Selecting the correct machine learning algorithm can be difficult, but doing so is critical in order to answer a given question with high speed and accuracy. In this blog, we will introduce three types of machine learning algorithms and explain how to select the right one when tackling business problems. Then, we share a few real-world use cases for the different algorithm types.\nBefore understanding how to choose the best machine learning algorithms let us briefly look into the types of machine learning algorithms. The machine learning algorithm can be broadly classified into three groups.\nSupervised Learning Algorithm\nUnsupervised Learning Algorithm\nReinforcement Learning Algorithm\nRegression\nK-Mean Clustering\nMonte Carlo\nRandom Forest\nKNN\nMarkov Decision Process\nDecision Tree\nSVD\nQ Learning\nSupervised learning model: Trained with the dataset which consists of labels for both Input and output.\nUnsupervised learning model: Trained with the dataset that has input features but does not have labels for the output.\nReinforcement learning model: Trains itself based on a set of actions and makes the decision.\nNow, let’s learn when to use these algorithms rather than how to use these algorithms. Choosing the correct algorithm is dependent on multiple variables. A good understanding of data science plays a very significant role in categorizing the problem statement.\nThe factors we need to consider while categorizing and solving the problem are:\nKnowledge of Data\n: The data’s structure and complexity help dictate the right algorithm\nAccuracy Requirements\n: Different questions demand different degrees of accuracy, which influences algorithm selection\nProcessing Speed\n: Algorithm choice may depend on the time constraints in place for a given analysis\nVariables\n: The unique features considered while training the model for the optimal result and accuracy help determine the right algorithm.\nParameters\n: Factors such as the number of iterations directly relate to the training time needed when generating output.\nOur first step is to analyze the data and observe the patterns and any hidden insights by visualizing the data. The insights from data visualization will help in making an initial decision on which algorithm to choose for solving the given problem.\nAs an example, if the dataset has input and output labels, a supervised learning algorithm will be best for the problem. Supervised learning can be segregated further based on the type of output. For any numeric output, one should choose a regression algorithm to train the model, but for any class output, one should target a classification algorithm to train the model.\nIf the dataset has no output labels, choosing an unsupervised learning algorithm will be a wise decision, for example, the K-Mean clustering algorithm comes in handy in dividing the dataset into a smaller cluster or set based on similar attributes.\nThe characteristics and behavior of the dataset also play an important role in making the ideal algorithm selection. For example, if the data is sequential, like stock price or heart rate, then the algorithm that fits best is the Markov model and decision tree.\nAnother important factor in deciding the algorithm is speed and accuracy. There is a tradeoff between speed and accuracy. If accuracy is not vital, perhaps when estimating a value, we can reduce the processing time, thus increasing the execution speed. Conversely, we can achieve better accuracy by slowing the execution speed.\nAnother factor that plays an important role in choosing the correct algorithm is the number and types of parameters we pass while training the model. These parameters may include iteration cycle, splitting train and test datasets, error tolerance, and more. The training time for a given model is directly proportional to the number of parameters included. When multiple parameters are required to train the model, SVM (Support Vector Machine) algorithms work well.\nReal-Life Use Cases\nLinear Regression\nThis can be used to predict housing prices based on multiple variables including square footage, the presence of a balcony, the number of bathrooms, total rooms, kitchen area, etc.\nEach of these variables plays an important role in predicting the accurate housing price. A co-relation of 1 indicates a perfect relationship and accurate housing price, anything less than 0.9 can safely be assumed as unreliable prediction.\nLogistic Regression\nCurrently, bank customers face the constant threat of credit card and identity fraud. As a result, the financial sector is working to devise new techniques to avoid these problems. One of the steps banks have taken to reduce fraud is by leveraging logistic regression to build a classifier, known as the LogR classifier, which adapts to the user’s behavior based on his historic data. As a result, financial institutions can more accurately distinguish their true customers from any impersonator.\nSupport Vector Machine (SVM)\nThis helps in handwriting detection using a support vector machine classifier. It has achieved accuracy of up to 96% when \nconverting handwritten notes\n into text-based data.\nRandom Forest\nRandom Forest algorithms can be used to predict equipment failure and schedule just-in-time preventive maintenance. The failure of the equipment, such as utility poles or transformers, can be caused by cracks, corrosion pitting, spalls on rolling surfaces, and more. These failures can be catastrophic and financially devastate a given organization.\nTaking advance of Random Forest machine learning algorithms and AI to monitor these assets may help in \ndetecting failures at their earliest stages\n, reducing or preventing damage to the asset and the organization’s bottom line altogether.\nK-Mean\nUber, Lyft and other rideshare apps utilize K-Mean machine learning algorithms for optimal placing and clustering of their vehicles based on the peak demand. As a result, customers enjoy reduced wait times and helps the rideshare companies dictate surge pricing when supply is low and demand is high.\nIn Conclusion\nAs we have discussed, machine learning algorithms generally fall into one of three categories – supervised learning, unsupervised learning, and reinforcement learning. When addressing different types of business challenges, analysts should carefully consider the characteristics of the data, speed and accuracy requirements, variables, and parameters in order to yield the desired insights.\nBy using the proper algorithms, organizations can expect to benefit from stronger data insights that reflect the unique conditions that guide each business process. As their algorithms continually learn and improve, so will the data-driven decisions that result.

