How AIot Can Master Automation For Businesses In The Future?


The ‘4.0’ Industrial revolution is here and Artificial Intelligence (AI) and the Internet of Things (IoT) are now more than just some buzzwords. The convergence of AI & IoT is AIoT (Artificial Intelligence of Things), which is going to be the biggest influence on industrial automation in years to come.

Both AI & IoT are individual powerful technologies but the inherent bond between the two can bring out exceptional results. When they come together AI acts as the brain making decisions and IoT acts as the nervous system executing them.

How AIoT functions?

Artificial Intelligence thrives by learning through data and deploys computational techniques and statistical methods to automate an industrial process. When this AI is merged with IoT technology, the devices will be able to understand inputs and interactions made by users and adjust or change without humans intervening.

IoT Vs. AIoT

IoT is all about data collection. When devices are connected with the Internet the technology within records every movement. In the case of AIoT, the devices not only collect data but also analyze it and look for data patterns and anomalies. Thus AIoT helps in making evolved decisions and improvise.

Why Businesses Should Use AIoT Technology?

Businesses can leverage this high-end technology, collect and analyze real-time data, and eventually bring a difference in the following ways:

  • Increased productivity by automating the manual tasks
  • Making use of bots and speech recognition for better customer engagement and interaction
  • Making business operations faster with improved insights
  • Optimizing industrial processes to minimize the cost
  • Quick data conversion to find a tangible value for businesses

AIoT Empowering Businesses

AIoT applications are innumerable and some of them are yet to be explored. AIoT has unrivaled potential and here’s how it can perform better:

  • To create a more in-depth value out of AIoT, other technologies like machine learning, Natural Language Processing (NLP) & computer vision need to be implemented side by side. For instance, making an advanced patient profile based on his medical history and engagement of the patient with medical devices such as a pacemaker.
  • For AIoT, to work at its full capacity it needs to have complete access to all data sets. This way it can analyze and choose the most valuable sets & work on deep insights to make a favorable decision.
  • The analysis implementation may differ with different scenarios i.e. the analytics can be purpose-oriented. In such a case, the AIoT will analyze only specific data sets and make a decision on the spot or otherwise.
  • To identify the most relevant incidents, event stream processing can analyze different data sets. This will help in identifying and analyzing any data in motion. Predictive analysis can help in making an on-spot decision. Continuous gathering of data and finding correlations helps devices make autonomous decisions.


AIoT has the ability to reform any business vertical. There are millions of devices, which can now be operated using this technology and several business use-cases can be tried and improved. The robust solutions offered by AIoT are worth the one-time investment as the benefits are long-term. If you are looking to make your business more productive then Winjit, a pioneer in AI & IoT services, can cater to your specific automation needs.

How AIot Can Master Automation For Businesses In The Future? 2020-09-02T12:34:01+00:00

Automated Visual Inspection Powered by AI in 2020


The brain and the human eye work in sync to identify, process, and analyze a piece of information. When this process is applied in a manufacturing or production line it’s called visual inspection.

With the advancement in technology, visual inspection is automated using Artificial Intelligence. Now AI-based visual inspection can detect the defects even the ones that are difficult to spot.

Concept of AI-based Visual Inspection

AI-based defect detection is an ensue of computer vision and deep learning (an aspect of machine learning). The machines learn through examples. They are provided with a neural network that possesses different sets of data. Upon implementation, the deep learning algorithm will allow the differentiation of characters and anomalies, which is similar to a human-based inspection system.

Let’s understand human Vs. AI-based visual inspection with an example.

There is a food-manufacturing unit that has employed a food inspector to keep a visual check on the process and machinery used to maintain the quality of the product. The job of the inspector is to notice anything out of ordinary such as a defect or a process gone wrong due to some machinery failure. Here the inspector will judge a situation based on his prior experience.

Now let’s consider a machine-based vision. It starts with data sets and once automation begins it mimics the same human-based inspection but with better capability. The machine’s deep learning, which is powered by a neural network allows it to make a decision based on its own learning during the process and is not influenced by prior experience.

The integration of an AI-based visual system in any business needs both hardware and software.

