore multiple scenarios without wasting time or effort. Because time is a common variable, organizations use time series analyses for a variety of applications. A time series is a sequence of data points that occur over a period of time. In time series analysis, analysts record data points at consistent intervals over a set period rather than just recording the data points intermittently or randomly. Time series analysis typically requires a large number of data points to ensure consistency and reliability. An extensive data set ensures you have a representative sample size and that analysis can cut through noisy data. It also ensures that any trends or patterns discovered are not outliers and can account for seasonal variance. It can show likely changes in the data, like seasonality or cyclic behavior, which provides a better understanding of data variables and helps forecast better.

Useful case: This model can be used for seasonality analysis, which predicts how assets are affected by certain times of the year, or trend analysis, which determines the movement of assets over time. Some practical applications include forecasting sales for the upcoming quarter, predicting the number of visitors to a store, or even determining when people are most likely to get the flu. Industries like finance, retail, and economics frequently use time series analysis because currency and sales are always changing. Stock market analysis is an excellent example of time series analysis in action, especially with automated trading algorithms. Likewise, time series analysis is ideal for forecasting weather changes, helping meteorologists predict

everything from tomorrow's weather report to future years of climate change. Changes in average household income or the price of a share over time.

Most often utilized types of Time Series:

Measurements gathered at regular time intervals (metrics)
Measurements gathered at irregular time intervals (events)

A detailed discussion of the Time Series data is in this article.

Neural Networks Model

A neural network is a powerful computational data model that can capture and represent complex input/output relationships. Neural networks are biologically inspired data processing techniques that intake past and current data to estimate future values. Their design enables them to find complex correlations buried in the data, in a way that simulates the human brain's pattern detection mechanisms. Widely used for applications like image recognition and patient diagnosis, they consist of several layers that take input (input layer), calculate predictions (hidden layer) and offer output (output layer) in the form of a single prediction. Most neural networks use mathematical equations to activate the neurons, where each input corresponds to an output. A neural network is made by creating a web of input nodes (which is where you insert the data), output nodes (which show the results when the data has passed through the network) and a hidden layer between these nodes. The hidden layer is what makes the network smarter than traditional predictive tools, because it "learns" the way a human would, by remembering past connections in data and incorporating this data in the algorithm. However, this hidden layer represents a 'black box,' meaning that even data scientists cannot necessarily understand how the algorithm produces its computations — only the inputs and outputs can be observed directly.

Useful case: This is mainly used in speech, face, and pattern recognition. These are also used in function approximation applications like Power Restoration systems. This is used to find the edges in any image.

Most often utilized types of Neural Networks:

Perceptron
Feed Forward Networks
Multi-Layer Perceptron
Radial Based Networks
Convolutional Neural Networks
Recurrent Neural Networks
Long Short-Term Memory Networks

A detailed discussion of the Neural network types is in this article.

Artificial Intelligence and Machine Learning

In the context of predictive modeling, machine learning is a method of computational learning that analyzes the data and creates a model that fits the data. Unfortunately, these machine learning models are essentially black boxes, where the models are derived directly from the data as a consequence of machine learning, without relying on explicit programming by a human. Consequently, the effectiveness of machine learning techniques hinges on the quality of the training data. Data that is biased, obsolete, or inadequately represents the target population erodes the accuracy of the model's predictions. The advantage of machine learning is that it can derive patterns from millions of observations. The model then uses this pattern recognition to train itself to learn to recognize patterns in data it hasn't yet seen.

Useful case: Historical data shows that students with a higher GPA tend to earn higher incomes, the algorithm will predict income as a function of GPA. Recognizing your face at your nearby bank office to help you with a more personal experience. Self-driving cars that learn how to drive. It can foresee when credit card transactions are likely to be fraudulent or which insurance customer is more likely to put forward a claim.

Most often utilized types of AI/Machine learning:

Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforced learning

A detailed discussion of the AI/ML types are in this article.

Predictive analytics in action

Using predictive analytics generates several benefits:

Forecasting Future Cash Flow
Determining Staffing Needs
Behavioral Targeting
Preventing Malfunction
Early Detection of Allergic Reactions
Diagnosis Accuracy
Recommendation Systems
Calculating Credit Scores
Estimated Time of Arrival
Personalize the customer experience
Mitigate risk and fraud
Proactively address problems
Reduce the time and cost of forecasting business outcomes
optimize marketing campaigns
Gain a competitive advantage
Improve profit margins

Get started with predictive analytics tools and certain techniques through:

SAP Analytics Cloud
Alteryx
Tableau
SAS Advanced Analytics
RapidMiner Studio
TIBCO Statistica
IBM SPSS
KNIME Analytics Platform
H2O
Microsoft Azure ML
Advanced Analytics with Power BI, R & Python

Summary

In this article, we went over the various techniques of predictive analytics, including its associated benefits, types, and applications. We also looked in detail at specific, prediction-enabling analytics techniques with different tools. Predictive analytics is an emerging field that is creating widespread demand for itself. In fact, data analytics as a whole will be shaping industries in the future. Not only is it revolutionizing businesses and companies, but it has also played an integral role in generating mass employment. With the potential of an exponential boom imminent, data analytics and its related fields of study like Machine Learning and Artificial Intelligence will be impacting human lives marginally in the next five to ten years. Hence Predictive analytics is a rapidly growing field that confers clear benefits on the companies that carefully practice it.