Visualize your enterprise data to uncover hidden insights.
A picture is worth a thousand words. Imagine the amount of data that is generated by any modern-day enterprise system, stored in database tables. There are immense possibilities that can be unleashed if all that data can be visualized. It will lead to thousands of new ways to slice and dice information and uncover things that you never knew existed. With the volume and variety of social, mobile, and device data, along with new technologies and tools, data science today plays a broader role than ever before. The business considers data science to be a technology-enabled strategy. In order for data science to be effective, its full lifecycle not only must support traditional analytics but must also work in concert with modern applications.
At Liberin, we work with different ways of visualizing enterprise data. Uncover patterns and build predictions using data, algorithms and machine learning, and AI techniques Leveraging the power of predictive analytics to derive real-time insights and reduce customer churn, we help our clients solve the toughest data challenges, predict demand for products and services to improve customer satisfaction and guide business strategies based on knowledge and foresight. Benefit from our Data Science service, from prediction to optimization. We help you in developing customized statistical models and algorithms, leverage advanced customer, operational, and IoT analytics, and generate and deploy intelligent insights in near real-time. Reduce revenue leakages and boost bottom line productivity using advanced data science solutions. Our team of experts enables you to find innovative ways to strategize and optimize operations while exploring new market opportunities businesses can benefit from data science.
Data Acquisition & Preparation
Liberin gathers and scrapes data from multiple sources such as web servers, logs, databases, APIs, and online repositories. Then comes data cleaning and transformation.
Exploratory Data Analysis
This step involves defining and refining the selection of feature variables that will be used in the model development.
Applying machine learning techniques like KNN, Decision Tree, and Naïve Bayes to the data helps us in identifying the model that best fits your business requirement.