FREQUENTLY ASKED QUESTIONS
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BASEQ is derived from the words “Base Query” and is addressed to enable all users with basic knowledge and technical knowledge/skills to quickly interpret data sets with the support of Artificial Intelligence and provide all the outputs and visualisations they need.
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A predictive question is a question you define when building a BASEQ model expressing the predicted output you expect to extract from the data set.
This question will vary depending on the business problem you want to solve.
For example, “What is the probability that customer segment [ABC] will experience [churn] (a specific activity) in [Q3 2025] (a specific period)?”.
The predictive question guides the model in learning from your data and making predictions.
BASEQ AI has started to be developed to improve the processes of individual and corporate customers, with the aim of improving customer processes in different scenarios.
When it was developed, it focused on data interpretation and visualization, and R&D processes are ongoing for other scenarios.
In general, since machine learning is all about extracting patterns from uploaded datasets, the breadth and quality of the dataset will affect the output. There is technically no limit to the amount of data you can link to your account, but it is limited by your membership features. We recommend that you have at least 1000 rows and 5 columns for your dataset.
BASEQ’s Predictive GenAI allows you to build a (predictive) model to generate predictions for use cases by almost all business units, as long as you have the relevant data. Predictive processes guide users to define the right model and training data set to fit their business needs. In more technical terms, BASEQ joins your team as a powerful player by building different classification models and regression models that can address a wide range of business questions.
Dataset prerequisites are different, depending on whether AutoML (classification, regression) or Time Series.
In BASEQ AI, it is really easy to load data into your account. In a few clicks, the data set loading is complete. The prerequisites are shown on the screen when the data set shape is selected, and guidance is provided.
Basic requirements:
For example, “Google, Apple, Facebook” are 3 separate values but are treated as a single value.
Personally Identifiable Information (PII) columns (e.g. phone, email, address, etc.) are not required.
You can create a practical data set by categorising long sentences with separate values.
Intermediate requirements
The requirements in the following items are the actions that should be taken for missing data to make your model more efficient.
- Missing Data
Advanced requirements
Technical knowledge is recommended for advanced requirements.
- Data Enrichment
Create new columns:
The quality of a dataset is often enhanced by deriving new columns from existing columns or by correlating different datasets.
For example, deriving age from date of birth, duration from start and end dates of customer subscription or employment period, etc.
Once new columns are created, unnecessary columns should not be considered for training the data as they are unnecessary information
Additional columns should be created from comma separated values. Columns with values in comma separated format are treated as one long piece of text instead of different values
For example, “Google, Apple, Facebook” are 3 separate values but treated as a single value
Separate columns can be created for each value and filled with 0/1 depending on their existence for a particular row
BASEQ AI is positioned as a no-code platform for business units and a low-code platform for those with technical know-how, a tool that makes it incredibly easy to do faster, better and less costly.
When building and using AI for business use cases, BASEQ positions AI modelling with no-code use. No-code AI effectively uses machine learning (ML) and artificial intelligence (AI) without writing a single line of code. On the No-Code AI platform, users are only expected to upload their data and click a few buttons to get the results.
Everyone from entry-level business analysts to expert data scientists can easily harness the power of machine learning and AI with AI’s No-code AI and intervene at the code level.
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BASEQ’s automated processes start with data quality, identifying and correcting common issues such as missing data, outliers, or duplicates. The platform also looks for relationships in your data, identifying new features to build, such as clusters, trends, and other key data points.
From hundreds or even thousands of potential features, BASEQ identifies the ones that matter for accurate predictions. These automated steps eliminate much of the tedious and time-consuming work required to create an AI-ready data set, leaving you to add the finishing touches. All this preparation improves the performance of end-to-end models and helps you get the most value from your data.
Don’t be afraid; this entire process is done automatically!
BASEQ AI allows the inclusion of different data sources for forecasting datasets. The most well-known and used of these is CSV. You can directly upload a CSV file up to 25 MB to the platform.
Another popular data storage format is Google Spreadsheets. You can upload a Google workbook with multiple sheets to BASEQ AI.
After selection, all file controls will be shown to you, and you will be guided.
If you decide to delete your Account, you may do so by emailing [email protected]. If you terminate your Account, any association between your Account and the information we store will no longer be accessible through your Account. However, due to the nature of sharing on the Services, all publicly available activity in your Account before deletion will continue to be stored on our servers and accessible to everyone.
BASEQ’s easy-to-use dashboards provide guidance on how to read and act on the model’s predictions, as well as easy-to-understand comparisons with related models.
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With your existing data, you can interpret the past and predict the near future. Getting results with converging ratios about the future makes it easier for you to review your workflows or strategies and work to get closer to your goals.
For example, You can predict your Q4 sales figures by enriching your 2023-2024 sales figures and different data types, and you can determine which channels will increase.
BASEQ AI enables users to make text-based queries with Generative AI capabilities by uploading data sets or taking them with application integrations. The data is then interpreted and visualised by the models according to the expected needs of the queries.
All these steps work only text-based without knowing query languages.
You’ll be able to trust it with ease. Automated – new – approaches to predictive modelling are widely accepted and used by the most sophisticated data scientists who rely on AutoML tools daily to build, evaluate and select suitable predictive algorithms quickly. We have tested our automated methods for data cleaning, feature engineering and model building in our studies and with our customers.
First, BASEQ automatically trains hundreds of predictive models using various algorithms from the literature that are known to perform well on the types of data businesses typically have. Then, the models are all automatically tested and compared.
BASEQ also has an accessible capability that allows analysts to control the business metrics used to select the model that will answer your business needs. The best-performing model is selected for you to review and refine in the BASEQ interface and deploy when you are ready.
