Qualetics is our on-demand Data Intelligence as a Service (DiaaS) platform that can help LMS platforms quickly embed advanced Data Analysis and Artificial Intelligence methodologies into their product in a much simpler manner than traditional methods. Solutions in the market put the onus of achieving success with implementing AI & ML solutions on the Software Developers without realizing the cost overhead it introduces. Our platform and advanced APIs allow for easy integration of insights and data-based Intelligence into your Products & Systems to better serve you and your customer needs without breaking the bank.

The goal of this study is to analyze the queries raised by customers on a particular social media platform by analyzing their interactions with the customer support and provide incisive insights to perform sentiment analysis. We performed exploratory data analysis to extract insights from the data. With Deep Learning tools like NLTK, sentiment analysis was performed to understand the positive, negative, and neutral sentiments of the customers of a brand. Machine Learning was used to identify the frequency of similar text appearances. Deep learning algorithms were used to understand the customer queries and the average time taken by the respective company's social customer support team in addressing the queries.

Machine Learning and Deep Learning models combined with easy to build open source techniques can help improve the diagnosis of the life-threatening Malaria disease. The objective of this usecase is to develop a model that can predict the probability of a human cell to be infected with Malaria parasite from an exploratory data analysis performed on a vast dataset containing images of infected and uninfected human cells. We leveraged deep learning models like CNN because of its effectiveness in providing solutions related to Computer Vision tasks. Using a CNN model we have been successful to predict both the categories and validate our approach to the future unseen data. To know how Qualetics gives an effective solution, download the full usecase.

The objective of this usecase is to predict the major risk factors that may result in the development of heart diseases in the future by using machine learning algorithms. Heart diseases are one of the main causes of death worldwide. Many factors like age, family history, high BP, high cholesterol levels, unhealthy lifestyles, lack of physical activity, etc can be attributed to the increasing cases of cardiovascular diseases. While some of these factors can be controlled, some others like age, hereditary, etc cannot be controlled by individuals. These factors are not constant among the individuals and keep varying.

The purpose of this usecase is to formulate inferences and generate insights based on certain indicators like the bed occupancy status, ward-wise revenues, delivery procedures (normal or caesarian), revenues generated by doctors, etc. The analysis performed gave deep insights (yearly, monthly, weekly) into the number of outpatients getting converted to inpatients, ward-wise inputs on bed occupancy and revenues, doctors' performance and the revenues generated by them, type of delivery (normal or caesarian), etc. This usecase also tries to predict the weekly trends in the patients which will help for better monitoring and allotment of resources. To know how Qualetics gives an effective solution, download the full usecase.