The development of predictive analytics using Machine Learning (ML) is a process that involves two stages: creating a model and refining it for accuracy. After multiple iterations, the ultimate model is produced and put into action. In this article, we will explore how to maximise the efficacy of predictive analytics with ML to uncover meaningful insights.
A Review of Machine Learning for Predictive Analytics
Predictive analytics is an invaluable tool for predicting future outcomes, such as a company’s estimated quarterly sales and profits. By using predictive analytics, organisations can gain insight into their potential performance and make informed decisions about their future. By analysing past trends and current data, predictive analytics can provide a reliable indication of what to expect in the future. This enables companies to better plan and manage their resources, allowing them to maximise their profitability and minimise risk.
In order to accurately forecast future sales, it is essential to have access to historical sales data. This data can be used to construct a dataset for training a machine learning model, which should include clean and organised data from descriptive analytics. By combining these two types of data, a comprehensive dataset can be generated which can then be used to build a reliable predictive model.
Once the model has been developed, it can be used to make predictions regarding future sales over the coming months. This can then be compared to the actual sales volume and financial earnings to ascertain whether the projected numbers have been met or exceeded. It is important to bear in mind that there may be discrepancies between the anticipated and actual results. In order to maximise the model’s predictive capability, it may be necessary to adjust certain constraints within the model.
Analytical Methods and Their Varieties
Distinct from one another, descriptive, diagnostic, predictive, and prescriptive analytics all exist.
- Descriptive statistics focuses on organising, correlating, summarising, and displaying data sets in order to spot trends.
- Analytics for diagnosis concerns itself with investigating causes and effects. Looking into why sales are going up or down is one such example.
- Anticipatory analytics Incorporating machine learning and statistical algorithms to make predictions about future events or outcomes that are currently not known is an essential part of forecasting. This approach allows for more precise predictions, which in turn can help organisations and individuals make more informed decisions.
- Analytics with a focus on what should be done next use both descriptive and prescriptive data to guide judgement.
There is a potential for an excessive amount of data to be present in a wide range of contexts. Unfortunately, there are currently no methods to instruct robots to perform specific tasks. The primary aim is for computers to draw upon the lessons learnt from the data and apply them to new sets of unknown data. This process is referred to as “machine learning”. A practical example of this could be if we wanted to determine the staff turnover rate of our business. We could utilise a machine learning model that has been trained on past data to predict whether or not a certain employee is likely to resign.
When it would be inefficient to create individual pieces of code for every variation of a given event, Machine Learning (ML) can be used as an alternative. For instance, what criteria should be used to decide whether a film uploaded to a sharing website is suitable for children? Or how can we gauge a show’s potential genre? With millions of videos added every day, it is nearly impossible to individually inspect and analyse each one. ML is particularly useful in this situation, as its algorithms are able to process large amounts of both structured (data in rows and columns) and unstructured (pictures, videos, text with emoticons, etc.) data.
Instructions for Making Predictive Analytical Decisions with Machine Learning
Predictive analytics using ML consists of eight separate procedures.
First, specify the issue at hand.
Initially, we gain an understanding of the current situation and its distinct boundaries, before selecting the most appropriate datasets for carrying out predictive analytics.
Example: A nearby supermarket provides an excellent opportunity to forecast grocery store sales over the next six months. To do so, we will be using a dataset that includes five years’ worth of historical grocery sales data, as well as the related revenue figures. This data will allow us to accurately analyse past trends and trends in order to make an informed prediction of future sales.
Phase 2: Information Gathering
In order to utilise machine learning for predictive analytics, it is essential that we determine which type of data is needed. Furthermore, it is of paramount importance that we verify the validity of the archival data in order to ensure its reliability.
As a part of our research, we are requesting the bookkeeper at a grocery business to provide us with historical sales data in some sort of electronic format, such as worksheets or billing software. We are specifically seeking information from the last five years.
Third, tidy the data.
Prior to training a model for predictive analytics, it is essential to preprocess the raw data retrieved to remove any inaccuracies, duplications, and missing information, as such data is unsuitable for use in the model. Preprocessing entails cleaning up a dataset by removing any redundant or irrelevant information.
Method 4: Conduct an Exploratory Data Analysis (EDA)
For Exploratory Data Analysis (EDA) to be effective, it is necessary to thoroughly investigate the dataset in order to uncover any patterns, outliers, or previously verified assumptions. Through this process, a clearer understanding of the primary features of the data is obtained. Commonly, data visualisation techniques are employed to assist in this process.
Construct a forecasting model (5th stage)
In Step 4, we use the data that has been cleaned and organised in Step 3 to identify patterns and build a predictive statistical machine learning model. This model offers powerful predictive analytics capabilities, enabling us to make predictions about the future of our grocery store company. Additionally, the model is portable, meaning that it can be used with multiple programming languages, not just MATLAB.
Examining the Presence of a Hypothesis
The use of a conventional statistical model allows for the exploration of hypotheses. The null hypothesis and its alternative can be established for evaluation. The alternative to rejecting the null hypothesis is not to do so.
Example: When customers purchase one bar of soap, they are eligible to receive one of the new face washes, complementary of charge, under the recently established “buy one, get one free” initiative. To illustrate the value of this program, let us consider the two scenarios outlined below:
For example 1: Soap sales did not rise as a result of the plan.
