THE SMARTEST INSURANCE ANALYTICS

Data cleaning has been a long-unsolved challenge that has plagued the data science and analytics industry. Data scientists spend numerous hours preparing their data for modeling.

Data cleaning is the step to be consistent with the customer database by identifying and removing incorrect data. The most important purpose of Data Cleaning is to recognize and receive mistakes and dual data, unstructured data, planning analysis, your brand’s goals and decisions in the texts, forcing you to export, store and organize typical menus that you cannot collect. Examine unstructured data analysis and tools. This makes better the quality of the exercise data for analytics and It makes the right decision. Most of the time, you should keep unstructured data in Word document databases and manually analyze the analysis tools in databases to prove this data.

B2Metric AI brings data preparation feature automatically for data scientists. Together with this developing technology, it has taken the new developments and technology data gathering & cleaning process to an advanced and higher-level providing users a new perspective and high level of experience.

Different types of data will require different types of cleaning. However, the systematic approach laid out in this lesson can always serve as a good starting point with using B2Metric AI. Data scientists and business analysts achieve this with a click of a button.

Furthermore, data cleaning is one of those things that everyone does but no one really talks about it. Surely, it’s not the best part of machine learning. And no, there aren’t hidden tricks and secrets to uncover. 

Data gathering is one of the most important processes in solving any examine ML problems. To establish a successful machine learning model, an organization must have the ability to train, test, and verify them before starting production. Data preparation technology is used to create a clean and explanatory basis for today’s modern machine learning, but good DP historically takes more time than other parts of the machine learning process.

Most machine-learning algorithms require that data be formatted in a very particular way, so data sets often require some preparation before providing useful information. Some data sets contain values that are missing, invalid, or otherwise difficult for an algorithm to handle. If the data is missing, the algorithm cannot use it. If the data is void, it causes the algorithm to procreate less accurate and even elusive results. Good data arrangement produces cleaner and better-curated data leading to more practical, accurate model results.

Data collection allows you to keep a record of past events so we can use data analysis to find duplicate patterns. From these patterns, you create prescience models using machine learning algorithms.

A characteristic or a set of characteristics can be considered as a feature in the act of machine learning. When these characteristics are transformed into some measurable form, they are labeled as features. Feature engineering is creating new input features from your existing ones. In general, the data cleaning process can be assumed as a subtraction process and the feature engineering as a process of addition. Feature engineering can directly be defined as the process of creating new features from the existing features in a dataset. 

Building machine learning models can often be a complex and boring process and involves many steps. So, B2Metric ML Studio is able to automate a certain percentage of feature engineering tasks, then the data scientists or the domain experts can focus on other aspects of the model. 

Automated feature engineering helps on time saving, building better predictive models, creating meaningful features and preventing leakage of datas. At the same time, the datas are prepared by B2Metric AI to automatically modeling, to execute operations like one-hot encoding, missing data imputation, text mining, standardization and data partitioning.

If this has to be done by hand this would have taken several days, yet with automated machine learning, this only took hardly any hours.The key strength of the automated feature engineering is when it’s applied to regrouping or reshaping data. This is why it is recommended that engaging the creativity and experience of business domain experts for domain-specific feature knowledge, such as how to correctly interpret the data.

The process of automating the wasteful time, iterative jobs of machine learning model development is called automated machine learning, also referred to as Auto-ML. It permits analysts, data scientists, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. B2Metric AI main technology got the basis of Auto-ML. Automated machine learning is the works of automating the end-to-end process of applying machine learning to complex real-world problems. B2Metric AutoML makes machine learning available in the right sense, even to people with no major expertise in this field.

Auto-ML takes advantage of the strengths of both humans and computers. Humans are proficient at communication, engagement, context and knowledge, as well as creativity and insight. Software systems and computers are excellent for repeated tasks, mathematics, data manipulation and parallel processing. Also, they provide humans to achieve master complex solutions.

The traditional way of ML model development process is a resource and labor-intensive, requiring critical domain expertise and time to produce and compare dozens of models. Apply automated ML when you want B2Metric Machine Learning to train and tune a model for you using the target metric you specify. 

Manually finding the right algorithm and tuning it to fit your dataset, well is a challenging task. B2Metric AI technology automates the algorithm selection and hyperparameter optimization on algorithms ranging from classical scikit-learn algorithms to complex time series algorithms. Every model built-in B2Metric AI can be put into production straight away. You can upload data to be scored in bundles. Monitor the performance of all deployed models from a central portal, and easily refresh and replace models if data and accuracy changes over time.

Automated ML replaces much of the work that is done by hand required by a more traditional data science process. But if it is wanted to be considered as a fully automated machine learning solution, a platform must meet these key points: Preparing Data, Feature Engineering, Diverse Algorithms, Algorithm Selection, Training and Tuning, Ensembling, Head-to-Head Model Competitions, Human-Friendly Insights, Easy Deployment, Model Monitoring and Management.

The success of machine learning in various applications has led to an ever-increasing demand for ML systems that can be used off the shelf by non-specialists. It leans to automate the maximum number of steps in an ML pipeline with a minimum size of human effort and without compromising the model’s performance.

Model deployments on the data stream and the predictions are live and on the spot. You can deploy on a dynamic B2Metric AI enables you to watch real-time updates from dashboards. If no ready-made AI service are available off-the-rack, that does not mean you have to build everything from the ground up with libraries. There is a middle ground: customizable AI and ML models that you can train with your own data. In this way, you can save more time and money. Thus, you can make and expect a faster and as well healthier decision than the feedback you need. As a result, you can integrate it into your own product using APIs.