The hardware involves certain devices such as a camera for real-time streaming, GPU (does fast processing in case image-based deep learning model), and photometer & colorimeter for detailed visualization.

The software needs a Python framework for neural network & web solutions for data transmission. The data storage can be via local server or cloud streaming.

The Advantages Of AI-Based Visual Inspection

  • It commits a far greater efficiency

Visual inspection can be a tiresome job for humans as its repetitive in nature. Without a doubt, machines can work tirelessly without being influenced by emotions or needing to take a break. The output would be far greater than humans.

  • Negligible room for error

Once the data sets are good to go, it is highly unlikely for machines to make a mistake. But to err is human. Detecting the error and coming to a conclusion after hours/days of going through manuals and journals is subjected to mistakes.

  • Saves time and money

Absolute vitals for any business, AI-based visual inspections are the need of the hour. Finding the anomaly and reporting it will take far less time in case of a machine. You just to invest in automation setup once and enjoy the quick results.


AI-based visual inspection is already a part of the airline industry, healthcare, automotive, equipment manufacturing, and textile sector, etc. The automation has room from improvement even after deployment. The accuracy of the model can be increased over time by collecting new data and doing a model re-training. If your business operation demands huge investment on human visual inspection, its time you contact Winjit for a customized AI-based inspection. Winjit is India’s leading service provider of AI and IoT solutions globally. Connect with our service managers today!

Automated Visual Inspection Powered by AI in 2020 2020-09-02T12:24:14+00:00

4 Ways AI & ML Are Changing The Data Game For Businesses


Artificial Intelligence (AI) & Machine Learning (ML) is capable of mapping out the answers for businesses with respect to their target customers. For instance,

  • Which products are getting maximum attraction?
  • What pages are getting maximum traffic and why?
  • Why some products are not registering any sale at all? Etc.

The high-end technologies can provide deep insights to companies by analyzing real-time data and finding the patterns.

  • Data Empowering Lead Generation

Businesses usually follow the regular drill of reaching out to the audience, collecting the contact information, and then introducing them to their product/service to get sales & conversions. But the very first step of lead generation is challenging. If there is data present then AI & ML can help you figure out

  • How your website should look like?
  • How long or short your contact form should be?
  • What landing pages are essential to attract visitors?

If a user searches for high pricing products then your website or app should suggest only those products that fall in that range.

  • Data’s Valuable Insights For Optimization Of User Experience

A business tracks real-time data of users using cookies, which can then be put through these high-end technologies. Based on the choices user takes while navigating through the website such as clicking on the call to actions and other interesting web links, the businesses can optimize the website to enhance user experience. For instance, when a user lands on the website it can be optimized for the banners, the amount of text shown overall, the number of fields in a form, etc.

  • Treating each customer differently

Gone are the days when the same website greeted every customer. Today it’s a different ball game altogether. Thanks to AI & ML that tracks the data and identifies user behavior. Upon its application, the website can now show specific deals, appealing pop-ups, user-specific recommendations & landing pages, etc. If a user is interested to buy shoes the recommendation can offer different brands, colors, sizes, or types rather than a different product.

  • Biometric calculation

Will you be amazed to know that technology like AI & ML can actually record and register the facial expressions and eye movement? Yes, it true and already happening. If a user is viewing a landing page for a product sale then the technology will record which area of a landing page is getting maximum engagement. This biometric evaluation can be game-changing for businesses, especially those who are looking to sell high-end products like software, machinery, solar equipment, etc.


The future will be all about certainty and no guesswork. With a few A/B testing, the technology will be able to indicate what will work best for a business and what changes are necessary. Data, in the coming years, will be the key to unlock all the winning secrets for running a business.

In case your business has recently migrated to digital space or has been in the digital sphere for quite some time, you can get your hands in this technology too. Winjit, an emerging key player of AI & ML, can customize the technology to suit your business needs. Contact our business heads today and share your requirements.

4 Ways AI & ML Are Changing The Data Game For Businesses 2020-09-10T14:20:15+00:00

Power of Algorithms


The concept of Artificial Intelligence is one of the most buzzing topics in recent times. The reason it has taken over the world by storm is because it has introduced “human-like” behavior among machines and technology systems.

