About VOGO

Vogo.family is an innovative concept in the HoReCa, Personal-Care and Lifestyle industries. Vogo uses technology to ensure quality and "well-being" for people, but emphasizes communication, discussions with real people, communities and friends who do everything they can to support and help you, and recommending only products verified by our community members and only from providers / verified and certified manufacturers VOGO. If you're tired of waiting for minutes on the phone or talking to an IVR robot that just delays you or puts you off, if you've had unpleasant experiences ordering products that didn't meet your expectations and that left you disappointed - in addition to spending money unnecessarily = waste - you understand what we mean.

VOGO products and services add value to culinary experiences, hospitality and lifestyle in general.

The internet is full of mixed information, subjectively presented, real reviews and bought reviews, direct advertisements or disguised advertisements. It is complicated for each of us to search, filter and choose the best services, quickly and with knowledge of the facts. We are assaulted by marketing and information that puts us in difficulty.

VOGO Family is probably, the largest integrator of verified premium services and products. According to the VOGO statute and code of conduct, access to members — both buyers and suppliers or experts in certain fields — is allowed exclusively based on recommendations, invitations or prior verification sent to the email address: romania.partners@vogo.family

If you have had unpleasant experiences in the past that have bothered you, then you can join the VOGO community. VOGO family uses technology to identify, in the multitude of available information, those services and products that offer good quality at a fair price. Attention: VOGO does not filter or select cheap services and products! We select and recommend only quality products, services and suppliers - validated and verified by us. After automatic selection through Data Mining and AI, all products and services are checked manually / directly by our advisors. The platform only contains products and services tested by counselors, certified and validated by them according to VOGO quality criteria.

Have you ever needed something and asked a friend: "Do you know a good mechanic?" Or: "Do you know, when I go to Cluj, where I can eat something good?" Or did you need a medicine from the pharmacy at night and had to to call a friend and ask him/her: "Can you please go over there and get me some Nurofen?" Or you had to get something but couldn't go out because you couldn't leave Bebe alone in the house and had to call a friend: "Please, can you help me...?"

Urban and technological development have the adverse effect that trusted friends and acquaintances/neighbors are becoming increasingly rare and free time is getting smaller and smaller. Information is getting more and more mixed.

VOGO family comes with a solution to these challenges, offering a real friend (not virtual, not robot) in the person of a consultant assigned to you, whom you can search for through the platform in the field of interest and who will answer you by phone, WhatsApp, email, etc.

How it works? See in the diagram below how we combine advanced technology with personal actions to get the best recommendations for our community members.

Our consultants who will help you have access to:
- verified and cataloged information, which I can immediately access in the "knowledge library"
- "lessons learned" - access to multiple information so that they will prevent you from making wrong choices; they will give you all the necessary information. Of course, the decision is yours.
- verified, validated product and service providers, available in any location and at any time of the day or night.

Vino in famiglia VOGO! Enjoy the VOGO experience. Reach out to your friends and share your experiences with us, to help each other! Together we are better.

In the first stage, VOGO collects recommendations and reviews from the main data sources (Google, Facebook, Tripadvisor, etc.) along with public data sets from official bodies in the fields of Horeca, Tourism, Auto, Embassies and Consulates, Authorities, Insurers, Financial, Judicial - Legal, etc.

In stage 2, the process of sorting through filtered data sets takes place to identify patterns and relationships that can help solve utility requirements through data analysis. Data mining techniques and tools help the solution predict future trends and make decisions based on solid data - decision support system.

Data mining is a key part of data analysis and one of the core disciplines in data science, which uses advanced analysis techniques to find useful information in data sets. At a more granular level, data mining is a step in the process of knowledge discovery in databases (KDD), a data science methodology for collecting, processing, and analyzing data. Data mining and KDD are sometimes referred to interchangeably, but are more commonly seen as distinct.

The process of extracting recommendations relies on the efficient implementation of data collection, storage, and processing. Data mining can be used to describe a target data set, predict outcomes, detect fraud or security issues, learn more about a user base, or detect bottlenecks and dependencies. Also, in accordance with "data mining" techniques - the operation is performed by components that operate both automatically and semi-automatically.

Although the number of stages may differ depending on how granular an organization wants each step to be, the data mining process can generally be divided into the following four main stages - stages adhered to by the VOGO system architecture:

1. "Data gathering". Identify and gather relevant data for an analytics application. Data can be located in various source systems, in a data warehouse, or in a data lake, an increasingly common repository in big data environments that contains a mix of structured and unstructured data. External data sources can also be used. Wherever the data comes from, a data scientist often moves it into a data lake for the remaining steps in the process.

2. "Data preparation". Data preparation. This stage includes a set of steps to prepare the data for extraction. Data preparation begins with data exploration, profiling, and preprocessing, followed by data cleaning work to fix errors and other data quality issues, such as duplicate or missing values. Data transformation is also done to make the data sets consistent, unless a data scientist wants to analyze raw, unfiltered data for a specific application.

3. "Data mining". Once the data is prepared, a data scientist chooses the appropriate data mining technique and then implements one or more algorithms to perform the mining. These techniques, for example, might analyze data relationships and detect patterns, associations, and correlations. In machine learning applications, algorithms typically need to be trained on sample data sets to search for the information sought before being run on the full data set.

4. "Data analysis and interpretation". Data analysis and interpretation. The results of data mining are used to create analytical models that can help drive decision-making and other business actions. The data scientist or other member of a data science team must also communicate the findings to business executives and users, often through data visualization and the use of data storytelling techniques.

Read more are here

Types of data mining techniques
Different techniques can be used to extract data for different data science applications. Pattern recognition is a common use case for data mining, as is anomaly detection, which helps identify outliers in data sets. Popular data mining techniques include the following types:

Association rule mining.  In data mining, association rules are if-then statements that identify relationships between data elements. Support and confidence criteria are used to evaluate relationships. Support measures how often associated elements occur in a data set, while confidence reflects how often an if-then statement is correct.

ClassificationThis approach assigns elements in datasets to different categories defined as part of the data mining process. Decision trees, Naive Bayes classifiers, k-nearest neighbors (KNN), and logistic regression are examples of classification methods.

ClusteringIn this case, data elements that share certain characteristics are grouped into clusters as part of data mining applications. Examples include k-means clustering, hierarchical clustering, and Gaussian models.

regress. This method finds relationships in data sets by calculating predicted data values ​​based on a set of variables. Linear regression and multivariate regression are examples. Decision trees and other classification methods can also be used to do regressions.

Sequence and path analysisData can also be mined to look for patterns in which a particular set of events or values ​​lead to subsequent events.

Neural networks. A neural network is a set of algorithms that simulate the activity of the human brain, where data is processed using nodes. Neural networks are particularly useful in complex pattern recognition applications involving deep learning, a more advanced branch of machine learning.

Decision trees. This process classifies or predicts potential outcomes using either classification or regression methods. Tree-like structures are used to represent potential decision outcomes.

Neural networks / KNNThis data mining method classifies data based on its proximity to other data points. Assuming that nearby data points are more similar to each other than other data points, KNN is used to predict the characteristics of the group.