Tag: Data mining

analytics solutions

How Can Analytics Solutions Empower Businesses in 2019?

With the changing business environment, data analytics solutions have become the key to outshine all competition. Leveraging analytics solutions effectively is the major difference between companies that fail and succeed in the competitive business landscape of today. Companies that are highly successful in the B2B marketplace are using analytics solutions to devise effective and highly specific campaigns to reach out to targeted prospects with a curated message. Data analytics solutions help business organizations to make data-driven decisions and pave the way for businesses to identify inefficient processes, spot new opportunities, and cut costs significantly. With the help of algorithms, data analytics solutions assist companies to extract actionable insights from the data and make data visualization simpler. There are various analytics solutions such as web crawling, text mining, predictive analytics, and market mix modeling that act as a silver bullet for companies operating in various sectors.

Here is a brief summary of common business challenges that can be mitigated with the aid of analytics solutions:

Challenges e-commerce businesses face and how web crawling and text mining solutions can help?

Competitor research, dynamic pricing, new product research, assessing the required data, and managing proxies to reliably scrape the web without disruption are some of the major challenges that e-commerce businesses face. Therefore, it becomes important for such businesses to consider the reliability of their data feed. Web crawling and text mining solutions can help e-commerce businesses to do so. Web crawling and data mining solutions can also assist marketers to fine-tune product portfolios, optimize price points, and customize it down to the individual level to boost profitability. 

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How can analytics in healthcare help companies deal with challenges in the industry?   

Despite its promising growth prospects, companies in the healthcare sector are hard pressed when it comes to finding solutions to several complicated issues such as shooting costs, medicinal and technological advancements, changes in policy and regulations, maintaining efficient operations, and providing the best care. But this is where leveraging analytics in healthcare can help. The use of data analytics solutions in healthcare can help healthcare service providers in minimizing healthcare waste, reducing hospitals readmissions, and providing cost-effective treatments to patients.

How can B2B companies tackle growing business challenges by utilizing market mix modeling?

Today, it has become really difficult for businesses to measure marketing effectiveness. Analyzing the future impact of a change in marketing metrics and optimizing investments to drive profits is a daunting task for B2B companies. Also, the influence of operational factors and several external factors can lead to an increase in the market spend for B2B businesses. This is where market mix modeling can be taken into account to set the correct foundation for their marketing strategies and branding solutions.

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Here is the list of that Quantzig Conducted on the above topics that you can watch for free:

#1: Importance of web crawling and text mining solutions to improve e-commerce business strategy: Customized web crawling and text mining based analytics solutions can help e-commerce businesses to tackle critical challenges created by the recent explosion of data. Web crawling and text mining solutions assist businesses to explore innovative techniques to collect vast volumes of online data and organize it in a structured manner. Furthermore, it helps gain better business insights, improves business planning, and optimizes decision management. Watch out the full webinar here:

#2: How can analytics bring in commercial excellence in the healthcare industry? Healthcare regulations and advancements in medicines have resulted in the restructuring of healthcare provisioning as well as diversification and specialization in life sciences. Organizations in healthcare need to develop commercial strategies that include all stakeholders. Leveraging analytics in healthcare can help organizations to gain real-world insights and maintain a single source of accurate information of customers for segmentation, targeting, and call planning. Check out the full webinar here:

#3: Is market mix modeling a cornerstone for B2B companies?  Marketers across industries are hard pressed to prove the value of their marketing investment decisions based on the performance of their business and identify opportunities for optimizing investment. Market mix modeling helps companies to develop a multi-channel strategy to execute and measure campaigns across digital channels and boost conversions. Watch out the full webinar to know more:

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Evaluating the Importance of Data Analytics and Visualization for Businesses

When it comes to successfully positioning your organization in the competitive market, data analytics and visualization act a key ingredient. Data visualization using appropriate data analysis methods can provide valuable insights into business operations. Such insights can be incorporated by businesses in the decision-making pipeline to make data-driven decisions. Leveraging such data analytics and visualization solutions can help companies to analyze their data irrespective of the size, scale, type, and domain and improve their decision-making capabilities.