Selecting the correct machine learning algorithm can be difficult, but doing so is critical in order to answer a given question with high speed and accuracy. In this blog, we will introduce three types of machine learning algorithms and explain how to select the right one when tackling business problems. Then, we share a few real-world use cases for the different algorithm types.

How to Choose the Right Machine Learning Algorithm

With the size and scope of collected data expanding with each passing day, it is very important to continually analyze available data to drive better-informed business and policy decisions. To a large extent, handwritten data remains unexplored and unanalyzed. If we can analyze handwritten text data, we can minimize the hurdles and save the manpower involved in digitizing handwritten data.\nIn this blog, we cover how handwritten text data can be processed using Python and Google Cloud Vision. Cloud vision offers pre-trained ML models which are very powerful, and we do not need to do any pre-training.\nBelow is an example of converting simple handwritten text into digital words, which can be easily ingested into a CSV file or database.\nUsing the applications listed in the instructions below, we are able to convert the scanned images or PDF files into digital text. Our approach for this solution requires us to first convert PDF documents to an image format. As we are processing images, it is better to convert all the images to the same size. We then need to define the source from which we are extracting the data from the image. This requires knowing the coordinates of the data to be extracted.\nSteps to Convert Handwritten Text into Digital Data\nThe prerequisites for this exercise are to install Google Cloud Vision, Python 3, Handprint, Keras, NumPy, pandas, pdf2image and cv2.\nConvert the PDF document to an image using the following python command.
\nimages = convert_from_path(file)\nSave the images to a directory so that this will be used for data extraction.
\nfor img in images:
   img.save(path + "\\" + fileName + '.jpg', 'JPEG')\nRead the image using following command.
\nimage = cv2.imread(filename)\n
\nPass the image to the pytesseract to convert the image to data item.
\nTo view the keys, run the below command.\nCheck the extracted text data.
\nExtract the coordinates for the required text using the following command.\nRepeat the same process for all the required text fields.\nUse the extracted coordinates to crop the image for the required field and save the cropped image to a specific location.
\nDownload the credentials file after setting up the Cloud Vision account on Google.
\nProcess these cropped images using Google’s Cloud Vision to extract handwritten values.
\nWe then extract the required data from the Json files.
\nWe have created a Python dictionary to store the extracted information. Convert the dictionary to dataframe for easy processing.
\nAs this example deals with only two images, the instructions did not include many validations. For a real-world application, analysts should validate the extracted data based on confidence levels and filter out the accurate data.\nIn Conclusion\nHandwritten image and text processing is very useful across many industries, including healthcare, insurance, and utilities. Various use cases include invoice processing, processing of inspection forms, employee onboarding, analyzing reviews and survey data and so on. One prescient application for utilities and other organizations relying on distributed field services is the digitization of handwritten notes taken by field technicians when completing repairs or regularly scheduled maintenance. By digitizing these notes, \nand even incorporating process automation to do so\n, utilities are better situated to retain critical handwritten information regarding asset health, maintenance history, and more.

In this blog, we cover how handwritten text data can be processed using Python and Google Cloud Vision. Cloud vision offers pre-trained ML models which are very powerful, and we do not need to do any pre-training.