Second scenario: soap sales went up once the program was implemented.
For the first scenario, if there is no difference in the results, then we cannot reject the null hypothesis. For the second scenario, we must state that the null hypothesis is false.
Measure No. 6: Check the Accuracy of the Model
At this juncture, it is essential to evaluate the model’s performance using previously unseen data. The accuracy of the model’s predictions will then determine the level of effort that is required to refine and assess the model.
Step 7: Use the Model in Practice
Deploying the model to a cloud computing platform allows users to access the model and utilise it in a real-world setting. Through this platform, the model will be able to take user input into account in real-time and generate predictions accordingly.
Step 8: Check the Model’s Status
It is essential that the efficacy of the model be evaluated before implementation in the real world. In the realm of machine learning, “model monitoring” involves tracking the model’s performance when presented with real data. If it is determined that the data needs to be expanded and the model needs to be restructured, then the necessary enhancements can be made and the system can be redeployed.
Predictive analytics and how machine learning helps
The utilisation of machine learning algorithms has facilitated considerable advancement in the realm of predictive analytics. The ensuing eight examples will illustrate the progress that has been made through the application of these algorithms.
The use of Machine Learning for Predictive Analytics has enabled businesses to gain a better understanding of consumer preferences. It works by analysing the frequency with which a particular product is clicked on an online platform. For example, when a person purchases a t-shirt from an online store, the website may suggest other similar t-shirts when the customer returns. This can lead to customers buying a bundle of items at a discounted rate, which can be beneficial for retailers as it could lead to better client retention. Predictive Analytics is also valuable for inventory management, as it allows suppliers to be informed of potential stockouts before they happen.
Assistance to Clients
Predictive analytics provides valuable insights that can be used to segment customers into distinct groups with common characteristics. For example, consumers may be categorised into groups such as those who purchase t-shirts, and those who purchase books. By analysing the features of each group, companies can develop specialised marketing plans tailored to the needs and behaviours of each subset.
The utilisation of machine learning technology in predictive analytics can be highly beneficial in terms of detecting customer dissatisfaction and creating strategies that are tailored to ensure customer loyalty and attract new customers. By utilising machine learning, organisations can gain a deeper understanding of their customer base and develop suitable solutions to address customer needs.
The Process of Diagnosis in Medicine
When it comes to diagnosing patients, the use of machine learning models that are trained on extensive and varied datasets can provide a more thorough assessment of symptoms and lead to faster, more precise diagnoses. Furthermore, predictive analytics can be used to identify factors that have previously caused hospital readmissions, thereby allowing healthcare providers to enhance the quality of care they deliver.
Predictive analytics can enable hospitals to better prepare for potential surges in patient volume, such as during the current COVID-19 pandemic. By analysing historical data and current trends, hospitals can gain a better understanding of their expected patient load in the coming weeks and months. This information can help them anticipate the need for additional hospital beds and personnel in order to provide quality care to those in need. For example, if hospitals are aware of the projected number of COVID-19 cases for the upcoming month and an anticipated increase in the number of seriously infected individuals, they may be able to better equip themselves to handle the situation.
Advertising and promotion
The implementation of predictive analytics can enable companies to better anticipate the needs of their target demographic by exploring historical data related to consumer behaviour and market trends. By organising sales and marketing as a data-driven process, businesses may be able to more effectively reach their objectives. Furthermore, through the use of demand forecasting, organisations may be able to gain insight into the future demand for certain products.
Banking and insurance
The financial services sector stands to benefit greatly from the application of predictive analytics powered by machine learning to detect fraudulent behaviour. Through the use of datasets, these algorithms are trained to identify patterns associated with fraudulent transactions, allowing for the creation of models that can accurately anticipate and protect against potential fraudulent activities.
Real-time web traffic analysis is made possible through the integration of machine learning techniques. By leveraging sophisticated statistical methods of predictive analytics, it is possible to detect abnormal trends and anticipate potential cyberattacks. This not only reduces the need for manual labour, but also enables automatic collection and analysis of data related to the attack, providing useful insights for decision-makers.
Manufacturers can make use of machine learning and predictive analytics to monitor their machinery, alerting them of any potential breakdowns before they occur. Additionally, they can anticipate shifts in the market, reduce the risk of accidents, enhance their key performance indicators, and ultimately improve the quality of their manufacturing processes.
Human Resource Management Databases (HRIS)
The Human Resources (HR) department may use predictive analytics and machine learning to track and monitor employee turnover rate. By utilising datasets that include information regarding an employee’s pay, benefits, and other monetary elements, these models can be trained to gain insight into why workers have chosen to leave their positions. Once the models have been trained, they can provide HR with an educated prediction as to whether or not they may expect a high turnover rate among new hires.
The implementation of software solutions that incorporate predictive analytics and machine learning (ML) has enabled a more efficient one-click forecasting process. However, there are several challenges that must be addressed in order to fully realise the potential of this technology. These challenges include the need to upgrade to the latest ML algorithms as technology advances, the high cost of predictive analytics software and data processing, difficulty in finding experienced professionals to deploy predictive models, and the considerable time and effort required to prepare and analyse the relevant datasets. In order to reap the full benefits of this technology, the aforementioned obstacles need to be overcome.