Machine learning systems have the ability to automatically learn and improve from experience without having to explicitly modify the program. Machine learning focuses on the development of computer programs that are capable of accessing data and using it to learn for themselves. Machine learning technologies in the recent past have become quite powerful and integration of best machine learning software into our daily lives have deepened over the these years. Our dependency on this technology is likely to increase, thereby benefiting the community at large. We at Folio3 have dedicated a considerable amount of resources to this technology and have delivered numerous end-to-end projects related to machine learning solution.

15 Best Machine Learning Software in 2020

1) Amazon Machine Learning (AML) AML is a cloud-based and one of the best machine learning tools that developers with varying levels of skill levels can use. It is a managed service that is used to create machine learning models and generate predictions. Moreover, it has the capability to integrate data from multiple sources such as Redshift, Amazon S3, or RDS. Features:

• It offers wizards & visualization tools.
• Supports three types of models, i.e., multi-class classification, binary classification and regression.
• Allows users to use MySQL database and Amazon Redshift for data source object creation.  

Pros:

• It can be used for ML models, Data sources, Real-time predictions, Evaluations, and Batch predictions.

Tool Cost/Plan Details: Free Download link: https://aws.amazon.com/free/machine-learning/ 2) Oryx 2 A realization of the lambda architecture and built on Apache Kafka and Apache Spark; Oryx 2 is widely used for real-time large-scale machine learning. This framework enables users to build end-to-end applications for regression, filtering, classification and clustering. Features:

• It consists of three tiers: specialization on top providing ML abstractions, generic lambda architecture tier, end-to-end implementation of the same standard ML algorithms.
• Oryx 2 is the upgraded version of Oryx 1 project.
• It has three side-by-side cooperating layers, such as: speed layer, serving layer and batch layer.
• There is also a data transport layer that shifts data between the layers and serves as a recipient of external data.

Tool Cost/Plan Details: Free Download Link: https://jar-download.com/artifacts/com.cloudera.oryx/oryx-ml/2.7.2 3) Apple`s Core ML Core ML is a data science software tool by Apple. It is a straightforward model that helps users integrate machine learning models into their mobile apps. The simple process requires users to drop the machine learning model file into their project and the Xcode automatically builds a Swift wrapper class or Objective-C. Features:

• Domain-specific frameworks and functionality can be based on it.
• Core ML easily supports Computer Vision for precise image analysis, GameplayKit for learned decision trees evaluation and Natural Language for natural language processing.
• It is optimized for on-device performance.
• It builds on top of low-level primitives.

Tool Cost/Plan Details: Free Download link: https://developer.apple.com/documentation/coreml 4) Scikit-learn Scikit-learn is for machine learning development in python; offers a library for the Python programming language. Features:

• It facilitates data mining and data analysis processes.
• It offers models and algorithms for Classification, Dimensional reduction, Regression, Clustering, Model selection and Pre-processing.

Pros:

• Provides comprehensible documentation. 
• When calling objects, parameters for specific algorithms can be changed.

Tool Cost/Plan Details: Free Download link: https://scikit-learn.org/stable/install.html 5) Pytorch PyTorch is a Python machine learning library based on Torch, which is basically a Lua based scripting language, computing framework and machine learning library. Features:

• It helps build neural networks through Autograd Module.
• It offers a number of optimization algorithms for building neural networks.
• It  can be used on cloud platforms.
• PyTorch has distributed training, many tools and libraries.

Pros:

• Users can easily create computational graphs.
• Hybrid front-end makes it easy to use.

Tool Cost/Plan Details: Free Download link: https://pytorch.org/ 6) TensorFlow TensorFlow offers a JavaScript library which facilitates machine learning. Available APIs help you build and train the models. Features:

• It helps in preparing and building your models. 
• You can run your current models with the assistance of TensorFlow.js (a model converter). 
• It helps in the neural network.

Pros:

• It can be used in two ways, i.e. by script tags or by installing through NPM.
• It can even facilitate human pose estimation.

Cons:

• It is difficult to learn.

Tool Cost/Plan Details: Free Download link: https://www.tensorflow.org/install 7) Weka Weka enables machine learning algorithms that help in data mining. Features: It assists in Data preparation, Classification, Regression, Clustering, Visualization and Association rules mining. Pros:

• Provides online courses for training.
• Easy to understand algorithms.
• It is good for students as well.

Cons:

• Not much documentation and online support are available.

Tool Cost/Plan Details: Free Download link: https://sourceforge.net/projects/weka/ 8) KNIME KNIME is a tool for big data solution, reporting and integration platform. Based on the data pipelining concept, it combines different elements for machine learning and data mining. Features:

• It can integrate the code of programming languages such as JavaScriptC, Java, C++, R, Python, etc.
• It can be utilized for business intelligence, CRM and financial data analysis.

Pros:

• It serves as a good SAS alternative.
• It is easy to deploy, install and learn.

Cons:

• It is not easy to build complicated models.
• Offers limited exporting and visualization capabilities.

Tool Cost/Plan Details:  Annual Subscription  (based on 5 users and 8 cores) KNIME Server for Azure 25.000 EUR 29,000 USD KNIME Server for AWS 45.500 EUR 52,000 USD Download link: https://www.knime.com/downloads 9) COLAB Google Colab is a cloud service that supports Python. It utilizes PyTorch, Keras, TensorFlow, and OpenCV libraries in order to help you build machine learning applications. Features: It assists in machine learning education and research. Pros: You can access it via your google drive. Tool Cost/Plan Details: Free 10) APACHE Mahout Apache Mahout assists mathematicians, statisticians and data scientists in building and implementing their own algorithms. Features:

• It offers algorithms for Pre-processors, Regression, Clustering, Recommenders and Distributed Linear Algebra. 
• Java libraries are incorporated for regular math operations. 
• It follows Distributed linear algebra framework.

Pros: It works for large data sets. Is simple and extensible.  Cons:

• Limited documentation and algorithms.

Tool Cost/Plan Details: Free Download link: https://mahout.apache.org/general/downloads 11) Accord.Net Accord.Net offers Machine Learning libraries for ai image and audio processing. Features:

• It provides algorithms for Numerical Linear Algebra, Numerical optimization, Statistics and Artificial Neural networks.
• Plus, for image, audio & signal processing.
• It also provides support for graph plotting and visualization libraries.

Pros: Libraries are made available from the source code and as well as executable installer & NuGet package manager. Cons:

• It supports only Net supported languages.

Tool Cost/Plan Details: Free Download link: http://accord-framework.net/ 12) Shogun Shogun provides numerous algorithms and data frameworks for machine learning. These machine learning libraries can be used for research and education. Features:

• It has support vector machines that can be used for regression and classification.
• It assists in implementing Hidden Markov models.
• It offers support for various languages including – Python, Scala, Ruby, Java, Octave, R and Lua.

Pros:

• It is easy to use and can process large data-sets which have been used for edge analytics services.
• It offers good customer support, features and functionalities. 

Tool Cost/Plan Details: Free Download link: https://www.shogun-toolbox.org/install 13) Keras.io Keras, written in Python is an API for neural networks that assists in carrying out quick research. Features:

• It can be utilized for easy and fast prototyping.
• It supports convolution networks and a combination of two networks.
• It assists recurrent networks.
• It can be run on the CPU and GPU.

Pros:

• It is extremely user-friendly
• It is both modular and extensible

Cons:

• In order to use Keras, you must need TensorFlow, Theano, or CNTK.

Tool Cost/Plan Details: Free Download link: https://keras.io/ 14) Rapid Miner Rapid Miner is a platform for machine learning, deep learning, text mining, data preparation and predictive analytics. It is mostly used for research, education and application development. Features:

• Through GUI, it helps in structuring and implementing systematic analytical workflows.
• It assists with data preparation. 
• Result visualization. 
• Model approval and optimization.

Pros:

• Extensible through plugins.
• Simple to use.
• Limited programming skills required.

Cons: Rapid Miner is costly. Tool Cost/Plan Details:

• Free plan
• Small: $2500 per year.
• Medium: $5000 per year.
• Large: $10000 per year.

Download link: https://rapidminer.com/get-started/

Best Machine Learning Framework/Technology in 2020

15) Tensorflow Framework TensorFlow is an open-source software library for data-based programming across tasks. This data science software tool is based on computational graphs which is basically a system of codes. Every nod represents a numerical activity that runs some basic or complex function. This framework is one of the best Machine Learning software, as it supports regressions, classifications and neural networks such as complicated tasks and algorithms. 16) FireBase ML Kit Firebase is another prominent machine learning framework that enables highly accurate and pre-trained deep models with minimal code. The framework offers models both on the Google cloud and locally. 17) CAFFE (Convolutional Architecture for Fast Feature Embedding) CAFFE framework provides the quickest solution to applying deep neural networks. It is the best Machine Learning framework known for its pre-trained model-Zoo, which is capable of performing a plethora of tasks including image classification, recommender system and machine vision. 18) Apache Spark Framework A cluster-computing framework, the Apache Spark machine learning is written in different languages like Java, Scala, Python and R. Spark’s Machine Learning library, MLlib has aided Spark’s success. Building MLlib on top of Spark enables it to tackle distinct needs of a single tool, as opposed to many disjointed ones. 19) Scikit-Learn Framework One of the best tools of Python community, Scikit-learn framework can efficiently handle data mining and support numerous practical tasks. It is built on foundations like SciPy, Numpy, and matplotlib. This framework offers supervised, unsupervised learning algorithms and cross-validation. The Scikit is mostly written in Python with some core algorithms in Cython for enhanced performance.