How to Process Handwritten Text Using Python and Cloud Vision

By Gokuleshwar Raju Nannapuraju, HEXstream integrations and analytics consultant\nIn this blog post, we navigate through the revolutionary impact of Oracle Cloud Infrastructure–Data Integration (OCI DI), offering a non-technical overview that illuminates its benefits and underscores why it stands as a true game-changer in reshaping the integration landscape for a seamlessly connected future.\nWhat Is OCI DI? \nOCI DI, an abbreviation for Oracle Cloud Infrastructure–Data Integration, serves as a cloud-based ETL (Extract, Transform, Load) tool designed to foster seamless connections between source and target databases. This powerful tool empowers users to efficiently cleanse, transform, and reshape data, streamlining the integration process for enhanced efficiency and reliability.\nUnderstanding Data Integration\nData integration involves the transformation of data from various sources, enabling seamless connectivity between different databases. This process includes connecting to source and target databases, cleansing and transforming the data through mapping and logic definition. Mapping from source to target databases enables data transformation either through manual execution or scheduled jobs based on desired frequencies.\nAdvantages of OCI DI\n1. Zero maintenance: Unlike traditional data-integration tools that require regular patching and service monitoring, OCI DI requires zero maintenance. Users can rest easy, knowing that they are free from any maintenance activities or concerns regarding application versions.\n2. No installation required: OCI DI eliminates the need for time-consuming installations. With a few simple steps, users can create a workspace and promptly start utilizing OCI DI.\n3. Basic configuration: Once the workspace is established, users can effortlessly connect to the source and target databases. OCI DI eliminates the need for complex configurations, ensuring a streamlined integration experience.\n4. Cost-effectiveness: OCI DI is an affordable option for businesses, and its unique pricing model charges users based on data usage and integration needs. There is no need to constantly start or stop services, as costs are calculated according to the actual usage.\n5. Enhanced speed: Regardless of the data volume being integrated, OCI DI utilizes powerful Spark engines in the background, ensuring high-speed execution once the job is initiated. This enables efficient data-integration processes for improved productivity.\nThe Significance Of Zero Maintenance\nLet’s circle back to that zero-maintenance benefit of OCI DI, which alleviates the burdens associated with managing services and ensuring timely startups. Users can enjoy uninterrupted integration capabilities without the need for downtime or patching activities. By eliminating these concerns, OCI DI optimizes business operations and maximizes efficiency.\nIn summary, OCI DI is transforming the integration landscape with its user-friendly features, cost-effectiveness, and maintenance-free experience. By leveraging the power of Oracle Cloud Infrastructure, businesses can enhance their data-integration capabilities and achieve seamless connectivity across various databases. Embrace OCI DI and unlock a new level of integration efficiency for your business.


Transforming Integration: The Evolving Benefits Of Oracle Cloud Infrastructure Data Integration

By Arun Kota, HEXstream senior director analytics\nAn intricately crafted disaster-recovery system stands as a vital pillar for utility organizations, ensuring swift and efficient recovery in the aftermath of catastrophes. In the dynamic landscape of IT, disasters take various forms, ranging from network outages and equipment failures to application glitches and natural calamities, which all pose imminent risks to the integrity of your applications. \nAs we can’t predict when a disaster will strike, the best option is to have a plan on how to quickly recover. \nAn important step in disaster-recovery planning is determining the Recovery Time Objective (RTO) and Recovery Point Objective (RPO) for an application. \nRecovery Time Objective (RTO) serves as a crucial metric, reflecting the maximum allowable downtime for a specific application following a disaster, with a general rule that emphasizes shorter RTOs for applications of higher criticality.\nRecovery Point Objective (RPO) signifies the timeframe, post-disaster, during which an application can endure data loss without negatively affecting the enterprise, underscoring the critical need for tailored recovery strategies based on data sensitivity and business-continuity priorities.\nIn crafting a robust disaster-recovery plan, it is imperative to factor in both RTO and RPO, built upon a strategic alignment that harmonizes the goal of timely application recovery with a tolerance for data loss. This dual consideration facilitates the development of a comprehensive, cost-effective, and resilient framework for disaster preparedness.\nMultiple Approaches for Disaster Recovery in OCI\nOracle Cloud Infrastructure (OCI) provides four different approaches in disaster recovery.
Selecting the appropriate disaster-recovery solution for an OCI application hinges on various factors such as availability needs, data durability, as well as the specific requirements for both RTO and RPO, necessitating a thoughtful evaluation to align the chosen solution with the organization’s unique considerations.\n1.    Backup and Restore–This approach backs up critical data and systems into offsite storage on regular intervals. In the event of a disaster, data can be restored from these backups to bring the systems back to their previous states. This is a relatively cheaper approach, but it usually takes more time. The backup-and-restore approach is usually used for noncritical applications.\n2.    Pilot Light–In this approach, a minimal version of the environment is always running in the cloud. In the event of a disaster, the infrastructure can quickly scale up to full capacity. The
RPO and RTO are shorter, but a little costly  compared to the backup-and-restore tactic. The
Pilot Light approach is usually used for critical applications.\n3.    Active/Passive or Warm Standby–In this approach, a fully installed and configured application is on standby and ready to run when required. This setup reduces the time required to switch to a fully operational state in the event of a disaster. The RPO is in seconds and RTO is in minutes; the cost is significantly more expensive than Pilot Light. The
Active/Passive or Warm Standby approach is usually used for critical applications.\n4.    Active/Active–In this approach, the applications are deployed in Active/Active mode and are actively serving. Every transaction is registered  in both applications. Both RPO and RTO are almost zero. This approach has the highest cost, as the applications are running in parallel. The Active/Active approach is used for extremely critical applications.\nOCI disaster recovery can be created using multiple regions. Choosing the right DR approach for an application is very important. The criticality of the applications, data consistency, cost and complexity need to be factored in when deciding the DR approach for an application.\n\nLEARN HOW HEXSTREAM CAN BOOST YOUR EFFICIENCY WITH ORACLE CLOUD INFRASTRUCTURE. CLICK HERE TO CONTACT US. \n\n


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