Data analytics and visualization have become a necessity for companies to manage risks and comply with the changing regulatory landscape.

Therefore, it is imperative for businesses to realize the importance of data analytics and visualization as visuals are immensely capable of predicting all possible outcomes and make complex decisions to yield favorable outcomes. In this article, our team of experts has highlighted how data analytics and visualization techniques can be utilized to make the most of the statistical data, old experiences/learnings, and fresh ideas to launch new products.

Extracting insights from disparate datasets can be a difficult task for companies. But data analytics and visualization techniques can help. Request a FREE demo today!

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How can data analytics and visualization help businesses?

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web mining

Building an Effective E-Commerce Business Strategy with the Help of Web Mining and Text Analytics | Quantzig

The client is a leading e-commerce retailer based out of Germany. The retailer was looking at expanding its online product offerings by sifting through the massive datasets in their repository to find hidden patterns and uncover meaningful insights.

The Business Challenge

Today, it doesn’t matter if you run a business online or in the real world, all that matters is how efficiently you can analyze the gigantic troves of information to extract granular insights lying at your disposal. This is considered to be the barometer that determines the success ratio of pure-play e-commerce retailers. Owing to the data explosion and the impact of technological advancements in today’s world, businesses own huge volumes of datasets, which act as a valuable source of actionable insights.

Businesses collect trillions of bytes of data on customer transactions, suppliers, internal operations, and market competitors. Such datasets are predicted to grow in the coming years, due to which businesses are focusing on the incorporation of web mining solutions and text analytics to exploit the true potential of data. Penetrating deeply into the textual datasets by leveraging web mining solutions empowers retailers to gain a better understanding of customer’ sentiments around what they offer. As a result, customer experience sprints from the mundane to marvelous and customer loyalty gets a solid push along with revenues.

Request a FREE Demo to understand how Quantzig’s web mining solutions can empower your business to achieve accelerated growth and the desired process efficiencies.

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Top Challenges Faced by the Client

The e-commerce boom has presented both challenges and new opportunities for retailers that are looking to expand globally by improving their online presence and expanding their digital footprint by leveraging web mining.

Problem Statement 1

The e-commerce retailer faced predicaments in collecting and analyzing the huge datasets spread across geographies and business units, owing to the unstructured nature of the datasets.

Problem Statement 2

The client found it challenging to extract insights from unstructured datasets related to business outcomes, sales, and suppliers. Being able to sift through the huge data repositories and structure the datasets in the form of intelligible information was challenging as the data existed in silos.

Problem Statement 3

Organizing the data in a structured manner and linking it with internal data proved to a major challenge that prevented the client from analyzing the user-generated content to optimize business decisions.

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Solution Offered and Value Delivered

We followed a three-pronged approach to gain the desired insights for this web mining engagement.

Phase 1: Web data collection

The first phase of this web mining engagement revolved around collecting and documenting the available information. The gathered information was then analyzed and populated into desired formats to discover prevalent market trends, new opportunities, and drive sales while cutting down costs.

Phase 2: Discovering hidden patterns

The second phase of this web mining engagement focused on pattern discovery. The structured datasets were analyzed to understand the browsing behavior of online customers and to determine the success of their marketing efforts.

Phase 3: Analyzing the data patterns

The last phase of this web mining engagement focused on using the extracted insights to fine-tune product development, pricing strategies, and customer experience. This helped them offer incremental value to the shopper’s online experience, while achieving a remarkable 3X improvement in customer satisfaction rates.

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Working hand-in-hand with the e-commerce retailer we gained a better understanding of their business processes and unique challenges. In line with this understanding, we put together a dedicated team of our web mining and text analytics experts who co-ordinated with their in-house team to completely transform their online business strategy. To drive down costs and compete better the ecommerce retailer had to leverage web mining, improve decision making, and develop new and innovative products to create new revenue streams, explore new markets, and better understand customer needs. Quantzig’s web mining and text analytics solutions offered the desired insights that helped the client to devise a new business strategy aimed at driving revenue from online channels.

