You travel to a different city after checking out the weather conditions based on past weather or a retailer trying to predict and improve sales after hyperinflation — these both examples shows real-life predictive analysis.
Taking predictions to the level next with Artificial intelligence is what we know as Predictive analytics in data science.
Predictive analysis is about predicting future outcomes, behavior patterns, and trends based on past data.
The power of predictive analytics makes organizations understand the current as well as their future trends in a better way. Predictive analytics models use various techniques and algorithms to assess past and present data to further predict the accurate and best possible outcome.
Every industry is now leveraging various predictive analysis methods to make insightful decisions. However, identifying which predictive analytics modeling technique best fit your business is the key to getting the most out of a predictive model.
For example, you wish to make up a cricket team of competitive players based on previous analytics and evidence-based data. In that case, your predictive analytical model will differ from the prediction model used for sales forecasting. Various predictive models have been developed now to meet specific requirements and applications. In this article, you’ll better understand “Predictive Analytics” in context of different models, its techniques, its algorithms,and how it works. So, without further ado, let’s get started!
Understanding the Predictive Analytics
Everyone anticipates what the future holds for them — Predictive Analytics, however, reduces your anticipation and predicts the likelihood of future outcomes based on historical data. In addition, if any changes occur in current data, the predictive models recalculate the predictions for you.
Predictive analytics, a branch of advanced data analytics, uses statistical modeling, data mining techniques, Machine learning, and deep learning algorithms to analyze patterns and accurately make future insights.
Forecasts suggest that By 2022, the Predictive Analytics market is expected to reach nearly $11 Billion in Annual revenue, as many businesses use predictive analytics techniques for everything from fraud detection to medical diagnosis.
Businesses rely on big-time predictive analytics to explore and uncover risks and opportunities. For instance, Global market leaders like Netflix use predictive analytics to track user preferences, helping them deliver a perfect user experience.
Similarly, Amazon uses past purchases and browsing history to recommend products to consumers.
Although these predictive models can predict anything in real-time with high speed, some models are a bit complicated to understand and predict when it comes to longer computations and analyzing complex data inputs such as quantum computing or computational biology.
5 Types of Predictive Models
1. Classification Model
As its name suggests, it classifies past and present data into different categories. Also, It is the simplest predictive model among others.
The classification model is the best fit to answer the questions in YES or NO, which helps make instant decisions. For example, this model helps to answer the questions like:
- Does this retailer go to make 100% sales this season?
- Will this loan be approved?
- Is the transaction completed?
Classification model is applied to every industry, especially Finance and Retail — carrying the power to retain the data easily and provide comprehensive analysis for answering the questions.
2. Clustering Model
The clustering model sorts the datasets into different groups based on their attributes. This predictive analytical model is a smart option, particularly for marketing campaigns — as they have to divide data into groups based on common characteristics to reach out to potential customers.
For instance, If you want to implement a targeted marketing campaign for your customers, you must go through a huge data set to design a customized strategy. Despite being possible, this is not the best option.
Here, Clustering models help you group interested customers into one group based on similar characteristics like purchasing history, so you can easily devise a customized strategy for them.
The clustering model further has two types – Hard clustering and Soft clustering.
Hard clustering model identifies whether a data point belongs to the right data cluster or not. While the soft clustering model assigns the data probability when joining a cluster.
3. Forecast Model
Incredibly versatile and most commonly used, the forecast model deals in metric value prediction, estimating a numeric value for new data based on learnings from past data.
The forecast model can be applied wherever historical numerical data is available. In addition, the best thing about the forecast model, which also made it popular, is that it includes multiple parameters at a time to predict a future outcome.
For example, a shoe manufacturing company can predict how many pairs they can produce per hour. The model will consider all factors that can impact the manufacturing rate, such as: is there any event coming by? Are prices for manufacturing products rising? Etc.
4. Outlier Model
While other predictive analysis models predict the future based on historical data, the outlier model deals with anomalous data from a given dataset for predictions. It analyzes anomalous data by itself or combined with other datasets and numbers.
This model works great in areas with persistent anomalous activities, such as Finance and retail.
It saves organizations thousands of dollars by quickly detecting anomalous patterns and fraudulent activities. For example, it identifies the unusual transaction behavior of a customer, spam calls from support systems, and false insurance claims.
5. Time Series Model
As its name explains, the time series model uses “time” as an input parameter to make predictions. This model works with various data points drawn from previous years and develops a numeric metric that further predicts outcomes within a specific period.
If you want to know the prospect of your business growth, the Time series model will help you foresee future changes over a specific time.
This predictive analytical model works differently from conventional forecasting models as it makes predictions by taking into account the progress of all business variables. Also, it considers all other risk factors that can probably impact the growth.