But Artificial Intelligence cannot become a transformative and revolutionary force on its own, it needs an effective algorithm, which is considered as the backbone of the whole concept.

There are many parameters in algorithms, which are based on statistics, and they can train models based on objectives and goals.

The models are modified according to need in order to avoid over fitting because the accuracy is directly impacted by it. The best possible way to address the situation is by implementing an ensemble technique. The accuracy of the machine learning algorithms is enhanced and improvised using the ensemble technique.

It is also denoted that the errors that occur in the outcomes are because of the factors such as variance, bias, and noise. Ensemble technique is known as the game changer in this direction, and it helps in achieving the desired results.

Ensemble Technique

Ensemble technique refers to that model of machine learning in which an effective model is developed based on various models. The basis on which the Ensemble Technique is based is that of training multiple models, and each of the models has the objective to classify or predict the set of results.

The ensemble or combination of various models into one more efficient model is described by three main terms given below:

Bagging: The function of bagging concentrates on creating multiple subsets of data from training sets, which are chosen samples and are randomly replaced. Each subset data is further implemented to train the decision tree. As a result of these activities, an ensemble of different models is achieved. The predictions of the different trees are implemented to achieve a more robust single decision tree.

Boosting: The term boosting is referred to an ensemble technique, which is implemented to create a collection of various predictors. This technique involves sequential learning in conjunction with the fitting of simple models to data, and then the data is analyzed for errors. In this approach, consecutive trees are fit with random samples at every step. The function of Boosting is aimed at solving net error from the prior tree.

Stacking: which means to increase the predictive force of the classifier

The most errors that occur in the learning of the model depend on three main factors; bias, noise, and variance. Ensemble method helps in increasing the stability of the model achieved finally and also helps in reducing the errors that are mentioned beforehand. The variance is reduced in most cases with the combination of various models.

This also happens even when the models are not great individually, as, after the application of Ensemble method, random errors are not encountered from a single source. Ensemble generally takes part in a group of methods, which is big in size and these are known as multi-classifiers. In multi-classifiers, set of hundreds or sometimes even thousands of learners that have a common objective are combined together in order to solve a problem.


“Intelligence” is developed in Artificial Intelligence because of the Machine Learning based algorithms. The more data an algorithm works on, the smarter it becomes and gives more accurate and efficient results.


Power of Algorithms 2020-07-28T14:10:00+00:00

Visualization Insights in machine learning


The development of the machine learning model has developed newer techniques, and in recent times it has changed almost every aspect of the tech world. The development is so vast that Machine Learning modules have also started to permeate in the everyday aspects, which are outside of the office. For a common man, it is easier to understand this way that Machine Learning is the basis through which Facebook knows what search results a person would like to see in the autocomplete feature. This is also the basis of Visualization Insights.

Visualization Approaches and Their Application

  • Predictive

Through the application of Predictive aspect, Machine Learning helps in a systematic combing throughout the data and identifies pre-determined patterns. Machine Learning keeps on learning from the ongoing data function and applies the learning in a future application. This helps business to be sure about predictions, without relying on “gut feel.”

  • Prescriptive Analytics

Prescriptive Analytics means when the system helps in “prescribing” various actions for a solution. In other words, analytics work as a piece of advice. Some actions are shortlisted, and they are guided towards a solution. It helps in quantifying the decisions to be taken in the future, and their possible outcome before even the decision is taken.

  • Anomaly Detection

Visualization helps in Anomaly Detection and prevents business from entering into an unwanted situation. Visualization helps in understanding and identifying any unwanted or abnormal values in the data, which are not required and can hamper the functioning of the whole system. Removal of such anomalies is important for the “healthy” functioning of the system.

  • Word Clouds

WordCloud refers to a particular technique of visualization, which helps in highlighting important textual data points out of a big text mass. This technique helps in the identification of important data points in the text and also potential features. In the WordCloud function, the common words that appear in the text are highlighted in large and bold format.