Best Open Source Machine Learning Tools in 2020

20) Uber Ludwig - Open Source Machine Learning Tool for Non-Programmers It is a toolbox built on top of TensorFlow that allows users to train and test deep learning models without having to write any code. It enables users to build complex models that they can tweak before implementing it into code, with minimal input. 21) MLFlow -  Open Source Machine Learning Tool for Model Deployment It can seamlessly work with any machine learning library or algorithm, enabling it to manage the entire lifecycle, including experimentation, reproducibility and deployment of machine learning models. One of the best machine learning tools, MLFlow is currently in alpha and its three components are: projects, tracking and models. 22) Hadoop - Open Source Machine Learning Tool for Big Data Hadoop project is a prominent and relevant tool for working with Big Data. It is a framework that enables distributed processing of large datasets across clusters of computers using simple programming models. It has the capability to scale up from a single server to thousands of machines, each offering local computation and storage. 23) SimpleCV - Open Source Machine Learning Tool for Computer Vision SimpleCV enables access to several high-powered computer vision libraries like OpenCV - making computer vision relatively easy. This can be done without having to learn about bit depths, file formats, color spaces, buffer management, eigenvalues, or matrix versus bitmap storage. 24) Reinforcement Learning - Open Source Tool for Reinforcement Learning RL is a popular phenomenon in Machine Learning and its goal is to train smart agents that can automatically interact with their environment and solve complex tasks. Real-world applications of this technology include robotics and self-driving cars, amongst others.

Best Machine Learning Software Alternatives

25) ServiceNow Platform Its intelligent engine is combined with Machine Learning to create contextual workflows and automate business processes. This helps reduce costs and speed time-to-resolution.  26) Qubole It delivers optimized responses of Big Data Analytics built on Amazon, Microsoft and Google Clouds. 27) Weka Weka contains tools for data pre-processing, classification, regression, clustering, association rules and visualization. It is also be utilized for developing new Machine Learning schemes. 28) IBM Watson Machine Learning It allows you to create, train and deploy machine learning models using your own data; enabling you to grow intelligent business applications.  29) BigML It is easier to set-up and enables users to enjoy the benefits of Programmatic Machine Learning.

Best Machine Learning Software FAQs:

Is Tensorflow framework used for Machine Learning only?

A creation of Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning. It can be used for a wide range of Machine Learning and deep learning applications. It combines machine learning and deep learning (aka neural networking) models/algorithms, making them useful by way of a common metaphor.

How to build a simple stock prediction software using Machine Learning?

Basic machine learning models on stock market data to predict future trends can be built using a single attribute i.e stock price to analyze the trend of stock. Or it can be achieved by simply opting for ARIMA, which is one such statistical model for time-series and atm cash forecasting that stands for Autoregressive Integrated Moving Average. And assumes that time-series information is data points measured at constant time intervals, such as hourly, weekly so and so forth.

Best way to incorporate Machine Learning to your App software?

Data forms the basis and the more data is provided to the algorithm and simpler the model, the better the accuracy of predictions. Hence, it is best to avoid subsampling. The success of the project depends on choosing the most appropriate Machine Learning method and right parameters. Proper data collection and understanding data features also impact learning processes and predictability. Additionally, you need to consider your business model and product capacities. Also keep in mind that algorithms need to be tested, which could increase the time and cost. 

How much does a Machine Learning software engineer make?

Machine learning software engineers are in high demand, which reflects in their salary and benefits packages. It is without a doubt one of the best jobs out there, outpacing many other technology jobs. Average machine learning salary, according to Indeed’s research, is approximately $146,085 (which has increased by 344% since 2015). An entry-level machine learning engineer with 0-4 years of experience would on an average make approximately $97,090. This can go up to $130,000 if there is profit-sharing or bonuses involved.  Mid-level machine learning engineers with 5-9 years of experience command an average salary of $112,095. The number can rise to $160,000 or more, depending on bonuses and profit-sharing. Senior machine learning engineers with over a decade of experience are the industry’s unicorns and command the best remuneration packages in the field. The are likely to make an average salary of $132,500, surpassing $181,000 annually with bonuses and profit-sharing.

Why You Need Machine Learning Software For Every Industry

Machine learning software is used to automate various company processes, across industries to boost productivity, such as enabling customer interactions to be carried out without human input. These machines are algorithms designed to process large amounts of information and make logical decisions. The machines are therefore programmed to learn and complete tasks without requiring any further programming. 1) Transportation Industry We are all aware of how unsmart traffic lights are, but with Machine Learning as a service and Artificial intelligence algorithms they can efficiently predict, monitor and manage the traffic. Best machine learning tools are also being utilized by car manufacturers like Tesla to introduce self-driving cars that can regulate speed, change lanes and park - without human assistance.  2) Healthcare Industry From radiology to diagnostics purposes, intelligent softwares are being employed to not only predict the likelihood of a disease occurring but also to suggest the best possible way to prevent/cure the disease. These are faster in detecting and scanning information, enabling them to produce results much more quickly than humans. 3) Finance Industry Machine Learning applications such as Robo-Advisors are already being used in the finance industry to simplify the investment process and as a cheaper alternative to hiring a human financial advisor. They employ Machine Learning algorithms to automate financial guidance to manage portfolios. Moreover, trading platforms such as High-Frequency Trading (HFT) is being employed by investment banks, pension funds and mutual funds, whereby allowing them to benefit from minute price differences that surface for a fraction of a second.  4) Agriculture Industry We are all aware of approaches that predict crop yields based on historical data and multi-parametric approaches that help optimize productivity; but now algorithms are being utilized to understand the crop quality, identify diseases and detect weeds. Machine Learning is also being utilized to  study soil moisture and temperature of fields to understand the dynamics of ecosystems and obstacles. Machine Learning based apps are successfully enabling farmers make better use of irrigation by providing accurate estimates of evapotranspiration. 5) Education Industry Machine Learning is being used by learning platforms like Udemy, Teachable and WizIQ to provide personalized academic lesson recommendations; similar to how YouTube does it. It is also helping educators become more efficient by automating tasks such as classroom management and scheduling.

Artificial Intelligence (AI) is extensively used in our everyday life. Its introduction has revolutionized the execution of a variety of processes that were known for their complexity. Computer vision is also a sub-branch of Artificial Intelligence. It is not about image processing, as often people misunderstand how does computer vision work? It is far different from image processing. 

Computer vision involves techniques enabling computers to see and interpret visual content for the automation of various processes. Computer vision has revolutionized plenty of industrial processes including manufacturing.

What are Computer Vision Goals and Tasks?

As the use of visual content is increasing day by day. More than half of consumers want to observe more visual content from the brands they like, as conceived by HubSpot. It has become immensely essential to make use of computer vision to handle information from visual content, including images and videos as well.

Simply put, computer vision is employed to mimic natural processes done by a human brain like retrieving information through visual content, handling the data, and interpreting the results for further actions. Though computers are still way behind the capabilities of a human brain in processing visual processing, computer vision is a great step in the right direction.

Computer vision works to perform multiple tasks. These tasks include image classification, semantic segmentation, classification, localization, object detection, object tracking, instance segmentation, action recognition, and image enhancement. These tasks of computer vision work pretty well in combination with each to bring comprehensive results.

How does Computer Vision Method Work in Today`s World?

In this section, we will try to elaborate on how computer vision works in various industries and processes for automation and saving the human effort required for the operation otherwise. Here is a list of some use cases for your reference:

Robotic Manufacturing Processes

As the industry is going through a revolutionary phase while transforming the manufacturing work from manual operations to automated solutions using robotics, computer vision has a lot to do for this cause.    

Robotic machines which are employed to handle the manufacturing process and eliminate the need for human effort are using computer vision method. Robots deployed to work in the manufacturing process need to see what is around them for accurate operations. 

Robotic machines often inspect entire assembly lines to figure out what is coming for them, this helps them in working at the required pace to perform required operations on every item in assembly tolerance timely.

Check out our Robotic process automation services.

Computer Vision for Vehicles

People are turning towards autonomous solutions in almost every task in their everyday life. Similar is the case with vehicles. After some successful results in testing of self-driving vehicles, almost all the vehicle manufacturers around the world have started their campaign to introduce autonomous vehicles with the capability of self-driving. Leaders in this autonomous vehicle campaign are Tesla and Mercedes. 

Tesla is known to be the leader in introducing self-driving vehicles. Here is a link to give more details about self-driving vehicles by Tesla. Mercedes, a renowned vehicle manufacturer, is known for its innovative approach throughout its history, is also introducing self-driving vehicles to have the edge over its competitors, especially BMW. 

In addition to self-driving, vehicles are using computer vision as driver assistance systems. The vehicles can assist in the control of speed, steering, and lane switch with the use of computer vision. There is more to come with the passage of time, but we are sure computer vision is helping a lot in working towards totally autonomous vehicles.

Health and Medicine:

The use of computer vision in the medicine industry is already bringing ideal results. Computer vision is used to pipeline cell segmentation in such a way that it can process an image at an individual pixel level. In addition to processing, computer vision is also capable of interpreting the results and identify the spot where rogue cells are present. Computer vision also helps in coloring multiple elements in an object in different colors for more straightforward interpretation.

Update: We have highlighted some greate uses cases of Predictive analytics in healthcare 

It is immensely useful in identifying and precisely detecting the root cause of a disease like tumors. Using a conventional way of pathology and medical scanning was quite complicated. It was also observed that medical experts agreed on the diagnosis in less than 48% of the medical cases. However, with the help of AI and computer vision, results are highly accurate. Therefore, medical experts don’t need much effort in brainstorming to diagnose the disease. This ultimately helps them in focusing more on the treatment to eliminate the ailment of patients.