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Though e-commerce retailers have come a long way, the absence of in-person interaction is the only area where they still taste defeat at the hands of brick-and-mortar retailing. The only way to inflict a fitting counterblow is to transform the online customer experience through analytics-driven, data-backed decisions.

What are the different types of web mining?

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Web Mining: A Key to Boost Your Business on Web

What is Web Mining?

When data mining techniques are applied to web data to identify useful patterns, it is called web mining. Web mining integrates the information collected from world wide web and information fetched from data mining techniques to quantify the success of a marketing campaign for any business. Web mining is one of the web analytics solutions that help organizations in analyzing the effectiveness of a specific website, understand consumer behaviour and competitors’ strategy efficiently. The information collected through web mining offers actionable insights into the web structure, content, and usage.

Types of Web Mining

Web Mining-Type #1: Web Content Mining

Web content mining aims at collecting multimedia documents such as video, images, and audio that are hyperlinked or embedded to the web pages. Web data mining deals with unstructured or semi-structured data and requires a creative approach to data mining techniques. In the last few years, the activities in the field of web content mining have expanded rapidly due to the exponential growth of data on the web. However, due to the lack of structure of web data and its heterogeneous nature poses several challenges in the web content mining process.

Web mining solutions can help in enhancing your website’s performance significantly. Request a FREE proposal now to know how!

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Web Mining-Type #2: Web Structure Mining

Web structure mining aims at analyzing the link structure of the web and identifying more desirable documents. Web structure mining assist companies in identifying similarities between different websites. It aims at generating a structural summary about the web page and the website. The main aim of web structure mining is to focus on the inner-document structure as well as on the structure of the hyperlinks at the inter-document level. Web structure mining uses topology of hyperlinks to categorize and deliver information on the relationship and similarities between multiple websites.  

Web Mining-Type #3: Web Usage Mining

Web usage mining aims at predicting the behaviour of users while they interact with the world wide web. It helps in discovering the navigation patterns of users. Also, it fetches useful information from the user interactions on the web that further helps in understanding consumer behaviour and buying patterns. Furthermore, such insights can help in providing personalized services, site modification, and overall system improvement.

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Advantages of Web Mining Solutions

Benefit #1: Improves business on the web

Web mining helps in analyzing click-stream data and unveiling e-business opportunities in real-time. It aids in targeting the right customers at the right time by understanding their needs. Also, web mining solutions help in customizing services according to customers demand which further helps in improving marketing strategies. Furthermore, web mining solutions help in improving the effectiveness of a website by offering what users want at the right time.

Web crawling and text mining solutions can help in improving cross-selling opportunities. Want to know how? Read our latest success story here:

Web Crawling and Text Mining Engagement for A Leading Digital Media Industry Player Helps in Increasing Cross-Selling Opportunities

Benefit #2: Helps in analyzing customer behaviour 

By leveraging web mining solutions, companies can understand and analyze several concerns such as the relationship between customer’s present and past behaviour and their loyalty towards the website. Analyzing customer-centric web behaviour can be helpful for companies in improving the website segmentation based on customer behaviour and preferences. Also, it can help in offering personalized services to customers. Furthermore, web mining guides in predicting customer’s future behaviour which is very important for a website’s future content design and planning.

Are you finding it difficult to analyze the effectiveness of company communications and customer interactions on the world wide web? Web analytics solutions may hold the key to success. Get in touch with our experts.

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Benefit #3: Assists in analyzing proxy servers

Web mining has the ability to help in identifying user behaviour on a particular website. Businesses can leverage web mining solutions to perform investigations on their own website and can yield actionable insights into the needs of users and accommodate them. But apart from this, what is equally important is analyzing a user’s behaviour on the proxy servers. Web mining can efficiently do this and can help in generating an overview of customer behaviour on the internet.