  • Histograms

When the numerical data and its distribution are accurately represented, the technique is called histogram. Histogram focuses on the estimation of probability and its distribution in the continuous variable aspect. The histogram is a plot that helps in the understanding of the distribution of features of the dataset. In the histogram, bins are created for numeric features, and then each bin is counted on the basis of a number of observations. These charts are closely related to each other, and the names are used interchangeably on various occasions.

  • Scatterplots

When two paired data samples are focused, and their relationship is taken into consideration, it is the function of Scatter Plot. It further means that various observations are recorded for any given observation, for example; the height and weight of any given person. The x-axis is the representation of the values of the first sample, and the y-axis is the representation of the other.

  • Decision Trees

Decision Trees is an essential model for understanding how models look like “gradient boosting machines” work. Although, the visualization packages are somewhat undeveloped and are not very helpful for the novice. The Decision Trees model helps sort this problem and works on the function of binary trees. It helps in understanding the relationship between observations in a training set and their target values.


When data visualization has become an inseparable part of various businesses, it is required to be efficient, informative, appealing and also predictive. With every day gone by, data visualization is becoming an inseparable part of our lives, and we need to understand its implications.

Visualization Insights in machine learning 2020-07-28T13:59:12+00:00

Data – The Heart of Machine Learning


More and more companies around the world are adopting Artificial Intelligence in their daily functions. The development of strategies for machine learning is crucial to gain an advantage over the competition. One of the primary components of the strategy used to develop machine learning is “data” which is used to draw solutions that are machine learning based.

Machine learning is a form of Artificial Intelligence that inputs a large quantity of datasets in order to teach computers how to react and respond like humans. It also helps in the optimization of operations for businesses and delivers a better experience for customers. It has several other benefits and enhancement of security is one of them.

How is data processed for better output?

Data being the centre point for all the essential strategies to work, here are few techniques, which are necessary before data is analysed.

  • Data Cleaning

The primary aim that is associated with data cleaning is to detect as well as remove errors along with anomalies so that the value of data can be increased. The more reliable the data is in analytics, the better decision making it would induce.

In other words, Data Cleaning refers to the process of detecting as well as correcting inaccurate and corrupt data from the database, table or record set. It also includes identification of incomplete inaccurate and incorrect parts of data, and then modifying, deleting or replacing the dirty or coarse data.

Data Cleaning can be performed interactively with the help of data wrangling tools, or it can be applied through scripting as batch processing.

  • Data Imputation

Data Imputation in machine learning is considered as the process deployed to replace the missing data with substituted values. When a data point is substituted, it is called, “unit imputation” and when a component is substituted, it is called “item imputation.” There are three main problems caused by missing data; I) It can induce a substantial amount of bias, II) It can make data analysis, more tiring, III) It can reduce efficiency. Methods of Imputation include; Single Imputation and Multiple Imputations.

  • Standardization

In Standardization, the mean is subtracted and then divided by the Standard Deviation obtained. Standardization results in the transformation of the data so that the mean is 0 and Standard Deviation is 1. The process associated with the rescaling of one or more attributes of the dataset in order to reach the mean value of 0 and the standard deviation of 1. The process of standardization is based on the assumption that the data includes Gaussian Bell Curve distribution. It does not have to be true, strictly, but the technique shows more effect if the distribution is Gaussian.

  • Normalization

Normalization aims at pre-processing of data so that burden is removed from Machine Learning (ML). It is a technique where the vector is divided by its length, and the data is transformed between the range 0 and 1. This denotes that each attribute has the largest value of 1 and the smallest value of 0. Normalization is considered to be a good technique when it is certain that the distribution is not Gaussian. The most common technique to normalize the attributes in the data set is by Weka, where Normalize filter can be applied.

The importance of Data cleaning

If Data Cleaning techniques are not applied, it invokes strange behavior of the output, and it cannot be relied upon. In the longer run, it induces wavered or incorrect decision making by the machine.


Machine Learning is widely based on the functioning of data. Data forms the basis on which further computation of data is based, and the functioning of the system is dependent on the functioning of the data and how it behaves. It is essential to feed correct data into the system and apply apt data interaction techniques.