Predictive Maintenance:

Predictive maintenance is a crucial part of the manufacturing process. The failure of machinery in the middle of this process could bring a lot of embarrassment and bad reputation to the company. This could be a disaster for any company. Therefore, many companies are turning towards Robotic Process Automation (RPA) for this process, which works hand in hand with the computer vision for accurate working and almost zero chance of failure.   

Machine learning-powered smart devices use computer vision to monitor incoming data from machinery with the help of sensors. This enables smart devices to identify signals and take proactive actions to avoid any manufacturing disaster.

Inspection of Packages:

Computer vision could also be used for inspection of packages to control the quality of the manufacturing process. This is especially a very beneficial tool for pharmaceutical companies to ensure the right number of tablets and capsules in packing. It could also be a great tool for companies manufacturing spare parts and bearings. 

The photos of packages moving through the production line are taken through advanced cameras and sensors. These cameras then deliver these snaps to main control unit which checks the package and counts the tally of items in the package. If an irregularity is identified, the particular package is eliminated from checkout line. This technology could also be used for inspection of packages at airports and ports by the customs department to avoid the delivery of any prohibited item in the country. 

Object Detection and Tracking in Sports:

Machine learning and computer vision have become an essential element in sports. Umpires and referees take the help of these technologies during decision referral systems for object detection and object tracking. 

The technology also helps in getting knowledge about the performances of players and athletes while performing actions. Computer vision also helps in the post-game analysis as well.

3D Computer Vision:

The 3D computer vision could be employed to analyze the performances of athletes and predict the actions of athletes during a game. 3D vision also allows the building of a 3D point cloud, which is a representation of a 2D image in 3D format. Computers can trace the location and shape of the object after building a 3D point cloud. This technology could also help a lot in forensics.

3D vision is also used in retail for monitoring items without any barcodes scanning process by Amazon. It also helps in healthcare. The process of surgery of patients could be observed in real-time through this technology.

Processing of Visuals for Agriculture:

Precision farming is getting popular day by day. Farmers are finding livestock management solution convenient for various agriculture processes. Many farming industries are making use of satellite images for analysis through computer vision to get precise information about conditions of crops and lands. 

AI-led computer vision solutions are also employed in the process of winemaking to ensure the production of finest and disease-free wine and monitor vineyards. Automated drones with high-quality cameras are also deployed to inspect the are to figure out any problem through computer vision.

Computer Vision Powered AR:

Augmented reality has become an essential element in advertising campaigns and industrial procedures. AR made possible through computer vision could help out vehicle manufacturers and various other industries to get a boost in the maintenance and assembly process because of the use of AR.

AR enables industries to make use of the option to implement the application of real-time data integrated with real objects easily. 

How Does Folio3 Computer Vision Solution Help Businesses?

Folio3 is known for its progressive approach and helping businesses from various verticals of the industry to bring automation in their industrial processes. The implementation of computer vision in industrial processes by Folio3 is not different. Here are some use cases made possible by our efficient computer vision implementation team for your reference:

Road Traffic Analysis

We took up a challenge to develop a solution for road traffic analysis by incorporating computer vision in the development to craft an excellent road traffic analysis proprietary product. We developed this product after in-depth research to analyze the road and traffic situation with the help of AI. 

Our solution is capable of distinguishing various types of vehicles and classify them through AI and deep learning. The road analysis system developed by Folio3 makes use of surveillance cameras and specialized software to manage the cameras & visual data through intellectual analysis. It is also capable of interacting with other existing systems already being used for the purpose of traffic management.

Some key functionalities of our traffic analysis and management system are the capability of road supervision by observing the movement of traffic, capable of making fast decisions, and acting in real-time to call relevant authorities like police and ambulance in case of an accident or traffic violation.  

It is also capable of monitoring traffic in real-time that is useful in taking actions proactively to save time in critical situations. It also enables centralized management for traffic control and management operations from the central office. 

Our intelligent solution has proved to be a key element in saving lives and improve the situation of traffic for better and safer roads. We have used technologies like Yolo, SSD, and OpenCV in the development of this efficient solution.

Automated Authentication for Drive-Thrus

Our customer, named ‘Dashcode’, consulted us for the development of an intelligent solution for automation of the drive-thru process. We did substantial research to come up with a smart solution incorporating AI and image analytics to increase workflow efficiency and avoid the time-consuming drive-thru activities. 

The manual process of drive-thru was time-consuming and had many choke-points that were responsible for the slow working of drive-thrus. We applied deep learning technology to change the working method of specific points in the drive-thru process to automate the processes and create a quality enterprise solution to ensure an enhanced drive-thru experience. 

Our state-of-the-art product includes many highlights. Some of the significant features are discussed here. Automated authentication assists in confirming customer identity increase the pace of traffic through the line.  A deep learning method helps the system to accurately identify the customers. Their vehicle’s make and model are also determined to enhance the ease in the identification process.

Our system performs automated transactions through analysis of live visuals. This helps in quick order-taking and avoiding the delay in transmissions to reduce time. Time efficiency is what keeps business running and customers satisfied. It also enables the system to process automated and secure payments in real-time. This, in turn, enables businesses to streamline order accuracy.

The technologies used in our drive-thru automation system includes TensorFlow, scikit-image. AM Turk, and various small tools and libraries. 

Facial Recognition System

Our innovative approach urges us to develop solutions for complex challenges, and that’s why we have developed a highly accurate real-time facial recognition solution. Our system provides real-time results by using HOG (Histogram of Oriented Gradients) and Convolutional Neural Network (CNN). It also makes use of dLib for face recognition and object-oriented detection, our system is capable of accurately showcase results in real-time. The face recognition system offers a feasible option for biometric security. Another advantage of using face recognition is there is no need for making contact with a person who needs to be identified through the biometric process. 

Some highlights of the face recognition system developed by Folio3 are discussed briefly here. Face searching enables the system to perform searches that would help in locating a specific person directly through the entries in the database by matching the specimen’s face.

Data management allows the system to share information with other systems by importing the JPEG format of generic photo data. It can be later used for face searching. Specific faces can be imported in advance to alert relevant authorities when the system observes them through surveillance cameras or any other source of visuals. 

The system would automatically notify users through a pop up if it discovers the face of a specific person. It would also produce warning sounds and flashing the camera on the map. The system is handy in identifying the faces on its own and notify the users for proactive action. The technologies used in the development of this smart solution are CNN, HOG, DLIB, and OpenCV.

How Does Computer Vision Work FAQs

1) What is vision input system?

It incorporates the use of a computer vision system that acts as a sensor and delivers high-value information about what is around.

2) How does object tracking work in computer vision?

Object tracking tends to track objects as they move through a series of video frames. It is a fast-paced system in computer vision.

3) How does computer vision work in the new amazon go store?

Computer vision is used to implement the 3D vision that is used by Amazon technology in retail to monitor items without any need to scan barcodes.

Drawing the Line

AI has brought a revolution in human lifestyle and industrial processes. Computer vision is also a sub-section of AI. Computer vision is proving to be helpful in various industrial and retail processes. It also brings automation to processes that require manual operation otherwise. We hope that after reading this blog, you would get plenty of information about what is computer vision and how does computer vision works? Computer vision is used in many processes, which ultimately bring comfort to our everyday life and our living experience, all thanks to AI!

Machine learning in finance has seen a massive rise in popularity during recent years. In simple words, machine learning in finance is all about importing large amounts of data and learning to perform particular tasks by learning from the data. It is making use of various techniques to manage massive volumes of information in the form of data. It also uses statistical models to get insights and make predictions. Head on to this link to get more info about machine learning solutions.

What is Machine Learning in Finance?

Management of massive data volumes through computer systems is also known as data science. There are many applications of data science in finance, like carrying out credit scores, managing assets, and analyzing risk. Machine learning has been a great fit for the financial industry because of its capability of handling large amounts of data with historical financial records.

Because of this capability, top banks and companies dealing in financial services have deployed AI and machine learning as a service. The reason is, of course, the automation in the various process which were sluggish and prone to error when performed manually. It has also helped finance-related businesses to decrease underlying risk and underwrite loans.

Advantages of Machine Learning Applications in Finance

There are various advantages of machine learning applications in finance. Many businesses and companies are using AI and Machine Learning in full throttle to get the maximum out of their business using these technologies. Some advantages are listed below:

1. Process automation
2. Reduced operational costs
3. Almost zero chance of an error
4. Enhanced Productivity
5. Better user experience using computer vision
6. Improved compliance
7. Reinforced security

In addition to these significant advantages, there are many benefits of implementing machine learning in the finance industry. There is a massive number of machine learning algorithms and tools which are compatible with financial records. Due to the stability of finance companies, there is no restriction of funds to set-up high-quality infrastructure of hardware for enhanced functionality and efficiency. 

As we all know, the finance industry is quantitative in nature. Finance experts have a great focus on maintaining and saving large amounts of historical financial data, which is a great fortune in the case of machine learning. Machine learning technique is deployed through computer systems to learn the process using previous data. 

As there are large amounts of financial data available, this means systems backed with machine learning algorithms would get better with time. In the case of machine learning, the more, the better is a fact. More data means more learning of the system, which would ultimately give advantage to the organizations. It would also help in enhancing many factors and processes in the financial domain.

Many companies have taken note of this development and are working to implement machine learning applications in finance. They are investing massive amounts of money in research and development for machine learning to make it more useable for the finance industry. And for those who are still not interested in it, they would find their businesses in hot waters after a few years.

9 Best Use Cases of Machine Learning and Data Sciences in Finance

To highlight the purpose of using data science in finance, we have selected some use cases to discuss in detail and elaborate on how machine learning is turning out to be a fortune for the finance industry. These use cases are listed below:

Fraud Detection

One of the most significant responsibilities of financial services providers is to decline any fraudulent move against their clients. They have to bear more than 250% of the cost lost due to fraudulent activities against their clients in terms of recovery and relevant charges. To avoid such huge loses, these organizations can use machine learning software for fraud detection.