Quantzig’s Advantage

Businesses that run online receive heavy traffic on a daily basis and the immense volume of continuous data makes it extremely challenging for businesses to track their visitors and analyze website statistics, potential consumers, and prospective buyers. Quantzig’s data analytics services and web mining solutions help companies in managing large volumes of web data and set up customized dashboards to derive real-time insights to facilitate data-driven decisions. Quantzig’s data analytics solutions provide companies with the required technologies, capabilities, and frameworks to handle the sheer volume and variety of data while ensuring privacy and security of information. Quantzig with leading data-governance practices deliver best-in-class frameworks for data visualization and multi-dimensional data aggregation that further helps in better insight generation.

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Data Mining and Analysis is Crucial to Your Business. Learn How!

What is data mining?

Data mining is an analytic process which is designed to inspect large amounts of data. It helps in searching systematic relationships and consistent patterns between variables. The final goal of big data mining is the prediction. In fact, predictive data mining is one of the most common types of data mining and has the most direct business applications. The process of big data mining basically comprises three stages:

  • The initial exploration
  • Model building or pattern identification with verification and validation
  • Deployment

Are you facing difficulty in multi-dimensional data aggregation and visualization? Get in touch with our experts to check out how our data mining and analysis solutions can help.

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Benefits of data mining and analysis for businesses 

Fast and feasible decisions

Searching information from the huge amount of data is time-consuming. But data mining and analysis helps to get the information much faster eliminating the possibility of making mistakes and incorrect decision making. It also helps in comparing the fetched information with various factors, so the decisions become more authentic and reliable.

Build powerful strategies

Data mining and provides reliable information which further helps in making different strategies. In other words, by analyzing information in different dimensions it becomes easier to make effective strategies and implement them. This can help companies to effectively expand their business boundaries and make authentic decisions.

Actionable Insights

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Gain a competitive advantage

Data mining and analysis help in comparing own data with that of the competitors in different aspects. Competitive analysis is an important aspect of data mining that helps businesses gain a competitive advantage. 

Our latest success story showcases an excellent example of how data mining and analysis helped a leading company in building a successful predictive model to predict future outcomes. Read the complete story here.

What are the different data mining and analysis techniques?

Classification analysis

Classification analysis is a very important data mining and analysis technique which is used to retrieve relevant and important information about data, and metadata. It categorizes datasets in different classes. In classification analysis algorithms are applied to decide how new data should be classified. An excellent example of classification analysis would be our outlook email. In outlook, certain algorithms are used to characterize an email as legitimate or spam.

Association rule learning

Association rule learning method in the data mining and analysis process helps in identifying interesting relations between different variables in large databases. This technique helps in uncovering hidden patterns in the data that can be further used to determine variables within the data and the concurrence of frequently appearing variables in the dataset. This is one of the data mining techniques that is used for examining and forecasting customer behavior and is highly recommended in a detailed retail industry analysis.

Are you wondering how to segment the data and evaluate the probability of future events? We’d love to help. Request a free proposal now to know more about our portfolio of services.

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Anomaly or outlier detection

This technique in the data mining and analysis process refers to the observation for data items in a dataset that do not match an expected behavior or an expected pattern. Anomalies are also known as novelties, outliers, noise, exceptions, and deviations. Often, they provide actionable and critical information. This is one of the data mining methods that can be used in a variety of domains, such as system health monitoring, intrusion detection, fault detection, detecting eco-system disturbances and event detection in sensor networks.

Clustering analysis

Cluster analysis in data mining and analysis process is actually a collection of data objects those objects are similar within the same cluster. That means the objects are similar to one another within the same group and are unrelated or dissimilar to the objects in other clusters. Clustering analysis is the method of data mining and analysis that help in discovering clusters and groups in the data in such a way that the degree of association between two objects is highest if they belong to the same group. The result of this analysis helps in creating customer profiles.