Use cases of the time series model include predicting customer demand in business, the number of students getting admission to the college, or the number of customers visiting the restaurant at the weekend.
Top 5 Predictive Analytics Algorithms
You can apply Predictive analytics algorithms combined with predictive analytics modeling techniques and machine learning tools — to predict future trends, gain a competitive edge over others, and create better products, such as Finance, marketing, medicine, and military operations.
These predictive algorithms try to achieve the lowest error possible while making predictions. Let’s talk about the top 10 powerful algorithms which predictive analytics models most commonly use:
1. Random Forest
Classification is a big part of machine learning, and the random forest algorithm deals with classification and regression problems. This algorithm is based on various decision trees that operate as one.
Each decision tree, usually trained with the “bagging” method, individually makes a class prediction in the random forest, and the class with the more votes becomes our model’s prediction.
Simply put: “Random Forest builds multiple decision trees and joins them together to get a more accurate future outcome.”
Furthermore, random forest adds more random values to the model while growing decision trees. This additional randomness allows the model to look for the best feature among random subsets of features while splitting the node. It results in a way better predictive model.
2. Generalized Linear Model (GLM)
Generalized linear model or GLM is a pro-predictive analytical algorithm. GLM itself is a broader term, enveloping other models, including linear regression, logistic regression, and Poisson regression.
GLMs are an extension of basic linear regression models, which predicts the response variable as a function of various predictor variables. These linear regression models work on several assumptions.
Suppose we use a straight line to show the relationship between a response and predictor variables. It means that a constant change in a predictor will also change the response variable. However, this assumption is often violated, so these models are extended into general linear models to deal with regular data.
3. Gradient Boosted Model (GBM)
Gradient boosted model (GBM) is an algorithm similar to Random forest but different in technique. It uses “Boosted” machine learning techniques to make the prediction work simpler.
This predictive algorithm is an assembly of decision trees (weak learners) but before generalization. Moreover, it builds a tree on top of another at one time. Every new tree helps in reducing the errors made by the previous one. It’s quite a long process but provides better outputs and reduces overfitting effects.
4. K-Means Algorithm
K-means is a highly popular machine-learning algorithm that places unlabeled data points into groups with similar characteristics. It’s a high-speed predictive algorithm primarily used in clustering models.
K-means always look for data points with common features to put in the same group so they can be easily analyzed. Moreover, the algorithm works incredibly if you wish to implement a personalized plan for large datasets. It shows its strength in various real-life domains like Academic performance, medical diagnostic systems, search engines, and wireless sensor networks.
Initially developed by Facebook, Prophet is an open-source algorithm for forecasting.
Widely used in time series and forecast models, it initially detects the following trends and seasonality from data, then combines them to make perfect predictions.
The prophet algorithm is extensively used in capacity planning, like allocating resources or setting sales goals for a business. Unlike manual forecasting, which requires more human workforce and hours to draw out accurate outputs yet provides inconsistent results, Prophet is an innovative and valuable alternative.
Flexible enough to include heuristics and valuable assumptions, Prophet is popular among forecasting models. Moreover, Its speed, reliability, and robustness while dealing with big data make it a magnificent analytical algorithm.
How do Predictive Analytics Models Work?
Though we know the strengths of Predictive models, they have weaknesses too. However, one of the most significant advantages these predictive models provide is that they are easily adjustable with standard business rules — they are flexible and reusable.
But ever wonder how these models work? We have an answer.
These analytical models run various algorithms on the available datasets to make predictions. Predictive models run these algorithms repeatedly to train the models. Moreover, the same data sets are applied to different models until the expected business objective is found.
Remember that predictive analytics models work iteratively — from pre-processing to data-mining until they have a strong understanding of required business objectives — followed by a data preparation process. Once it’s done, data is then modeled, evaluated, and deployed.
Predictive algorithms play the main character in these processes, determining the patterns and trends in available data sets with data mining and statistical techniques. Predictive models are built to perform particular tasks with intelligent algorithms like regression, clustering, or outlier algorithms.
Our comprehensive blog about Predictive modeling examples will tell you how predictive models work in every industry. Please give it a read!
How will the world change with the power of Predictive Analytics Models?
Predictive analytics is reshaping the world we live in.
AI, machine learning, and deep learning have unveiled opportunities to use predictive models in ways previously unknown in data science. However, the result is that Predictive Analytics is rapidly evolving — proactively identifying risks and opportunities in mainstream businesses leveraging big data.
The ultimate goal of predictive analysis is to go beyond analyzing what happened in the past and suggest what could be done to improve the scenario. Don’t you think that’s cool?