They won’t win their campaign against financial frauds using outdated and obsolete techniques and approaches. However, it is possible by incorporating machine learning applications in finance domain. They can use sophisticated software solutions backed with machine learning to identify and prevent fraudulent transactions.

These solutions are capable of analyzing massive volumes of data. This analysis enables software systems to recognize patterns and process predictive analysis. Thus, machine learning algorithms used in these solutions can restrict fraudulent transactions with a high accuracy, which won’t be possible by using AI only. 

Many companies are using machine learning to reduce the losses due to financial frauds, while others are working fast to implement it in their systems to take advantage.

Risk Management

Risk management is also an essential responsibility of financial institutions, and service providers are supposed to do risk management. They depend on accurate predictions for the success of their businesses. Therefore, it is absolutely necessary for financial institutions to process current data in order to identify trends and accurately forecast emerging risks.

Conventional software systems used in the financial domain are capable of predicting creditworthiness on the basis of static data imported from loan applications and recent financial reports. However, machine learning technology is far more advanced, with a whole lot of possibilities. Machine learning algorithms can recognize live trends and relevant factors that could influence the ability of the client to make the payment.

Risk management is also connected with the prevention of financial fraud and crisis prediction. Machine learning financial services are capable of addressing these and many other relevant issues in order to manage financial risks. That is why a considerable number of financial institutions were already emphasizing on the implementation of machine learning-enabled solutions in their existing systems.

Investment Predictions

The hedge funds have diverted from old-school prediction methods these days. The use of machine learning in predictions of trends of the fund has seen a huge rise. Hedge fund managers can easily recognize market inclination probably a lot earlier than it was possible with conventional investment analysis models. 

Major financial institutions have taken the potential of machine learning to interfere with the investment banking industry, and therefore, they are working to develop automated investment advisors or Robo-advisors backed with machine learning technology. JP Morgan, Bank of America, and Morgan Stanley have achieved considerable success in this venture. Other companies are also likely to follow the footsteps of leaders. 

Network Security

The security of financial data has been a huge concern for financial institutions. The number of security breaches has also increased considerably in recent years. The task of identifying modern cyber-attacks can’t be restricted using obsolete security software. 

This is a challenging situation and requires advanced counter technology. The security solutions backed with machine learning are amazingly capable of serving the purpose of security of high-value financial data. These solutions have the ability of intelligent pattern analysis in combination with big data operations. 

This gives machine learning security technology an upper hand over conventional security software solutions. This is why a lot of companies are investing in advanced technology machine learning that enables data security solutions to make their valuable data safe from cyber-attacks.  

Loan and Insurance Underwriting

A considerable number of insurance companies are turning their heads towards machine learning to identify risks and set premiums. Machine learning is capable of making predictions on the basis of historical patterns and on-going trends, that is why it is the perfect tool for insurance companies to enhance their revenue and profits.

The banking sector is also getting a huge advantage through the use of machine learning technology. Financial organizations that offer insurance products and loans to their clients are also getting benefits because of machine learning in a similar way. Regardless of the insurance product, whether it is loan protection, health, mortgage, or life insurance. Machine learning is able to cut the chances of underwriting risks.

Algorithmic Trading

Algorithmic trading is supposed to automate the process of trading by performing trading action in accordance with existing criteria defined by the user, which could be a trader or fund manager. In short, algorithmic trade is capable of executing purchase or sale of a stock quantity whenever price-per reaches an ideal or particular value.

With the incorporation of machine learning in algorithmic trading, various new tools are available to make algorithmic trading more than just an automated process. It turns algorithmic trading into intelligent trading. 

The machine learning algorithms are designed in such a way that they are capable of analyzing historical behavior of markets, figure out an ideal market strategy, making trade forecasts, and much more. Even AI is not capable of giving such value without machine learning.

Money-Laundering Prevention

According to recent estimations, it was found that around 2% to 5% of the Global GDP was laundered annually. Banks are not capable of winning the battle against this unethical and immoral activity.

This problem could be addressed with the help of advanced machine learning technology. It has the ability to identify patterns that are closely associated with money-laundering practices. Machine learning applications in finance are proved to be a great help in the detection of money laundering patterns, reducing the number of false positives, and easier compliance with regulatory authorities.

Commerzbank is working on automating 80% of its compliance checklist processes through machine learning till the year 2020. The process will be done through shifting the focus of AI towards money laundering. 

Customer Services

Financial consumers often make complaints against poor customer services.  They want accurate information and fast processing for the solution to their problems regardless of whom they are talking with, whether it is a virtual assistant or a human operator.

AI chatbots are being used for long for customer services, but customers aren’t satisfied. A lot of consumers complain that it doesn’t look like their problems are being understood while talking to chatbots. 

Machine learning brings a whole new era of virtual assistants and cognitive services who are capable of learning instead of following a predefined set of instructions. Chatbots powered with machine learning adapts their serving strategy in accordance with the behavior of individual customers. This ultimately gives a whole new experience of customer services to consumers, which is enhanced and more comfortable.

Check out this free speech to text software

Trade Settlements

The process of payment transactions and purchased security following a stock trade is termed as trade settlement. Electronic transactions are an instant way to complete trade settlements and are being used for long, but the trade isn’t always like it should be. A number of factors could limit the accomplishment of trade.

The use of modern trading platforms and regulatory requirements have considerably reduced the number of trade failures. But while handling high trade volumes, trade failures can still influence the efficiency of the trading system. A challenging task is to resolve failed settlements manually, which takes a considerable amount of time.

However, with the use of machine learning solutions, the cause for failure of a trade could be identified instantly. The machine learning applications in finance are also capable of providing solutions in a matter of seconds. Machine learning technology can even predict the trades which are likely to fail. So, machine learning is a great way to handle failed trades in a fraction of seconds. 

Folio3 Machine Learning Financial Services

Folio3 always do intense research and take up challenges to meet the requirement of clients and cater to the needs of various verticals of the industry. Here are some applications of our machine learning financial services for industry:

ATM Cash Forecasting

A multinational bank that is also regarded as the largest bank in Pakistan on the basis of assets is a client of Folio3. Our valuable client approached us to address their problem regarding ATMs (Automatic Teller Machines). The bank operates over 2000 ATMs across the globe. They wanted to get a solution with the capability to predict the cash-flow management for such a large number of ATMs. 

They asked us to develop a state-of-the-art atm cash forecasting solution for them to address the issue. Our talented developers worked on the requirements and suggestions of our client to come up with a cutting-edge solution. This intelligent solution helped the bank in managing the cash flow for ATMs and raised the profits by 6%.

The highlights of our sophisticated ATM cash flow management solution are the optimization of ATM cash management, which helps the bank to avoid situations like out of cash and overstock. Our solution provides automation, automated analysis of past transactions enables our system to predict the required cash in individual ATMs. It also provides timely reports and notifications to predict the pattern of cash withdrawal.

Our system is capable of forecasting patterns of cash flow and real-time data, which ensures the availability of cash and, ultimately, customer satisfaction. The technology used explicitly in the development of this sophisticated solution is scikit-learn.

Customer Churn Prediction   

A leading tech company in Pakistan is our client. This company deals in high-end services for clients in various countries. They needed a solution capable of predicting and understanding the behaviors of customers. After analyzing the needs and suggestions of our client, we took the responsibility to develop a high-end predictive learning system. The solution was able to recognize the customers who were not willing to pay dues and the ones who won’t renew the subscription of SaaS solutions. In addition to working on user’s data, the system is aimed to work in cooperation with owners and marketing team to understand various underlying factors which could help in designing better campaigns.

Our churn prediction solution helps our client in quantifying the loyalty of their customers while facilitating the reduction of churn with the help of data. The highlights of our solution are customer segmentation through advance data science techniques for dynamic user segments and evolved client base. 

These highlights also include predictive attrition for an enhanced customer retention rate and improved data management activities. The statistical analysis helps our client in recognizing the underlying factors and designing better campaigns. Through these highlights, our sophisticated customer churn prediction solution improves customer retention.

We used technologies like Apache Kafka, Yarn, Spark, and Zeppelin to develop this cutting-edge solution for our client. To get more detail about this solution, head on to the given link

3 Best Machine Learning Applications in Finance

In this section, we will discuss some great machine learning applications in finance, which have made various aspects of the finance domain better. These applications are listed below:

Streamlined Claim Handling Process

There are multiple challenges for the insurance industry. One of the limiting factors in the insurance field of the finance domain is the longer time span taken to process the claims of the insured person or organization. As the insurance company’s personnel gets involved in the claim process to investigate and assess the situation for claim processes.

This sluggish process could be a huge roadblock for an insurance company and the whole industry. To avoid complications, machine learning and artificial intelligence algorithms are deployed to streamline the claim processes in the insurance industry.

Whether it is a simple car accident or destruction of crops due to drought, the aftermath of an insurance claim could take weeks otherwise. However, machine learning algorithms can be employed to examine the situation and make quick rational decisions about the early payouts, which would surely satisfy the consumers. Thus, eventually creating a trusted and strong bond between insurance companies and consumers.

Internal Workflow Automation

Inspecting massive data written in hard form in a short time is almost impossible. Loan and Insurance organizations don’t have much time to engage their employees in extracting data from papers and then transfer it again on papers.

This could be a massive roadblock for financial organizations. However, going through the data of consumers is also necessary to gain substantial information about the consumers for a secure business.

