Doctors typically write in incomprehensible handwriting, making it difficult for both the general public and some pharmacists to understand the medications they have prescribed. It is not ideal for them to write the prescription quietly and methodically because they will be dealing with dozens of patients every day and will be swamped with work.As a result, their handwriting is illegible. This may result in reports or prescriptions consisting of short forms and cursive writing that a typical person or pharmacist won't be able to read properly, which will cause prescribed medications to be misspelled. However, some individuals are accustomed to writing prescriptions in regional languages because we all live in an area with a diversity of regional languages. It makes analyzing the content much more challenging. So, in this project, we'll use a recognition system to build a tool that can translate the handwriting of physicians in any language. This system will be made into an application which is fully autonomous in functioning. As the user uploads the prescription image the program will pre-process the image by performing image pre-processing, and word segmentations initially before processing the image for training. And it will be done for every language we require the model to detect. And as of the deduction model will be made using deep learning techniques including CNN, RNN, and LSTM, which are utilized to train the model. To match words from various languages that will be written in the system, Unicode will be used. Furthermore, fuzzy search and market basket analysis are employed to offer an end result that will be optimized from the pharmaceutical database and displayed to the user as a structured output.

This article was published as a part of the Data Science Blogathon. A supermarket store named Big Mart opened a coffee shop inside the premises, and after the launch, it started seeing great transactions, and it was decided to have similar coffee shops at all the stores across the region for Big Mart. Big Mart has been using association rules for its main retail stores, and under the marketing plan for these coffee shops, they want to create similar association rules and do combo offers for these shops. Transaction data for the coffee shop relating to 9000 purchases were collected. The task is to find out the top association rules for the product team to create combo offers and use the insights to make the coffee shop even more profitable at all these stores.

Some of us go to the grocery with a standard list; while some of us have a hard time sticking to our grocery shopping list, no matter how determined we are. No matter which type of person you are, retailers will always be experts at making various temptations to inflate your budget. Remember the time when you had the "Ohh, I might need this as well." Retailers boost their sales by relying on this one simple intuition. People that buy this will most likely want to buy that as well. People who buy bread will have a higher chance of buying butter together, therefore an experienced assortment manager will definitely know that having a discount on bread pushes the sales on butter as well.

The AI and machine learning industries are booming like never before. As of 2021, the increase in AI usage across businesses will create US$2.9 trillion in business value. AI has automated many industries across the globe and changed the way they operate. Most large companies incorporate AI to maximize productivity in their workflow, and industries like marketing and healthcare have undergone a paradigm shift due to the consolidation of AI. Time-series forecasting is a machine learning technique used very often in the industry.

The AI and Machine Learning industry is booming like never before. As of 2021, the increase in AI usage across businesses will create $2.9 trillion of business value. AI has automated many industries across the globe and changed the way they operate. Most large companies incorporate AI to maximize productivity in their workflow, and industries like marketing and healthcare have undergone a paradigm shift due to the consolidation of AI. Due to this, there has been an increasing demand in the past few years for AI professionals. There has almost been a 100% increase in AI and machine learning-related job postings from 2015 to 2018. This number has grown since and is projected to rise in 2021. If you are looking to break into the machine learning industry, the good news is that there is no shortage of jobs available. Companies need a talented workforce that is capable of pioneering the shift to machine learning.

Businesses generally run campaigns of 8-10 weeks duration with weekly e-mails sent to the reachable customer base. Since the customer's purchase pattern depends on the nature of products in the product catalog, the time to the next purchase is usually a month or more, depending on the category. As a result, for most of the customers, the content being sent across the weekly campaigns is usually the same because the model recommendations do not change weekly based on the historical data. Therefore, stagnant recommendations over a period of 3 to 4 weeks may lead to a bad customer experience. On the flip side, based on the frequency of purchase, sending e-mails with similar content may also serve as a reminder in case the customer misses any of the previous e-mails.

Market Basket Analysis (MBA) is a popular technique to identify associations between products, which is crucial for business decision making. Previous studies typically adopt conventional frequent itemset mining algorithms to perform MBA. However, they generally fail to uncover rarely occurring associations among the products at their most granular level. Also, they have limited ability to capture temporal dynamics in associations between products. Hence, we propose OMBA, a novel representation learning technique for Online Market Basket Analysis. OMBA jointly learns representations for products and users such that they preserve the temporal dynamics of product-to-product and user-to-product associations. Subsequently, OMBA proposes a scalable yet effective online method to generate products' associations using their representations. Our extensive experiments on three real-world datasets show that OMBA outperforms state-of-the-art methods by as much as 21%, while emphasizing rarely occurring strong associations and effectively capturing temporal changes in associations.

We provide a methodology for learning sparse statistical models that use as features all possible multiplicative interactions among an underlying atomic set of features. While the resulting optimization problems are exponentially sized, our methodology leads to algorithms that can often solve these problems exactly or provide approximate solutions based on combining highly correlated features. We also introduce an algorithmic paradigm, the Fine Control Kernel framework, so named because it is based on Fenchel Duality and is reminiscent of kernel methods. Its theory is tailored to large sparse learning problems, and it leads to efficient feature screening rules for interactions. These rules are inspired by the Apriori algorithm for market basket analysis -- which also falls under the purview of Fine Control Kernels, and can be applied to a plurality of learning problems including the Lasso and sparse matrix estimation. Experiments on biomedical datasets demonstrate the efficacy of our methodology in deriving algorithms that efficiently produce interactions models which achieve state-of-the-art accuracy and are interpretable.

You may be dazzled, spooked or annoyed by the ability of online retailers to predict which products you're interested in purchasing--but what if you could play the same game? Whether it's Amazon recommending soap or Netflix suggesting a movie, more companies are making personalized predictions using variations on a machine learning tactic called market basket analysis. This technique uses an algorithm that sorts through behavioral data to determine how frequently certain actions (purchases, views, etc.) are associated with other actions. The algorithm provides the statistical likelihood that if one action takes place, another desired action is likely to follow. Sophisticated algorithms aren't limited to shopping carts or movie recommendations, however.

The fields of machine learning and deep learning are on the brink of unprecedented breakthroughs across a variety of verticals. And according to a new report from Databricks, "data is the new fuel," for these market advancements. AI and deep learning are set to disrupt and change industries across the board, and potential for innovation is certainly great. That said, the question for many enterprises becomes how to take advantage of the myriad of data ML tools now available. The new report explores the Databricks Unified Analytics Platform and provides four real-life machine learning use cases, including code samples and notebooks.