Data – The Heart of Machine Learning 2020-07-28T13:59:19+00:00

Supervised Learning in Machine Learning


Supervised learning refers to the function of learning that includes mapping of the inputsin relation to the outputs. It is based on the derivation of input-outputpairs. Supervised Learning includes pairs, consisting of the input objects suchas a vector and output value that is desired, also known as a supervisorysignal.

Supervised Learning is deployed in the practical machine learning in the great majority. The input variables are (X), and the output variables are (Y), and the algorithm is used to learn the mapping function, which is then used to understand the function from input to output.

The goal of Supervised Learning is to approximate the function of mapping impeccably so that it gives a new data for input (x) and prediction for output variables (y) can be done for the data.

The reason it is called Supervised Learning is because of the process of algorithm learning that it involves from the training dataset, just like a supervisor in the learning process. When the correct answers are known, and the algorithm makes predictions iteratively, it is just like the supervisor or teacher that corrects the wavered flaws. The learning process is stopped when the algorithm achieves an optimum level performance and gives desired outputs.

How supervised learning helps businesses grow?

Supervised Learning is often termed as a “low hanging” fruit for the businesses that wish to initiate Machine Learning functions to enhance their businesses.

Here are some of the common usages of Supervised Learning in business:

–    Marketing and Sales

Machine Learning is commonly used in the marketing and sales function to predict customer timeline and future happenings. The common functions that are included are Lifetime Value, Churn Value and Sentiment Analysis.

–    People Analytics

Most of the companies deploy a digital function to track various behaviour patterns of the people involved with the business. The aspects that are ascertained by Supervised Learning include Sales Performance, Retention of Employees and Human Resource Allocation.

–    Time Series Market Forecasting

When time-dependent events are predicted through machine learning and statistics, it is called Time-Series Forecasting. This involves the forecast of seasonal or cyclic fluctuations.

–    Security

While most of the cyber security revolves around unsupervised learning, in some cases, supervised learning is deployed. Such incidents include Spam Filtering, Malicious Emails and Links and Fraud Detection.

–    Asset Maintenance and IoT

When Internet of Things is deployed in various functions of Asset Maintenance, Supervised Machine Learning is used in functions such as Logistics and Outage Prediction.

Method of Distinction

Method of Distinction is considered as a training method deployed in algorithms, such that they can take decisions on their own. The data can present several patterns that can be used for classification into a group or a category. Distinction applies to the method of categorizing and mapping the set of data that represents a particular type of attributes.

List of Algorithms Covered Under Supervised Learning

Some of the common algorithms covered for supervised learning include Nearest Neighbour, naïve Bayes, Decision Trees, Linear Regression, Support Vector Machines (SVM), and Neural Networks.

Current Applications of Supervised Learning

The various sectors where supervised learning finds its application are:

– Bioinformatics

– Cheminformatics

– Database Marketing

– Handwriting Recognition

– Information Retrieval

– Information Attraction

– Spam Detection

– Optical Character Recognition

– Speech Recognition

– Pattern Recognition

Supervised Learning enables the model to predict future outcomes after they are trained based on past data. Thus, the application for this type of machine learning is unlimited in the near future.

Supervised Learning in Machine Learning 2020-07-28T13:59:39+00:00

Anomaly Detection


Anomaly detection can be termed as a technique, which is deployed to identify various unusual patterns, which are not in collation with the expected behavior of the data. These unnatural occurrences are also termed as outliners. The application of Anomaly detection starts with the involvement of the business intrusion aspect in business, where it identifies unnatural patterns within the network traffic, which can eventually signal a system hack.

Another field where Anomaly detection is deployed is the health monitoring which is based on a system. It can help with the function of detecting a malignant tumor through an MRI scan. Anomaly detection also helps in fraud detection in the banking sector, where it can prevent the occurrence of unwanted financial transactions.