Therefore, machine learning algorithms are deployed to automate the internal workflow of financial organizations and to engage the staff in more productive work. Machine learning algorithms have the capability of analyzing the data written in hard form and converting it in soft form for further use. These algorithms also predict the risk rate while analyzing the data extracted and aid in making quick decisions.

Behavioral Finance

There could be various factors to drive the performance of the stock market. One of these factors is the behavior of investors and how they respond to rumors or news floating in the market. AI machine learning algorithms deployed by various trading agencies import massive data about historical transactions and analyze the current situation while keeping in view the various types of news influencing the market to predict the behavior of traders and investors in the stock market.

There could be news like natural disasters which could impact the market on a larger scale, the machine learning algorithms deployed to analyze behavioral finance predicts the future outcomes and helps trading organizations to act accordingly to get benefit from the market and avoid incurring any loss due to these situations.

In today's IT centred business environment, companies receive almost 95% of their customer data in the form of unstructured text. Sources include emails, surveys, online reviews, social media posts and comments on different forums.

Natural Language Processing (NLP), Cognitive services and AI an increasingly popular topic in business and, at this point, seems all but necessary for successful companies. NLP holds power to automate support, analyse feedback and enhance customer experiences. Although implementing AI technology might sound intimidating, NLP is a relatively pure form of AI to understand and implement and can propel your business significantly. This article will cover some of the common Natural Language Processing examples in the industry today.

Converse Smartly® is an advanced speech recognition application for the web developed by Folio3. It is a strong contender in the use and application of Machine Learning, Artificial Intelligence and NLP. It enables organisations to work smarter, faster and with greater accuracy. The advanced features of the app can analyse speech from dialogue, team meetings, interviews, conferences and more.

Natural Language Processing Definition, and What Is it?

In dictionary terms, Natural Language Processing (NLP) is “the application of computational techniques to the analysis and synthesis of natural language and speech”. What this jargon means is that NLP uses machine learning and artificial intelligence to analyse text using contextual cues. In doing so, the algorithm can identify, differentiate between and hence categorise words and phrases and therefore develop an appropriate response. Some of the most common NLP examples include Spell Check, Autocomplete, Voice-to-Text services as well as the automatic replies system offered by Gmail.

Natural Language Processing Uses in Businesses

Given that communication with the customer is the foundation upon which most companies thrive, communicating effectively and efficiently is critical. Regardless of whether it is a traditional, physical brick-and-mortar setup or an online, digital marketing agency, the company needs to communicate with the customer before, during and after a sale. The use of NLP, in this regard, is focused on automating the tracking, facilitating, and analysis of thousands of daily customer interactions to improve service delivery and customer satisfaction.

Improve user experience

A website integrated with NLP can provide more user-friendly interactions with the customer. Features such as spell check, autocorrect/correct make it easier for users to search through the website, especially if they are unclear of what they want. Most people search using general terms or part-phrases based on what they can remember. Enabling visitor in their search stops them from navigating away from the page in favour of the competition.

Automate support

Providing adequate support can be tedious and labour intensive. To improve communication efficiency, companies often have to either outsource to 3rd-party service providers or use large in-house teams. AI without NLP, cannot cope with the dynamic nature of human interaction on its own. With NLP, live agents become unnecessary as the primary Point of Contact (POC). Chatbots can effectively help users navigate to support articles, order products and services, or even manage their accounts.

Monitor and Analyse Feedback

Feedback comes in from many different channels with the highest volume in social media and then reviews, forms and support pages, among others. NLP can aggregate and help make sense of all the incoming information from feedback, and transform it into actionable insight.

Improve Internal Communication

One of the most monotonous and time-consuming aspects of any internal communication is record keeping. Minutes and transcriptions can take hours, but with NLP, interviews, meetings, seminars, conferences can all be converted to text quickly.

Make Sense of Unstructured data

There are a large number of information sources that form naturally in doing business. These can sometimes overwhelm human resources in converting it to data, analyzing it and then inferring meaning from it. NLP automates the process of coding, sorting and sifting of this text and transforming it to quantitative data which can be used to make insightful decisions.

Folio3's NLP and Cognitive Services

Folio3 is a California based company that offers robust cognitive services through its NLP services and applications built using superior algorithms. The company provides tailored machine learning applications that enable extraction of the best value from your data with easy-to-use solutions geared towards analysing sophisticated text and speech. Their NLP apps can process unstructured data using both linguistic and statistical algorithms.

Natural Language Processing Examples in 2020

Below are some of the common real-world Natural Language Processing Examples. Most of these examples are ways in which NLP is useful is in business situations, but some are about IT companies that offer exceptional NLP services.

1) Search Autocorrect

Making mistakes when typing, AKA' typos' are easy to make and often tricky to spot, especially when in a hurry. If the website visitor is unaware that they are mistyping keywords, and the search engine does not prompt corrections, the search is likely to return null. In which case, the potential customer may very well switch to a competitor. Therefore, companies like HubSpot reduce the chances of this happening by equipping their search engine with an autocorrect feature. The system automatically catches errors and alerts the user much like Google search bars.

2) Search Autocomplete

Autocomplete services in online search help users by suggesting the rest of the keywords after entering a few or a partial word. Historical data for time, location and search history, among other things becoming the basis. Autocomplete features have no become commonplace due to the efforts of Google and other reliable search engines.

Salesforce is an example of a software that offers this autocomplete feature in their search engine. As mentioned earlier, people wanting to know more about salesforce may not remember the exact phrase and only just a part of it.

3) Form Spell Check

Frequent flyers of the internet are well aware of one the purest forms of NLP, spell check. It is a simple, easy-to-use tool for improving the coherence of text and speech. Nobody has the time nor the linguistic know-how to compose a perfect sentence during a conversation between customer and sales agent or help desk. Grammarly provides excellent services in this department, even going as far to suggest better vocabulary and sentence structure depending on your preferences while you browse the web.

4) Smart Search

A smart-search feature offers the same autocomplete services as well as adding relevant synonyms in context to a catalogue to improve search results. Klevu is a company that provides smart search capability powered by NLP coupled with self-learning technology. Best suited for e-commerce portals, Klevu offers relevant search results and personalised search based on historical data on how a customer previously interacted with a product or service.

5) Messenger or chatbots

Many companies today use messenger apps coupled with social media, to deliver connect and interact with customers. Facebook Messenger is one of the more recent platforms used for this purpose. In this case, NLP enables expansion in the use of automatic reply systems so that they not only advertise a product or service but can also fully interact with customers. The more comfortable the service is, the more people are likely to use the app. Uber took advantage of this concept and developed a Facebook Messenger chatbot, thereby creating a new source of revenue for themselves.

6) Machine Translation

Translation of both text and speech is a must in today's global economy. Regardless of the physical location of a company, customers can place orders from anywhere at any time. The trouble lies in the apparent language barrier. When communicating with customers and potential buyers from various countries. Lilt is a translation tool that seeks to make the process easier. It integrates with any third-party platform to make communication across language barriers smoother and cheaper than human translators.

7) Virtual Assistants

Mastercard launched its first chatbot in 2016 which was compatible with Facebook Messenger. Although, compared to Uber’s bot, this bot functions more like a virtual assistant. Having a bank teller in your pocket is the closest you can come to the experience of using the Mastercard bot. The assistant can complete several tasks and offers helpful information such as a dashboard of spending habits and alerts for new benefits and offers available.

8) Knowledge Base Support

An answer bot provides direction within a pre-existing knowledge base. For example, Zendesk offers answer bot software for businesses that uses NLP to answer the questions of potential buyers’. The bot points them in the right direction, i.e. articles that best answer their questions. If the answer bot is unsuccessful in providing support, it will generate a support ticket for the user to get them connected with a live agent.

9) Email filters

Email filters were one of the earliest applications of NLP online. It began with just spam filters based on previous interactions with certain types of emails by the mail clients user base. By uncovering certain words or phrases that signal a spam message the mail client immediately flags the email and moves it to spam. One of the more recent additions to NLP applications in email Gmail's classification system. The system recognises if emails belong in one of three categories (primary, social, or promotions) based on their contents. For all Gmail users, this keeps your inbox to a manageable size with meaningful, relevant emails you wish to review and respond to quickly.

10) Survey Analytics

One of the best ways for NLP to improve insight and company experience is by analysing data for keyword frequency and trends, which tend to indicate overall customer sentiment about a brand. Even though the name, IBM SPSS Text Analytics for Surveys is one of the best software out there for analysing almost any free text, not just surveys. One reviewer tested the system by using his Twitter archive as an input. Although the user interface leaves something to be desired.

11) Social Media Monitoring

Monitoring and evaluation of what customers are saying about a brand on social media can help businesses decide whether to make changes in brand or continue as it is. NLP makes this process automatic, quicker and more accurate. Social media listening tool such as Sprout Social help monitor, evaluate and analyse social media activity concerning a particular brand. The services sports a user-friendly interface does not require a ton of input for it to run.

12) Marketing Strategy

Developing the right content marketing strategies is an excellent way to grow the business. MarketMuse is one such company that produces marketing content strategy tools powered by NLP and AI. Much like Grammarly, the software analyses text as it is written, thereby giving detailed instructions about the direction to ensure that the content of the highest quality. MarketMuse also analyses current affairs and recent news stories, thus providing users to create relevant content quickly.

13) Descriptive Analytics

Reviews increase the confidence in potential buyers for the product or service they wish to procure. Collecting reviews for products and services has many benefits and can be used to activate seller ratings on Google Ads. However, NLP-equipped tools such as Wonderflow’s Wonderboard can bring together customer feedback, analyse it and show the frequency of individual advantages and disadvantage mentions.