Regression analysis

Regression analysis is the data mining and analysis technique that helps to identify and analyze the relationship among different variables. It also aids in understanding the characteristic value of the dependent variable changes. This data mining method is generally used for prediction and forecasting.

We understand the challenges faced by companies when it comes to data visualization, data mining and analysis, reducing customer churn, and increasing customer loyalty. Our data mining and analysis techniques provide best-in-class frameworks to facilitate a better understanding of new business opportunities, competitive situations, and complex challenges. Request a FREE demo for more insights!

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3 Ways Predictive Analytics Solutions Can Help Businesses Derive Successful Outcomes

With the shifting key values of every industry from customer-focused to customer-centric, there is an immense growth in data and information. This has changed customer dynamics and as a result, every business is facing numerous challenges such as market uncertainties, driving efficiency, and effectiveness in their marketing productivity, immense competition, customer demands, fraud detection, and risk management.

Many businesses across all industries, in order to cater customer demands, are trying to utilize the data that their customers leave behind while interacting with the company. Harnessing this pool of data can offer several benefits to organizations. However, many companies still have not realized the importance of data mining and have not gone beyond gathering and storing their data. Although it is difficult to deal with an unstructured set of data by leveraging predictive analytics solutions businesses can fetch optimum results from such data. 

At Quantzig, we understand the impact that predictive analytics solutions can have on your business. And to help companies derive actionable insights from large and complex data sets, our team of experts has highlighted three important ways in which predictive analytics solutions can help in managing large volumes of data and setting up analytical frameworks to derive real-time insights that facilitate more informed and wise decisions.

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Data Mining Engagement for a Traditional Chinese Medicine Company Helps Identify Anomalies in Datasets

What the Client Wanted

Leverage data mining techniques to predict future outcomes by identifying incongruities, data correlations, and patterns within huge sets of data in the traditional Chinese medicine segment.

The Outcome

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A leading traditional Chinese medicine (TCM) manufacturing firm, wanted to help their patients achieve and maintain health by developing precise remedies to tackle their health issues. The staggering amount of unstructured data related to lifestyle disorders and other chronic diseases posed major challenges that hampered their R&D process.

To unearth insights using the right data mining techniques, the traditional Chinese medicine manufacturing firm approached Quantzig to leverage its expertise and devise appropriate solutions to help their customers.

Traditional Chinese Medicine Market Overview

Traditional Chinese medicine is an ancient field of medicine based on the principles of Daoism, which states that everything in the universe is interrelated. Thousands of years of observation and practice resulted in the development of traditional Chinese medicine, which follows a unique strategy to understand the functions of internal organs and physiological processes. Traditional Chinese medicine is a very complex and complicated field of the medical system that aims at promoting health through diet and exercise. It also focuses on treating illnesses using herbs, acupuncture, and Qigong.

Challenges facing the Traditional Chinese Medicine Market

  • Authentication issues: Traditional Chinese Medicine (TCM) is winning supporters across the world, which makes it necessary for players in this filed to validate the safety and quality of their medicinal products. A proper authentication process along with the use of data mining techniques can help ensure that the products are safe from contaminants. However, the recently formed Chinese Medicine Association of Suppliers (CMAS) in the UK plays a key role in authentication.Request Solution Demo
  • Difficulty in creating an appropriate placebo: A major challenge for traditional Chinese medicine practitioners revolves around the difficulty in developing appropriate placebos for multiple-herb based decoctions. This is because it is technically challenging to produce an indistinguishable placebo in the form of decoction for a multiple-herb formulation.

About the Client

One of the largest manufacturers of premium traditional Chinese medicine. The client is a global leader in scientific research and production of Chinese Granule Herbs, with their sales network spread across 30 countries globally. 

Client’s Challenge

Owing to the challenges they faced in drug discovery, the client wanted to implement efficient data mining techniques to discover data patterns that help improve the quality of drug discovery and drug delivery methods. The traditional Chinese medicine firm wanted to sift through the repetitive noise in their data to gain an understanding and make use of relevant factors and assess likely outcomes.