Types of Anomalies

In order to understand the various techniques that help in Anomaly detection, it is vital to comprehend various prevalent anomalies:

  • Point Anomalies: These can be termed as a single or “on-off” event of a particular data interaction, which is very different from the routine transactions. The common example in banking is when an absurd amount spent is detected in a credit card statement, which helps in detecting fraud.
  • Contextual Anomalies: This particular abnormality can be specified through context. The very common occurrence of such anomaly is within the time-series data. For example; it is common to spend about $100 on food every day during holidays, but on normal days, it is considered irrational.
  • Collective Anomalies: The incident, when a particular “set” of data collectively helps in identifying anomalies is known as Collective Anomalies. In real-time, it can be defined; when someone tries to copy a series of data from the local host the remote machine being pre-defined, it can be flagged as a potential cyber-attack.

Anomaly Detection Techniques

Various techniques are deployed to identify anomalies in a given data environment. These techniques work in conjunction with the nature of the business and the nature of the given data.

A. Simple Statistical Methods

The basic and the simplest way to identify the irregularities in data occurrences is by flagging the abnormalities. The data points are flagged on the basis of their uncommon behavior, other than the pre-set patterns based on mean, median, mode, and quantiles.

Various Challenges Associated with the Method:

  • Sometimes there is “noise” present in the data, which is similar to the abnormalities, which makes it very difficult to identify the anomaly.
  • The definition associated with the abnormality can be changed frequently, and the adversities adopt themselves very quickly.
  • The data pattern is based on seasonality.

B. Machine Learning Based Approaches

Given below are various machine learning based techniques to identify anomalies:

i. Density Based Anomaly Detection

This method is based on the “k-nearest” neighbour algorithm. Using a score does the evaluation of the nearest set of data. The score can be Euclidian distance or a measure similar to it, which is dependent on the type of data, whether numerical or categorical.

ii. Clustering-Based Anomaly Detection

In the domain of unsupervised learning, Clustering is considered as one of the most popular concepts. Clustering algorithm widely used the K-means. “K” is created in the similar cluster as the data points. Anomalies are identified as the data instances that fall outside of these defined groups.

iii. Support Vector Machine-Based Anomaly Detection

This is another very effective technique for detecting anomalies. It is associated with supervised learning and involves extensions such as OneClassCVM. The algorithm learns a soft boundry in order to cluster the data instances, which are “normal.” The instances that occur outside of the normal pattern are marked as anomalies.

Thus, anomaly detection is of great significance and finds application in various industries and sectors.

Anomaly Detection 2020-07-28T13:59:46+00:00

Feature Visualization in Machine Learning


When data features are deployed to train a machine, greater performance can be achieved through it. The reason behind this approach is to make humans understand and respond to the data more efficiently. The application of Artificial Intelligence is targetedtowards making algorithms more responsive towards the given data, in the sameway, humans are. An intriguing fact in this direction is that when theArtificial Intelligence succeeds beyond a point, Features Visualization willbecome redundant. The development of Artificial Intelligence works inconjunction with the relationship between algorithms and data.

The non-relevant features or the features that are partially relevant depict a negative impact on the performance of the model. The process of selecting some features from the data, so that they contribute more towards the output, is known as Feature Selection. The process is followed to reach a more favoured or intended output from the data. If there are too many irrelevant features in the data, it can cause inaccuracy in results. This is more prone to linear algorithms such as linear and logistic regression.

Various Tools Deployed in Feature Visualization

The best way to explain this part is with the common example of a restaurant. When we go to a restaurant, before ordering the food, we go through the menu and understand more about the options. Similarly, there are various tools, which are adapted to gain more understanding about the data and then make decisions.

Scikit Learn

The use of statistical tests can be implemented to select pre-determined features having the strongest relationship with the output variable. The ScikitLearn feature allows narrowing down on the best class that can be implemented in the suite having different statistical tests in order to select a specific number of features.


The type of data visualization, which is based on matplotlib is known as Seaborn. The features help in segregating the given data through informative and attractive statistical graphics. It is a dataset-oriented API used for examination of the relationship among multiple variables. The categorical variables are shown through specialized support of observations or aggregate statistics. The data is visualized as univariate or bivariate and is compared between subsets of data. The plotting function in Seaborn is dataset-oriented and operates on data-frames and arrays that contain whole datasets. The necessary semantic mapping is internally performed by statistical aggregation by producing informative plots.


Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits. Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc., with just a few lines of code.


The conjunction between Matplotlib and the very popular graphics library which is Python-based; d3js, is known as mpld3. It is a popular JavaScript, which is used in the creation of interactive data visualization for the web. API is exported from matplotlib graphics to HTML code as a result of its application. This code can be utilized within the web browser including standard web pages, tools or blogs, including IPython notebook. Mpld3 has the ability to add plugins to the plot, and this is one of the most interesting features of the extension. The objects that help in defining the interactive functionality of the visualization are known as plugins.

Feature visualization is a very detailed topic, and it needs deeper understanding before application. Every tool of feature visualization plays an important role and provides a better understanding of the nature of the data. Feature visualization makes machine learning more feasible.

Feature Visualization in Machine Learning 2020-07-28T14:00:13+00:00

Importance of Accurate Predictions in Machine Learning


Machine Learning has emerged as a coveted branch of Artificial Intelligence in the recent past and large businesses have started to rely upon it. The reason behind this is its ability to make predictions about a future trend or an event. These predictions are made without much programming and input. This is the basic reason why the element of “prediction” is considered a crucial trait in machine learning.

Importance Of Accurate Predictions And Its Impact On Business

It is estimated that almost 75% of the businesses worldwide exist on the principle of forecast in their regular business functions. Out of these 75%, only 60% are equipped with the predictive and analytical capabilities. The major hindrance in the way of adoption of the analytical capabilities is the application of the correct set of analytical tools. The prediction function begins with the identification of storage of digital information and this information is vast. By implementation of the algorithms of Artificial Intelligence, businesses can optimise a whole new pattern of statistical application and enhance their predictive capability.

Type of Data Analysis

The basis of analytical activity is the vast amount of data. It is the foremost activity of the management to ensure that the application of analytics would fulfil the business expectations and business goals, which is appropriate for the environment of big data.

There are three types of analytics that are applied:

Descriptive Analytics – This is the basic form of analytics that aggregates the big data and provisions important insights of past events.

Predictive Analytics – The next level in the process of analytics is data reduction. There are various statistical modelling and machine learning techniques that are applied in this function. The function predicts future outcomes on basis of past data.

Prescriptive Analysis – The combination of business rules provides a new form of analytics, which is based on machine learning and computational modelling. This is done to recommend the best possible course for the business with an intended pre-specified outcome.

Neural Networks – Basis of Data Analysis

Neural networks refer to the hardware setups and the software applied in the system. This function is similar to the central nervous system of humans. It estimates the functions, which are dependent on huge amount of unknown inputs. The three aspects taken in consideration by Neural Networks are architecture, activity rule and learning rule.

Application Of Predictive Capabilities In Various Businesses

The function of predictive analytics works according to the nature, department and industry of the business. Some of the uses of Machine Learning based predictive analysis are:

E-Commerce – The use of Machine Learning can help the business to predict the fraudulent transactions and customer churn. It also helps the business to predict which customer would make the required click.

Marketing – The marketing is clubbed with Machine Learning to identify and acquire prospective customers with attributes that are similar to existing customers.

Customer Service – The analytics help in the customer service by conducting historical customer satisfaction surveys. These surveys help in correcting several activities such as total time, resolution of ticket, response delay etc.

Medical Diagnosis – Machine Learning is very useful in medical facilities to predict a particular illness. The prediction is based on the database of previous patients and the symptoms displayed by them in past.

Predictions paving way for business insights

Thus, machine learning uses various statistics and algorithms to find a relation between different sets of data. The likelihood of an event occurring in the future can be estimated thereby offering actionable insights to the business. These predictions can be a way to plan your business’ future, let it be for sales, purchase or defining customer behaviors.

Thus, prediction is a crucial component when it comes to defining strategies and taking actions based on the available data. Therefore implementing machine learning to get accurate predictions is a total win-win.

Importance of Accurate Predictions in Machine Learning 2020-07-28T14:00:21+00:00
Load More Posts