14) Natural Language Programming

Programming is a highly technical field which is practically gibberish to the average consumer. NLP can help bridge the gap between the programming language and natural language used by humans. In this way, the end-user can type out the recommended changes, and the computer system can read it, analyse it and make the appropriate changes.

15) Automatic Insights

Automatic insights are the next step in NLP applications. This feature does not merely analyse or identify patterns in a collection of free text but can also deliver insights about a product or service performance that mimics human speech. In other words, let us say someone has a question like "what is the most significant drawback of using freeware?". In this case, the software will deliver an appropriate response based on data about how others have replied to a similar question.

Conclusion

As is evident from the long list of Natural Language Processing examples described above, there is an infinite number of possibilities for NLP application in business. Teaching AI to read, listen and speak as humans will lead to significant efficiency improvements in businesses operations. From sifting through incoming emails to generating automatic insight using computer vision, NLP can change the way people interact with technology altogether.

Effective utilisation of big data and predictive analytics has become critical in today's data-driven world for businesses to achieve their goals. In conjunction with machine learning and artificial intelligence, we have many real-world predictive analytics applications. Governments, research and academic institutions and commercial businesses can all use predictive analytics to make informed and decisions that take into consideration historical data trends. Here in this article, we discuss how predictive analytics works and some of the most common predictive analytics examples that exist today.

What Is Predictive Analytics?

Predictive Analytics is the systematic process of using historical data (last month, last year) to establish connections and analyse possible patterns within the data. These relationships and trends, when used on current or future data (tomorrow, next month, next year), can help predict future outcomes.

The Artificial Intelligence (AI) uses a combination of machine learning applications and statistical techniques to determine the presence and magnitude of changes in one element caused by changes in another within the same data set. After establishing this causality, the AI can apply the same models to new data sets and predict whether there is a change in the second element. For example, you can use historical marketing and sales data to see how significantly specific marketing techniques impact sales.

Why Is Predictive Analytics the Talk of the Town?

In fairness, predictive analytics is nothing new. However, it used to be available only to companies that could afford the resources necessary to carry it out. Only recently has the use of predictive analytics become affordable, easy to use and therefore, popular. It is used increasingly by different organisations in various industries to improve efficiency in everyday operations and achieve that elusive competitive edge.

Send Marketing Material to Customers That Are Most Likely to Make a Purchase

Developing a marketing campaign is both a labour and capital intensive task. Successful marketing needs to be pertinent and engaging at all levels in your target audience. Therefore, you need precisely who is most likely to buy your product and what are the commonalities between such individuals (or groups). For example, let us say that your business has a $5,000 budget for a marketing campaign, and you have 100,000 customers. In this case, you clearly cannot give them all a 10% discount. Business intelligence, coupled with predictive analytics, can help forecast the customers that exhibit the most significant probability of buying your product. You can then focus your discounts/coupon books to only those that generate the most revenue.

Identify Customers that May Abandon a Service or Product

Consider, for example, a Gym that has implemented a predictive analytics model. Keep in mind that fitness centres usually have a high attrition rate with many customers dropping the subscription reasonably quickly. In this case, let us assume that 'Jack' is one such customer that the system identified will not continue the subscription. The basis of judgement is historical data gathered from the Gym as well as the data available about Jack. The next time that Jack comes into the Gym, the staff will be prepared to offer incentives and discuss the continuation of the relationship between the Gym and Jack.

A prominent predictive analysis example comes in the form of Folio3. It is a company that has developed a solution specifically designed for this problem. Their Customer Churn Prediction system offers advances customer segmentation, predictive attrition, statistical analysis and customer retention strategies tailored to the potential churners.

Increase customer service quality through precise planning

Knowing when customers are most likely to arrive and what they will need is the ideal situation. You can use this information to improve service quality and the overall efficiency of your organisation. Through predictive analytics, businesses can forecast demand better by using advanced models and business intelligence. Consider a hotel chain, for example, that wants to know the number of customers that are most likely to visit and stay at specific locations during the Halloween weekend. The hotel can then ensure that they have enough resources and the staff necessary to cater to the flow of customers.

Similarly, a fast-food chain can use predictive analytics to improve drive-thrus and reduce customer wait times. Folio3 successfully developed one such system for its client. Their Automated Authentication fro Drive-Thrus. 'Dashcode’, the company that wanted the automated system wished to increase the workflow efficiency in Drive-Thrus by breaking down each activity and applying deep learning. In doing so, the system would be able to predict what customers are likely to order and recommend it first to save on order time.

Best Predictive Analytics Examples

No sector is precisely the same and therefore is likely to use predictive analytics in different ways. Here are the top seven industry predictive analytics examples.

1) Sports

One of the most recent additions to predictive analytics in sports is the Microsoft Sports Performance Platform. It is a tool that comes up with data-driven decisions for athletes and teams for almost any aspect of the game, including everything from training schemes for each player to the final composition of the team. The algorithms even help prevent injuries and also predict the total recovery time by taking into account factors such as distance sprinted and temperature.

A small Danish football club, FC Midtjylland is an excellent predictive analysis example. It now analyses each player twice a month and tailors individual training plans for every player. Analytical models are used to gain insights during the game to make changes to the game plan in half-time and also to suggest new players.

In tennis, IBM introduced a tool named 'SlamTracker’ which predicts the winner of a match based on a player’s pattern of play, the propensity of forehand use and willingness to volley. Coupled with computer vision and live game footage, this data can then predict the winner.

2) Retail

Retail is probably one of the most significant predictive analytics examples. Retailers are always in a search for ways to increase customer engagement, loyalty and retention. One such example is Amazon’s recommendations system. Whenever you make a purchase, the company offers a list of other similar items to the one you just bought. The AI supplies this list based on historical data of other customers that have been buying the same products.

There are many other benefits of predictive analytics in retail, including sales forecasts, market analysis, segmentation and managing inventory. You can also derive revisions to the business models and the best retail locations using predictive analysis. However, it also acts post-sale, acting to reduce returns, get the customer to come back and extend warranty sales.

3) Weather

Predictive analytics has become almost indispensable to weather forecasting today. Five-day forecast is now just as accurate as one-day forecasts from the 1980s. We can now accurately predict the occurrence of and movement of large weather systems including hurricanes, tornados and flood based on historical data. For example, recently, an extreme polar vortex brought frigid winds down to places like Wisconsin and Minnesota and dropped temperatures to -50 degrees Fahrenheit. The prediction arrived several days in advance, giving people time to prepare. All of this is possible thanks to satellite monitoring of the Earth and complete predictive models that represent how the Earth functions. The movie “Day after Tomorrow” is an excellent example of the use of predictive analytics in assessing the risk of global weather patterns.

4) Health

Predictive analytics in the health care sector is focused primarily on how likely is it that an individual will get better, or sicker. By analysing historical data, hospitals can predict which patients are likely to contract a chronic disease and are susceptible to Central-Line Associated Bloodstream (CLAB) infections. This cognitive services process can also help determine the risk of a patient that does not show up for scheduled appointments. Health Catalyst is one such company operating in Salt Lake City since 2008 that specialises in the focus areas mentioned above.

5) Insurance and Risk Assessment

Insurance firms managed to bring losses within risk tolerances levels despite recent financial disasters thanks to predictive analytics. The system ensures that firms set up competitive prices for underwriting, identify fraudulent claims, analyse and estimate future losses. Insurance firms can also use predictive analysis to develop marketing campaigns and generate better insights into risk selection and assessment.

6) Financial modelling

Predictive analytics for financial services helps fine-tune the overall business strategy, revenue generation strategy and resource optimisation. Automation of analytics in financial services enables firms to process thousands of models simultaneously and deliver quicker results than with traditional modelling based on manual human interpretation of the data. Human error can severely affect the process of analysing trends.

The predictive analysis systems can help financial institutions target specific customer segments based on profitability and risk level. By using historical data, the company can also forecast cash flows and predict demand patterns for specific financial services. Most importantly, the algorithms can alert the firm to customers that deviate from past payment patterns and therefore collect overdue payments much faster.

7) Energy

In the power generation sector, component failures are a real risk and can sometimes cause catastrophic results. The Chernobyl disaster is one such example. By using predictive analytics in power plants, the company can anticipate equipment failures and reduce sudden shutdowns. By predicting when a component will fail, the power plant can use preventive maintenance to address the issue in time and thus reduce maintenance costs and improve the availability of power.

Similarly, utility companies can predict when customers might get a high bill and then alert customers to spikes that occur at certain times of the day. Thereby, the power company can manage the load better and reduce customer complaints.

Predictive Analytics Vs Predictive Modelling

Predictive analytics and modelling serve the same purpose but differ in the method used. Both systems use historical data and statistical techniques to predict the occurrence of an unknown future event. Predictive modelling examples can be found most commonly in astrology and meteorology.

However, predictive modelling will only give you a probability based on a predetermined modelling framework. On the other hand, predictive analytics uses modelling that is driven by data mining and, therefore, yields more intuitive results. Predictive analytics can predict future events and suggest the best course of action to achieve the best results.

Conclusion

Predictive analytics is increasingly becoming a core function of business no matter the size. To succeed and grow the business, companies depend on predictive analytics to forecast cash flows, customer engagement, demand, risk and many more. Predictive analytics and predictive modelling examples are available throughout the business world, improving business insight and therefore increasing competency.

When it comes to innovation, Google seems to always hold the edge and now it is taking on-device machine learning solutions a notch further. Turning speech to text and by leveraging advances in deep learning, Google has brought AI to the device that fits in your pocket. Real-time speech processing and machine learning models with complex algorithms are paving the way for further developments. With the widespread usage of artificial intelligence to perform many tasks, companies are now interested in moving AI tasks from the cloud to the edge.