Data Mining Engagement Summary

Traditional Chinese Medicine

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Business Impact

Implementation of data mining techniques helped the traditional Chinese medicine manufacturer to build a successful predictive model to predict future outcomes of their medicinal formulations. It also helped them find answers to essential analytical questions such as – Which formulations are likely to be retained? How to ensure effectiveness and efficiency with optimal resource allocation? What factors after the drug discovery process?

Insights on data mining techniques

Organizations today have access to huge volumes of information that is gathered from all kinds of sources across the industry. However, the unanswered question here is – How can you use this data to generate insights that are required to drive business value? This is where ‘Data Mining Techniques’ contribute in a big way. Data mining techniques play a key role in helping organizations predict future trends and act accordingly to carve out a niche for themselves in the market.

Did this case study help you gain detailed insights on how data mining techniques can help drive your business’ profit margins and keep abreast of the new industry trends? To know more

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Top 5 Data Mining Techniques to Facilitate Big Data Analytics

For long, data scientists have been trying to find patterns and trends in data. To do so, they need to mine through a substantial amount of data. The more the data, the more accurate the insights and information. The recent explosion of big data has posed new problems to data scientists who are struggling to process such data sets. The data sets go way beyond the current storage capacity and computing power. So, it is essential to make the data mining techniques more efficient to gain relevant insights within seconds. These data mining techniques extend beyond just simple statistical analysis by analyzing millions and billions of data point to present in-depth insights. So what are the topFree demo data mining techniques used by companies to make sense out of their data?

Association rule learning

More like how humans develop knowledge, association rule implies the same principle of learning. A kid learns that fire is hot, and anything with flame will be hot as well. Similarly, interesting relationships can be uncovered between different variables in large datasets by using association rule learning. It is also the most straightforward data mining technique. The data scientist makes a simple correlation between two or more items to identify the same type of patterns. For instance, in a retail setting, a retailer may discover that a certain customer always buys eggs when they buy milk, and therefore they may suggest eggs the next time they put milk in their cart. Additionally, the technique can be used to determine product clustering, catalog design, shopping basket data analysis, and store layout.

Classification analysis

One of the easiest way to teach a machine is by classifying data into close groups. The data scientists assign the given data into a pre-defined category, and the machine can learn to accurately predict in the future what data belongs to which category. One of the most efficient form of this technique can be seen in Gmail, where the sorting algorithm can automatically identify if a mail is spam, promotional, update, or personal. This data mining techniques can also be used across other industries to classify customers based on age and social group.

Top Data Mining TechniquesClustering analysis

Clustering is more or less similar to classification analysis, with the only difference being that there is no pre-determined category. Cluster here refers to a collection of data objects that are close together when plotted on a graph. It indicates that two objects that are closer to each other exhibit more or less the same properties than those that are far away. It helps data scientists to identify customer profiles from scratch. However, it can be challenging to pinpoint a specific cluster, since one data set can be the same distance apart from two different clusters.

Anomaly or outlier identification

In statistical terms, data is dispersed consistently, with the majority of the data point clustering around the average. However, there can be few outliers which can be on the extreme end of the spectrum. Sometimes it can occur naturally, but usually, it presents the analyst with a concerning observation. Such data mining technique is used in fraud detection, intrusion detection, and system health monitoring. For instance, a customer who spends on average $50 per transaction, suddenly spends $10,000 in a single transaction can signal that a fraud has occurred.

Decision trees

Decision trees are closely related to other data mining techniques. It can be used as a part of selection criteria or to support the use of specific data within the overall structure. Each decision tree starts out with a simple question and based on the answers, further questions are asked which helps to be sorted to a particular category. With time, accumulating many answers can enable data scientists to make a prediction based on each type of answer. For instance, such data mining techniques can start out with a simple question like whether a customer is a male or a female; then based on the answer, further questions could be asked. Based on the answers obtained, accurate predictions can be made.

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