The AI algorithms rely on huge amounts of data and computer power to perform tasks and therefore traditionally required a connection to the cloud to process voice commands. These, AI models that run Google’s speech recognition algorithms were over 100 gigabytes in size. The company has now been able to shrink it to half a gigabyte, enabling the change. Furthermore, this has eliminated latency and breakdowns, whereby you no longer have to upload image or audio files on a cloud for processing.

Google’s on-device machine learning technology has made the Assistant ten times faster, which can now facilitate users to give commands in real-time. With real-time processing, real-time transcription of videos playing on your phone can also be done. This has created the possibility for new ways to use your phone, for instance, more effective use of robot surgeons to save lives.

The biggest challenge with deep learning applications is the privacy concern, whereby voice, image or text commands are recorded and sent to the cloud to be processed.  Moreover, companies like Google and Amazon need access to data to train their deep learning models. However, with on-device AI, the data stays on your device, such as AI-powered home security stream videos. On-device machine learning is also beneficial for tech companies that offer AI smarts to their users while also staying compliant with privacy rules.

Computing usage and cloud data centers require huge amounts of electricity to run AI algorithms. Plus, the resources that facilitate the running of deep learning models in cloud, also require huge amount of electricity, creating additional costs and carbon footprint. Additionally, transferring data and networking between the device and cloud, requires a lot of energy too. In comparison to that AI tasks at the edge will become more efficient with specialized hardware running deep learning algorithms. For instance, Xnor, a company has already developed a prototype AI accelerator that can run neural networks for several years with just a cell battery and solar power unit.

Losing your connection to the cloud becomes very frustrating; however,  with Google’s on-device technology, the Assistant can navigate apps regardless of the availability of internet. For instance, the AI-powered rescue drones,  these would be able to operate in areas where there is no internet connectivity.

Google’s on-device machine learning capabilities are quite impressive and can really transform existing processes. However, there are some gaps and challenges that need to be addressed before there practicality is determined. We, as a team are already developing models to embrace this technology to further improve Folio3 AI Solutions. Our Artificial Intelligence Solutions are custom designed for our clients, in order from them to derive best value from this technology.

As a team we ensure Folio3 AI Solutions are at par or exceeding industry standards, whereby enabling us to retain our leading position in the market.

To understand MapReduce algorithm, it is vital to understand the challenge it attempts to provide a solution for. With the rise of digital age and the capability of capturing and storing data, there has been an explosion in the amount of data at our disposal. Businesses and corporations were intuitive enough to realize the true potential of this data in terms of gaining insights about customer needs and making predictions to take informed decisions; yet, only within a few years, managing this gigantic amount of data posed a serious challenge for organizations. This is where Big Data comes into picture.

Big data refers to the gigantic volumes of structured and unstructured data and the ways of dealing with it to aid in strategic business planning, reduction in production costs, and smart decision making. However, with Big Data came great challenges of capturing, storing, analyzing and sharing this data with traditional database servers. As a major breakthrough in processing of immense data, Google came up with the MapReduce algorithm inspired by the classic technique: Divide and Conquer.

MapReduce Algorithm

MapReduce, when combined with Hadoop Distributed File System, plays a crucial role in Big Data Analytics. It introduces a way of performing multiple operations on large volumes of data parallely in batch mode using ‘key-value’ pair as the basic unit of data for processing.

MapReduce algorithm involves two major components; Map and Reduce.

The Map component (aka Mapper) is responsible for splitting large data in equal sized chunks of information which are then distributed among a number of nodes (computers) in such a way that the load is balanced and distributed as well as faults and failures are managed by rollbacks.

The Reduce component (aka Reducer) comes into play once the distributed computation is completed and acts as an accumulator to aggregate the results as final output.

Hadoop MapReduce

Hadoop MapReduce is an implementation of MapReduce algorithm by Apache Hadoop project to run applications where data is processed in a parallel way, in batches, across multiple CPU nodes.

The entire process of MapReduce includes four stages.

1. Input Split

In the first phase, the input file is located and transformed for processing by the Mapper.  The file gets split up in fixed-sized chunks on Hadoop Distributed File System. The input file format decides how to split up the data using a function called InputSplit. The intuition behind splitting data is simply that the time taken to process a split is always smaller than the time to process the whole dataset as well as to balance the load eloquently across multiple nodes within the cluster.

2. Mapping

Once all the data has been transformed in an acceptable form, each input split is passed to a distinct instance of mapper to perform computations which result in key-value pairs of the dataset. All the nodes participating in Hadoop cluster perform the same map computations on their respective local datasets simultaneously. Once mapping is completed, each node outputs a list of key-value pairs which are written on the local disk of the respective node rather than HDFS. These outputs are now fed as inputs to the Reducer.

3. Shuffling and Sorting

Before the reducer runs, the intermediate results of mapper are gathered together in a Partitioner to be shuffled and sorted so as to prepare them for optimal processing by the reducer.

4. Reducing

For each output, reduce is called to perform its task. The reduce function is user-defined. Reducer takes as input the intermediate shuffled output and aggregates all these result into the desired result set. The output of reduce stage is also a key-value pair but can be transformed in accordance to application requirements by making use of OutputFormat, a feature provided by Hadoop.

It is clear from the stages’ order that MapReduce is a sequential algorithm. Reducer cannot start its operation unless Mapper has completed its execution. Despite being prone to I/O latency and a sequential algorithm, MapReduce is thought of as the heart of Big Data Analytics owing to its capability of parallelism and fault-tolerance.

After getting familiar with the gist of MapReduce Algorithm, we will now move ahead to translate the Word Count Example as shown in figure in Python code.

MapReduce in Python 

We aim to write a simple MapReduce program for Hadoop in Python that is meant to count words by value in a given input file.

We will make use of Hadoop Streaming API to be able to pass data between different phases of MapReduce through STDIN (Standard Input) and STDOUT (Standard Output).

1. First of all, we need to create an example input file.

Create a text file named ‘dummytext.txt’ and copy the simple text in it:           

            Folio3 introduces ML.

            Folio3 introduces BigData.

            BigData facilitates ML.

2. Now, create “mapper.py” to be executed in the Map phase.

Mapper.py will read data from standard input and will print on standard output a list of tuples for each word occuring in the input file.

            “mapper.py”

import sys

# input comes from STDIN (standard input)
for line in sys.stdin:
    # remove leading and trailing whitespace
    line = line.strip()

    # split the line into words
    words = line.split()
   
    for word in words:
        # write the results to STDOUT (standard output)
        # tab-delimited words
with default count 1
        print '%s\t%s' % (word, 1)

3. Next, create a file named “reducer.py” to be executed in Reduce phase. Reducer.py will take the output of mapper.py as its input and will sum the occurrences of each word to a final count.

"reducer.py"

from operator import itemgetter
import sys

current_word = None
current_count = 0
word = None

# input comes from STDIN
for line in sys.stdin:
    # remove leading and trailing whitespace
    line = line.strip()

    # parse the input we got from mapper.py
    word, count = line.split('\t', 1)

    # convert count (currently a string) to int
    try:
        count = int(count)
    except ValueError:
        # count was not a number, so silently
        # ignore/discard this line
        continue

    # prepare mapper.py output to be sorted by Hadoop
    # by key before it is passed to the reducer
    if current_word == word:
        current_count += count
    else:
        if current_word:
            # write result to STDOUT
            print '%s\t%s' % (current_word, current_count)
        current_count = count
        current_word = word

# to output the last word if needed
if current_word == word:
    print '%s\t%s' % (current_word, current_count)

4. Make sure you make the two programs executable by using the following commands:

          > chmod +x mapper.py

          > chmod +x reducer.py

 You can find the full code at Folio3 AI repository.

Running MapReduce Locally

> cat dummytext.txt | python mapper.py | sort -k1 | python reducer.py

Running MapReduce on Hadoop Cluster 

We assume that the default user created in Hadoop is f3user.

1. Firstly, we will copy local dummy file to Hadoop Distributed file system by running:

> hdfs dfs -put /src/dummytext.txt /user/f3user

2. Finally, we run our MapReduce job on Hadoop cluster by leveraging the support of streaming API to support standard I/O.

> hadoop jar /usr/lib/hadoop-mapreduce/hadoop-streaming.jar \

 -file /src/mapper.py -mapper "python mapper.py"  \

-file /src/reducer.py -reducer "python reducer.py"  \

-input /user/f3user/dummytext.txt -output /user/f3user/wordcount

The job will take input from ‘user/f3user/dummytext.txt’ and write output to ‘user/f3user/wordcount’.

Running this job will produce the output as:

Congratulations, you just completed your first MapReduce application on Hadoop with Python!

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization for your Business Solutions.

We have expertise in Deep learning, Computer Vision, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188

Apache spark is a general-purpose cluster computing engine aimed mainly at distributed data processing. In this tutorial, we will walk you through the step by step process of setting up Apache Spark on Windows. 

Spark supports a number of programming languages including Java, Python, Scala, and R. In this tutorial, we will set up Spark with Python Development Environment by making use of Spark Python API (PySpark) which exposes the Spark programming model to Python.

Required Tools and Technologies:

• - Python Development Environment.
• - Apache Spark.
• - Java Development Kit (Java 8).
• - Hadoop winutils.exe.

Pointers for smooth installation:

- As of writing of this blog, Spark is not compatible with Java version>=9. Please ensure that you install JAVA 8 to avoid encountering installation errors.

- Apache Spark version 2.4.0 has a reported inherent bug that makes Spark incompatible for Windows as it breaks worker.py.

Any other version>2.0 will do fine. 

- Ensure Python 2.7 is not pre-installed independently if you are using a Python 3 Development Environment.

Steps to setup Spark with Python

1. Install Python Development Environment

Enthought canopy is one of the Python Development Environments just like Anaconda. If you are already using one, as long as it is Python 3 or higher development environment, you are covered. ( You can also go by installing Python 3 manually and setting up environment variables for your installation if you do not prefer using a development environment. )

Download your system compatible version 2.1.9 for Windows from Enthought Canopy. 

(If you have pre-installed Python 2.7 version, it may conflict with the new installations by the development environment for python 3).

Follow the installation wizard to complete the installation.

Once done, right click on canopy icon and select Properties. Inside the Compatibility tab, ensure Run as Administrator is checked.

2. Install Java Development Kit

Java 8 is a prerequisite for working with Apache Spark. Spark runs on top of Scala and Scala requires Java Virtual Machine to execute.

Download JDK 8 based on your system requirements and run the installer. Ensure to install Java to a path that doesn’t contains spaces. For the purpose of this blog, we change the default installation location to c:\jdk (Earlier versions of spark cause trouble with spaces in paths of program files). The same applies when the installer proceeds to install JRE. Change the default installation location to c:\jre.

Important Note: If you have a previous installation of Java. Please ensure that you remove it from your system path. Spark won’t work if JAVA exists in some directory path that has a space in its name.

3. Install Apache Spark

Download the pre-built version of Apache Spark 2.3.0. The package downloaded will be packed as tgz file. Please extract the file using any utility such as WinRar. 

Once unpacked, copy all the contents of unpacked folder and paste to a new location: c:\spark.

Now, inside the new directory c:\spark, go to conf directory and rename the log4j.properties.template file to log4j.properties.

It is advised to change log level for log4j from ‘INFO’ to ‘ERROR’ to avoid unnecessary console clutter in spark-shell. To achieve this, open log4j.properties in an editor and replace ‘INFO’ by ‘ERROR’ on line number 19. 

4. Install winutils.exe

Spark uses Hadoop internally for file system access. Even if you are not working with Hadoop (or only using Spark for local development), Windows still needs Hadoop to initialize “Hive” context, otherwise Java will throw java.io.IOException. This can be fixed by adding a dummy Hadoop installation that tricks Windows to believe that Hadoop is actually installed.

Download Hadoop 2.7 winutils.exe. Create a directory winutils with subdirectory bin and copy downloaded winutils.exe into it such that its path becomes: c:\winutils\bin\winutils.exe. 

Spark SQL supports Apache Hive using HiveContext. Apache Hive is a data warehouse software meant for analyzing and querying large datasets, which are principally stored on Hadoop Files using SQL-like queries. HiveContext is a specialized SQLContext to work with Hive in Spark. The next step is to change access permissions to c:\tmp\hive directory using winutils.exe.

- Create tmp directory containing hive subdirectory if it does not already exist as such its path becomes: c:\tmp\hive.

- Run command prompt as administrator.

- Change directory to winutils\bin by executing: cd c\winutils\bin. 

- Change access permissions using winutils.exe: winutils.exe chmod 777 \tmp\hive.

5. Setting up Environment Variables

The final step is to set up some environment variables.

From start menu, go to Control Panel > System > Advanced System Settings and click on Environment variables button from the dialog box.

Under the user variables, add three new variables:

JAVA_HOME: c:\jdk

SPARK_HOME: c:\spark

HADOOP_HOME: c:\winutils

Finally, edit the PATH user variable by adding two more paths in it:

%JAVA_HOME%\bin

%SPARK_HOME%\bin

That’s it. You are all ready to create your own Spark applications with Python on Windows.

Testing your Spark installation 

Before diving into Spark basics, let’s first test if our spark installation is running with Python by writing a simple Spark program to generate squares of a given list of numbers.

- Open Enthought Canopy.

- Go to Tools menu and select Canopy Command Prompt. This will open a command line interface with all the environment variables and permissions set up by Enthought Canopy already to run Python.

- Kick off spark interpreter by command pyspark. At this point, there should be no ERROR messages showing on the console. Now, run the following code: 

> nums = sc.parallelize([2,4,6,8])

> nums.map(lambda x: x*x).collect()      

The first command creates a resilient data set (RDD) by parallelizing a python list given as input argument [2, 4, 6, 8] and store it as ‘nums’. The second command uses the famous map function to transform the ‘nums’ RDD into a new RDD containing the list of squares of numbers. Finally, ‘collect’ action is called on the new RDD to return a classic Python list. By executing the second command, you should see a resulting list of squared numbers as:

[4, 16, 36, 64]

Congratulations! You have successfully set up PySpark on Windows.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions.

We have expertise in Deep learning, Computer Vision, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188

The term "ETA" usually means "Estimated Time of Arrival" but in the technology realm it refers as "Estimated Completion Time" of a computational process in general. In particular, this problem is specific to estimating completion time of a batch of long scripts running parallel to each other.

Problem

A number of campaigns run together in parallel, to process data and prepare some lists. Running time for each campaign varies from 10 minutes to may be 10 hours or more, depending on the data. A batch of campaigns is considered as complete when execution of all campaigns is finished and then resorted to have mutually exclusive data.

What we will do is to provide a solution that can accurately estimate completion time of campaigns based on some past data. 

Data

We have very limited data available; per campaign, from the past executions of these campaigns: 

Feature Engineering / Preprocessing

These are the campaigns for which past data is available. A batch can consist of one or many campaigns from the above list. The feature uses_recommendations resulted during feature engineering. This feature helps machine differentiate between the campaigns which are dependent on an over the network API and the ones which are not, so that machine can keep a variable which caters network lag implicitly. 

Is this a Time Series Forecasting problem?

It could have been, but from the analysis it can be observed that the time of the year doesn’t impact the data that much. So this problem can be tackled as a regression problem instead of time series forecasting problem. 

How it turned into a regression problem?

The absolute difference in seconds between start time and end time of a campaign will be a numeric variable, that can be made the target variable. This is what we are going to estimate using Regression.

Regression

Our input X is the data available, and output y is the time difference between start time and the end time. Now let’s import the dataset and start processing. 

> import pandas as pd

dataset = pd.read_csv('batch_data.csv')

As it can be seen from our data that there are no missing entries as of now, but as this may be an automated process, so we better handle the NA values. The following command will fill the NA values with column mean.

> dataset = dataset.fillna(dataset.mean())

The output y is the difference between start time and end time of the campaign. Let’s set up our output variable.

> start = pd.to_datetime(dataset['start_time'])
> process_end = pd.to_datetime(dataset[‘end_time'])
> y = (process_end - start).dt.seconds

y is taken out from the dataset, and we won’t be needing start_time and end_time columns in the X.

> X = dataset.drop(['start_time', ‘end_time'], 1)

You might have questioned that how would Machine differentiate between the campaign_ids, here in particular, or any such categorical data in general. 

A quick recall if you already know the concept of One-Hot-Encoding. It is a method to create a toggle variable for categorical instances of data. So that, a variable is 1 for the rows where data belongs to that categorical value, and all other variables are 0. 

If you don’t already know the One-Hot-Encoding concept, it is highly recommended to read more about it online and come back to continue. We’ll use OneHotEncoder from sklearn library.

> from sklearn.preprocessing import OneHotEncoder
> onehotencoder = OneHotEncoder(categorical_features = [0], handle_unknown = 'ignore')
> X = onehotencoder.fit_transform(X).toarray()

> start = pd.to_datetime(dataset['start_time'])
> # Avoiding the Dummy Variable Trap is part of one hot encoding
> X = X[:, 1:]

Now that input data is ready, one final thing to do is to separate out some data to later test how good our algorithm is performing. We are separating out 20% data at random.

> # Splitting the dataset into the Training set and Test set
> from sklearn.cross_validation import train_test_split

> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)

After trying Linear regression, SVR (Support Vector Regressor) and XGBoosts and RandomForests on the data it turned out that Linear and SVR models doesn’t fit the data well. And the other finding was that the performance of XGBoost and RandomForest was close to each other for this data. With a slight difference, let’s move forward with RandomForest.

> # Fitting Regression to the dataset
> from sklearn.ensemble import RandomForestRegressor

> regressor = RandomForestRegressor(n_estimators=300, random_state=1, criterion='mae')
> regressor.fit(X_train, y_train)

The regression has been performed and a regressor has been fitted on our data. What follows next is checking how good is the fit.

Performance Measure

We’ll use Root Mean Square Error (RMSE) as our unit of goodness. The lesser the RMSE, the better the regression.

Let’s ask our regressor to make predictions on our Training data, that is 80% of the total data we had. This will give a glimpse of training accuracy. Later we’ll make predictions on the Test data, the remaining 20%, which will tell us about the performance of this regressor on Unseen data.

If the performance on training data is very good, and the performance on unseen data is poor, then our model is Overfitting. So, ideally the performance on unseen data should be close to that on the training data.

> from sklearn.metrics import mean_squared_error
> from math import sqrt
> training_predictions = regressor.predict(X_train)

> training_mse = mean_squared_error(y_train, training_predictions)
> training_rmse = sqrt(training_mse) / 60 # Divide by 60 to turn it into minutes

We got the training RMSE, you should print it and see how many minutes does it deviate on average from the actual.

Now, let’s get the test RMSE.

> test_predictions = regressor.predict(X_test)
> test_mse = mean_squared_error(y_test_pred, test_predictions)

> test_rmse = sqrt(test_mse) / 60

Compare the test_rmse with training_rmse to see how good is the regression performing on seen and unseen data.

What’s next for you is to now try fitting XGBoost, SVR and any other Regression models that you think should fit well on this data and see how different is the performance of different models.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions.

We have expertise in Deep learning, Computer Vision, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188